NVIDIA-Merlin
Rename BatchedDataset to Loader
Goals :soccer:
To prepare for migrating to our new dataloaders package, this PR renames BatchedDataset to Loader since that will be the new name.
This PR also exposes the Loader in our public AP in order to make data-augmentations easier.
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GitHub pull request #709 of commit 5183a24aeae9a6a4dcafb09441f0b94fca78fbe0, no merge conflicts.
Running as SYSTEM
Setting status of 5183a24aeae9a6a4dcafb09441f0b94fca78fbe0 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1120/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
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Fetching changes from the remote Git repository
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Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
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Checking out Revision 5183a24aeae9a6a4dcafb09441f0b94fca78fbe0 (detached)
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Commit message: "Rename BatchedDataset to Loader"
> git rev-list --no-walk eb70c8f7dd07c1bd7514d2a3033655328cd6593c # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins16047507343559585215.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 678 items
tests/unit/config/test_schema.py .... [ 0%]
tests/unit/datasets/test_advertising.py .s [ 0%]
tests/unit/datasets/test_ecommerce.py ..sss [ 1%]
tests/unit/datasets/test_entertainment.py ....sss. [ 2%]
tests/unit/datasets/test_social.py . [ 2%]
tests/unit/datasets/test_synthetic.py ..F... [ 3%]
tests/unit/implicit/test_implicit.py . [ 3%]
tests/unit/lightfm/test_lightfm.py . [ 4%]
tests/unit/tf/test_core.py FFFF.. [ 5%]
tests/unit/tf/test_dataset.py ...............F [ 7%]
tests/unit/tf/test_public_api.py . [ 7%]
tests/unit/tf/blocks/test_cross.py ......FFFF. [ 9%]
tests/unit/tf/blocks/test_dlrm.py FFF.F....F [ 10%]
tests/unit/tf/blocks/test_interactions.py . [ 10%]
tests/unit/tf/blocks/test_mlp.py FFFFFFFFFFFFFFFFFFFFFFFF......... [ 15%]
tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]
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tests/unit/tf/blocks/retrieval/test_base.py . [ 23%]
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py F. [ 24%]
tests/unit/tf/blocks/retrieval/test_two_tower.py F..FFFF.... [ 25%]
tests/unit/tf/blocks/sampling/test_cross_batch.py . [ 25%]
tests/unit/tf/blocks/sampling/test_in_batch.py . [ 25%]
tests/unit/tf/core/test_aggregation.py FF.F...F. [ 27%]
tests/unit/tf/core/test_base.py F. [ 27%]
tests/unit/tf/core/test_combinators.py sFFFFFFF............ [ 30%]
tests/unit/tf/core/test_index.py F.F [ 30%]
tests/unit/tf/core/test_prediction.py .F [ 31%]
tests/unit/tf/core/test_tabular.py .... [ 31%]
tests/unit/tf/core/test_transformations.py s............................ [ 36%]
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tests/unit/tf/data_augmentation/test_misc.py . [ 38%]
tests/unit/tf/data_augmentation/test_negative_sampling.py .FFFFFFFF. [ 40%]
tests/unit/tf/data_augmentation/test_noise.py ..... [ 41%]
tests/unit/tf/examples/test_01_getting_started.py . [ 41%]
tests/unit/tf/examples/test_02_dataschema.py . [ 41%]
tests/unit/tf/examples/test_03_exploring_different_models.py . [ 41%]
tests/unit/tf/examples/test_04_export_ranking_models.py . [ 41%]
tests/unit/tf/examples/test_05_export_retrieval_model.py . [ 41%]
tests/unit/tf/examples/test_06_advanced_own_architecture.py F [ 42%]
tests/unit/tf/examples/test_07_train_traditional_models.py . [ 42%]
tests/unit/tf/examples/test_usecase_ecommerce_session_based.py F [ 42%]
tests/unit/tf/examples/test_usecase_pretrained_embeddings.py . [ 42%]
tests/unit/tf/inputs/test_continuous.py .F... [ 43%]
tests/unit/tf/inputs/test_embedding.py ......................F...FFFFFFF [ 48%]
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tests/unit/tf/inputs/test_tabular.py F.F........FFFFFFF [ 51%]
tests/unit/tf/layers/test_queue.py .............. [ 53%]
tests/unit/tf/losses/test_losses.py ....................... [ 56%]
tests/unit/tf/metrics/test_metrics_popularity.py ..... [ 57%]
tests/unit/tf/metrics/test_metrics_topk.py ....................... [ 60%]
tests/unit/tf/models/test_base.py s................ [ 63%]
tests/unit/tf/models/test_benchmark.py .. [ 63%]
tests/unit/tf/models/test_ranking.py ..........................FF.. [ 67%]
tests/unit/tf/models/test_retrieval.py ..F...F......................... [ 72%]
tests/unit/tf/prediction_tasks/test_classification.py .. [ 72%]
tests/unit/tf/prediction_tasks/test_multi_task.py FFFFFFFFFFFFFFFF [ 75%]
tests/unit/tf/prediction_tasks/test_next_item.py ..... [ 75%]
tests/unit/tf/prediction_tasks/test_regression.py .. [ 76%]
tests/unit/tf/prediction_tasks/test_retrieval.py . [ 76%]
tests/unit/tf/prediction_tasks/test_sampling.py ...... [ 77%]
tests/unit/tf/predictions/test_base.py ..F.. [ 78%]
tests/unit/tf/predictions/test_classification.py ....... [ 79%]
tests/unit/tf/predictions/test_dot_product.py ........ [ 80%]
tests/unit/tf/predictions/test_regression.py .. [ 80%]
tests/unit/tf/predictions/test_sampling.py .... [ 81%]
tests/unit/tf/utils/test_batch.py .... [ 81%]
tests/unit/tf/utils/test_tf_utils.py ..... [ 82%]
tests/unit/torch/test_dataset.py ......... [ 83%]
tests/unit/torch/test_public_api.py . [ 83%]
tests/unit/torch/block/test_base.py .... [ 84%]
tests/unit/torch/block/test_mlp.py . [ 84%]
tests/unit/torch/features/test_continuous.py .. [ 84%]
tests/unit/torch/features/test_embedding.py .............. [ 87%]
tests/unit/torch/features/test_tabular.py .... [ 87%]
tests/unit/torch/model/test_head.py ............ [ 89%]
tests/unit/torch/model/test_model.py .. [ 89%]
tests/unit/torch/tabular/test_aggregation.py ........ [ 90%]
tests/unit/torch/tabular/test_tabular.py ... [ 91%]
tests/unit/torch/tabular/test_transformations.py ....... [ 92%]
tests/unit/utils/test_schema_utils.py ................................ [ 97%]
tests/unit/xgb/test_xgboost.py .................... [100%]
=================================== FAILURES ===================================
________________________ test_tf_tensors_generation_cpu ________________________
def test_tf_tensors_generation_cpu():
tf = pytest.importorskip("tensorflow")
data = generate_data("testing", num_rows=100, min_session_length=5, max_session_length=50)
schema = data.schema
from merlin.models.tf import sample_batch
E ImportError: cannot import name 'sample_batch' from 'merlin.models.tf' (/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/init.py)
tests/unit/datasets/test_synthetic.py:47: ImportError
_________ test_serialization_continuous_features[True-None-None-None] __________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b423a820>, pre = None
post = None, aggregation = None, include_schema = True
@pytest.mark.parametrize("pre", [None])
@pytest.mark.parametrize("post", [None])
@pytest.mark.parametrize("aggregation", [None, "concat"])
@pytest.mark.parametrize("include_schema", [True, False])
def test_serialization_continuous_features(
testing_data: Dataset, pre, post, aggregation, include_schema
):
schema = None
if include_schema:
schema = testing_data.schema
inputs = ml.TabularBlock(pre=pre, post=post, aggregation=aggregation, schema=schema)
copy_layer = testing_utils.assert_serialization(inputs)
keep_cols = ["user_id", "item_id", "event_hour_sin", "event_hour_cos"]
tf_tabular_data = ml.sample_batch(testing_data, batch_size=100, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/test_core.py:25: AttributeError
________ test_serialization_continuous_features[True-concat-None-None] _________
testing_data = <merlin.io.dataset.Dataset object at 0x7f8997dd5af0>, pre = None
post = None, aggregation = 'concat', include_schema = True
@pytest.mark.parametrize("pre", [None])
@pytest.mark.parametrize("post", [None])
@pytest.mark.parametrize("aggregation", [None, "concat"])
@pytest.mark.parametrize("include_schema", [True, False])
def test_serialization_continuous_features(
testing_data: Dataset, pre, post, aggregation, include_schema
):
schema = None
if include_schema:
schema = testing_data.schema
inputs = ml.TabularBlock(pre=pre, post=post, aggregation=aggregation, schema=schema)
copy_layer = testing_utils.assert_serialization(inputs)
keep_cols = ["user_id", "item_id", "event_hour_sin", "event_hour_cos"]
tf_tabular_data = ml.sample_batch(testing_data, batch_size=100, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/test_core.py:25: AttributeError
_________ test_serialization_continuous_features[False-None-None-None] _________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4d849a0>, pre = None
post = None, aggregation = None, include_schema = False
@pytest.mark.parametrize("pre", [None])
@pytest.mark.parametrize("post", [None])
@pytest.mark.parametrize("aggregation", [None, "concat"])
@pytest.mark.parametrize("include_schema", [True, False])
def test_serialization_continuous_features(
testing_data: Dataset, pre, post, aggregation, include_schema
):
schema = None
if include_schema:
schema = testing_data.schema
inputs = ml.TabularBlock(pre=pre, post=post, aggregation=aggregation, schema=schema)
copy_layer = testing_utils.assert_serialization(inputs)
keep_cols = ["user_id", "item_id", "event_hour_sin", "event_hour_cos"]
tf_tabular_data = ml.sample_batch(testing_data, batch_size=100, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/test_core.py:25: AttributeError
________ test_serialization_continuous_features[False-concat-None-None] ________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4169fa0>, pre = None
post = None, aggregation = 'concat', include_schema = False
@pytest.mark.parametrize("pre", [None])
@pytest.mark.parametrize("post", [None])
@pytest.mark.parametrize("aggregation", [None, "concat"])
@pytest.mark.parametrize("include_schema", [True, False])
def test_serialization_continuous_features(
testing_data: Dataset, pre, post, aggregation, include_schema
):
schema = None
if include_schema:
schema = testing_data.schema
inputs = ml.TabularBlock(pre=pre, post=post, aggregation=aggregation, schema=schema)
copy_layer = testing_utils.assert_serialization(inputs)
keep_cols = ["user_id", "item_id", "event_hour_sin", "event_hour_cos"]
tf_tabular_data = ml.sample_batch(testing_data, batch_size=100, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/test_core.py:25: AttributeError
______________________ test_block_with_categorical_target ______________________
def test_block_with_categorical_target():
import pandas as pd
from merlin.schema import Schema, Tags
df = pd.DataFrame(
{
"Author": [12, 4, 23, 19],
"Engaging User": [23, 23, 12, 5],
"target": [1, 2, 3, 4],
}
)
s = Schema(
[
create_categorical_column("Engaging User", num_items=24, tags=[Tags.CATEGORICAL]),
create_categorical_column("Author", num_items=24, tags=[Tags.CATEGORICAL]),
create_categorical_column("target", num_items=5, tags=[Tags.CATEGORICAL, Tags.TARGET]),
]
)
data = Dataset(df, schema=s)
batch = mm.sample_batch(data, batch_size=2)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/test_dataset.py:334: AttributeError
_____________________ test_cross_with_inputs_to_be_concat ______________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e3bda90>
def test_cross_with_inputs_to_be_concat(testing_data: Dataset):
inputs = mm.InputBlock(
testing_data.schema,
embedding_options=mm.EmbeddingOptions(embedding_dim_default=128),
)
cross = mm.CrossBlock(depth=1, inputs=inputs)
output = cross(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_cross.py:74: AttributeError
_____________________________ test_dcn_v2_stacked ______________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e3ba7f0>
def test_dcn_v2_stacked(testing_data: Dataset):
dcn_body = (
mm.InputBlock(
testing_data.schema,
embedding_options=mm.EmbeddingOptions(embedding_dim_default=128),
aggregation="concat",
)
.connect(mm.CrossBlock(3))
.connect(mm.MLPBlock([512, 256]))
)
output = dcn_body(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_cross.py:91: AttributeError
_________________________ test_dcn_v2_stacked_low_rank _________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e23bfa0>
def test_dcn_v2_stacked_low_rank(testing_data: Dataset):
dcn_body = (
mm.InputBlock(
testing_data.schema,
embedding_options=mm.EmbeddingOptions(embedding_dim_default=128),
aggregation="concat",
)
.connect(mm.CrossBlock(3, low_rank_dim=64))
.connect(mm.MLPBlock([512, 256]))
)
output = dcn_body(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_cross.py:108: AttributeError
_____________________________ test_dcn_v2_parallel _____________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e1eae20>
def test_dcn_v2_parallel(testing_data: Dataset):
input_layer = mm.InputBlock(
testing_data.schema,
embedding_options=mm.EmbeddingOptions(embedding_dim_default=128),
aggregation="concat",
)
features = mm.sample_batch(testing_data, batch_size=100, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_cross.py:120: AttributeError
_______________________________ test_dlrm_block ________________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897d69dee0>
def test_dlrm_block(testing_data: Dataset):
schema = testing_data.schema
dlrm = mm.DLRMBlock(
schema,
embedding_dim=64,
bottom_block=mm.MLPBlock([64]),
top_block=mm.DenseResidualBlock(),
)
outputs = dlrm(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_dlrm.py:32: AttributeError
_________________________ test_dlrm_block_no_top_block _________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898423cee0>
def test_dlrm_block_no_top_block(testing_data: Dataset):
schema = testing_data.schema
dlrm = mm.DLRMBlock(
schema,
embedding_dim=64,
bottom_block=mm.MLPBlock([64]),
)
outputs = dlrm(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_dlrm.py:45: AttributeError
____________________ test_dlrm_block_no_continuous_features ____________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c2b91c0>
def test_dlrm_block_no_continuous_features(testing_data: Dataset):
schema = testing_data.schema.remove_by_tag(Tags.CONTINUOUS)
dlrm = mm.DLRMBlock(schema, embedding_dim=64, top_block=mm.MLPBlock([32]))
outputs = dlrm(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_dlrm.py:55: AttributeError
_____________________ test_dlrm_block_single_categ_feature _____________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b5063820>
def test_dlrm_block_single_categ_feature(testing_data: Dataset):
schema = testing_data.schema.select_by_tag([Tags.ITEM_ID])
dlrm = mm.DLRMBlock(schema, embedding_dim=64, top_block=mm.MLPBlock([32]))
outputs = dlrm(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_dlrm.py:72: AttributeError
__________________________ test_dlrm_with_embeddings ___________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897f253fa0>
def test_dlrm_with_embeddings(testing_data: Dataset):
schema = testing_data.schema
embedding_dim = 12
top_dim = 4
dlrm = mm.DLRMBlock(
schema,
embeddings=mm.Embeddings(schema.select_by_tag(Tags.CATEGORICAL), dim=embedding_dim),
bottom_block=mm.MLPBlock([embedding_dim]),
top_block=mm.MLPBlock([top_dim]),
)
outputs = dlrm(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_dlrm.py:122: AttributeError
_________________ test_mlp_block_yoochoose[None-None-relu-32] __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4ff7610>, dim = 32
activation = 'relu', dropout = None, normalization = None
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_________________ test_mlp_block_yoochoose[None-None-relu-64] __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4d30a90>, dim = 64
activation = 'relu', dropout = None, normalization = None
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_________________ test_mlp_block_yoochoose[None-None-tanh-32] __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897f253550>, dim = 32
activation = 'tanh', dropout = None, normalization = None
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_________________ test_mlp_block_yoochoose[None-None-tanh-64] __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c7e8790>, dim = 64
activation = 'tanh', dropout = None, normalization = None
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
__________________ test_mlp_block_yoochoose[None-0.5-relu-32] __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4d0a970>, dim = 32
activation = 'relu', dropout = 0.5, normalization = None
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
__________________ test_mlp_block_yoochoose[None-0.5-relu-64] __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c1833d0>, dim = 64
activation = 'relu', dropout = 0.5, normalization = None
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
__________________ test_mlp_block_yoochoose[None-0.5-tanh-32] __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4da2d60>, dim = 32
activation = 'tanh', dropout = 0.5, normalization = None
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
__________________ test_mlp_block_yoochoose[None-0.5-tanh-64] __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f8997dd5cd0>, dim = 64
activation = 'tanh', dropout = 0.5, normalization = None
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
______________ test_mlp_block_yoochoose[batch_norm-None-relu-32] _______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c7befa0>, dim = 32
activation = 'relu', dropout = None, normalization = 'batch_norm'
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
______________ test_mlp_block_yoochoose[batch_norm-None-relu-64] _______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f8997dddc70>, dim = 64
activation = 'relu', dropout = None, normalization = 'batch_norm'
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
______________ test_mlp_block_yoochoose[batch_norm-None-tanh-32] _______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c13e700>, dim = 32
activation = 'tanh', dropout = None, normalization = 'batch_norm'
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
______________ test_mlp_block_yoochoose[batch_norm-None-tanh-64] _______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f899405c0a0>, dim = 64
activation = 'tanh', dropout = None, normalization = 'batch_norm'
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_______________ test_mlp_block_yoochoose[batch_norm-0.5-relu-32] _______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e595d00>, dim = 32
activation = 'relu', dropout = 0.5, normalization = 'batch_norm'
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_______________ test_mlp_block_yoochoose[batch_norm-0.5-relu-64] _______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e499a30>, dim = 64
activation = 'relu', dropout = 0.5, normalization = 'batch_norm'
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_______________ test_mlp_block_yoochoose[batch_norm-0.5-tanh-32] _______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f899405c2e0>, dim = 32
activation = 'tanh', dropout = 0.5, normalization = 'batch_norm'
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_______________ test_mlp_block_yoochoose[batch_norm-0.5-tanh-64] _______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4cc0d60>, dim = 64
activation = 'tanh', dropout = 0.5, normalization = 'batch_norm'
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
____________ test_mlp_block_yoochoose[normalization2-None-relu-32] _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e595b20>, dim = 32
activation = 'relu', dropout = None, normalization = BatchNormalization()
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
____________ test_mlp_block_yoochoose[normalization2-None-relu-64] _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e7a6b20>, dim = 64
activation = 'relu', dropout = None, normalization = BatchNormalization()
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
____________ test_mlp_block_yoochoose[normalization2-None-tanh-32] _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c5cf7c0>, dim = 32
activation = 'tanh', dropout = None, normalization = BatchNormalization()
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
____________ test_mlp_block_yoochoose[normalization2-None-tanh-64] _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e341760>, dim = 64
activation = 'tanh', dropout = None, normalization = BatchNormalization()
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_____________ test_mlp_block_yoochoose[normalization2-0.5-relu-32] _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e351250>, dim = 32
activation = 'relu', dropout = 0.5, normalization = BatchNormalization()
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_____________ test_mlp_block_yoochoose[normalization2-0.5-relu-64] _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4d17ca0>, dim = 64
activation = 'relu', dropout = 0.5, normalization = BatchNormalization()
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_____________ test_mlp_block_yoochoose[normalization2-0.5-tanh-32] _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e8959d0>, dim = 32
activation = 'tanh', dropout = 0.5, normalization = BatchNormalization()
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_____________ test_mlp_block_yoochoose[normalization2-0.5-tanh-64] _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e4991f0>, dim = 64
activation = 'tanh', dropout = 0.5, normalization = BatchNormalization()
kernel_initializer = 'glorot_uniform', bias_initializer = 'zeros'
kernel_regularizer = 'l2', bias_regularizer = 'l1'
activity_regularizer = <keras.regularizers.L2 object at 0x7f8a5007fe50>
@pytest.mark.parametrize("dim", [32, 64])
@pytest.mark.parametrize("activation", ["relu", "tanh"])
@pytest.mark.parametrize("dropout", [None, 0.5])
@pytest.mark.parametrize(
"normalization", [None, "batch_norm", tf.keras.layers.BatchNormalization()]
)
def test_mlp_block_yoochoose(
testing_data: Dataset,
dim,
activation,
dropout,
normalization,
kernel_initializer="glorot_uniform",
bias_initializer="zeros",
kernel_regularizer="l2",
bias_regularizer="l1",
activity_regularizer=regularizers.l2(1e-4),
):
inputs = ml.InputBlock(testing_data.schema)
mlp = ml.MLPBlock(
[dim],
activation=activation,
dropout=dropout,
normalization=normalization,
kernel_initializer=kernel_initializer,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
)
body = ml.SequentialBlock([inputs, mlp])
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/test_mlp.py:58: AttributeError
_______________________ test_matrix_factorization_block ________________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897e8b85e0>
def test_matrix_factorization_block(music_streaming_data: Dataset):
mf = ml.QueryItemIdsEmbeddingsBlock(music_streaming_data.schema, dim=128)
outputs = mf(ml.sample_batch(music_streaming_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py:28: AttributeError
_______________________ test_matrix_factorization_block ________________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f89b4d529d0>
def test_matrix_factorization_block(music_streaming_data: Dataset):
mf = ml.QueryItemIdsEmbeddingsBlock(music_streaming_data.schema, dim=128)
outputs = mf(ml.sample_batch(music_streaming_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/retrieval/test_two_tower.py:32: AttributeError
_____________________________ test_two_tower_block _____________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e7a6eb0>
def test_two_tower_block(testing_data: Dataset):
two_tower = ml.TwoTowerBlock(testing_data.schema, query_tower=ml.MLPBlock([64, 128]))
outputs = two_tower(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/retrieval/test_two_tower.py:85: AttributeError
_____________ test_two_tower_block_with_l2_norm_on_towers_outputs ______________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c4046a0>
def test_two_tower_block_with_l2_norm_on_towers_outputs(testing_data: Dataset):
two_tower = ml.TwoTowerBlock(
testing_data.schema, query_tower=ml.MLPBlock([64, 128]), l2_normalization=True
)
outputs = two_tower(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/retrieval/test_two_tower.py:100: AttributeError
_______________________ test_two_tower_block_tower_save ________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f8965f00d30>
tmp_path = PosixPath('/tmp/pytest-of-jenkins/pytest-23/test_two_tower_block_tower_sav0')
def test_two_tower_block_tower_save(testing_data: Dataset, tmp_path):
two_tower = ml.TwoTowerBlock(testing_data.schema, query_tower=ml.MLPBlock([64, 128]))
two_tower(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/retrieval/test_two_tower.py:114: AttributeError
______________________ test_two_tower_block_serialization ______________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b432beb0>
def test_two_tower_block_serialization(testing_data: Dataset):
two_tower = ml.TwoTowerBlock(testing_data.schema, query_tower=ml.MLPBlock([64, 128]))
copy_two_tower = testing_utils.assert_serialization(two_tower)
outputs = copy_two_tower(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/blocks/retrieval/test_two_tower.py:139: AttributeError
______________________ test_concat_aggregation_yoochoose _______________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897f2a7e80>
def test_concat_aggregation_yoochoose(testing_data: Dataset):
tab_module = ml.InputBlock(testing_data.schema)
block = tab_module >> ml.ConcatFeatures()
out = block(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_aggregation.py:32: AttributeError
_______________________ test_stack_aggregation_yoochoose _______________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897c575f40>
def test_stack_aggregation_yoochoose(testing_data: Dataset):
tab_module = ml.EmbeddingFeatures.from_schema(testing_data.schema)
block = tab_module >> ml.StackFeatures()
out = block(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_aggregation.py:42: AttributeError
_________________ test_element_wise_sum_aggregation_yoochoose __________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c2e7a30>
def test_element_wise_sum_aggregation_yoochoose(testing_data: Dataset):
tab_module = ml.EmbeddingFeatures.from_schema(testing_data.schema)
block = tab_module >> ml.ElementwiseSum()
out = block(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_aggregation.py:64: AttributeError
____________ test_element_wise_sum_item_multi_aggregation_yoochoose ____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b5341c40>
def test_element_wise_sum_item_multi_aggregation_yoochoose(testing_data: Dataset):
tab_module = ml.EmbeddingFeatures.from_schema(testing_data.schema)
block = tab_module >> ml.ElementwiseSumItemMulti(testing_data.schema)
out = block(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_aggregation.py:103: AttributeError
_______________________ test_sequential_block_yoochoose ________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f896513b700>
def test_sequential_block_yoochoose(testing_data: Dataset):
body = ml.InputBlock(testing_data.schema).connect(ml.MLPBlock([64]))
outputs = body(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_base.py:27: AttributeError
______________________ test_parallel_block_pruning[True] _______________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f8965397340>
name_branches = True
@pytest.mark.parametrize("name_branches", [True, False])
def test_parallel_block_pruning(music_streaming_data: Dataset, name_branches: bool):
music_streaming_data.schema = music_streaming_data.schema.remove_by_tag(Tags.CONTINUOUS)
continuous_block = mm.Filter(music_streaming_data.schema.select_by_tag(Tags.CONTINUOUS))
embedding_block = mm.EmbeddingFeatures.from_schema(
music_streaming_data.schema.select_by_tag(Tags.CATEGORICAL)
)
if name_branches:
branches = {"continuous": continuous_block, "embedding": embedding_block}
else:
branches = [continuous_block, embedding_block]
input_block = mm.ParallelBlock(branches, schema=music_streaming_data.schema)
features = mm.sample_batch(music_streaming_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_combinators.py:28: AttributeError
______________________ test_parallel_block_pruning[False] ______________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f898c16ed30>
name_branches = False
@pytest.mark.parametrize("name_branches", [True, False])
def test_parallel_block_pruning(music_streaming_data: Dataset, name_branches: bool):
music_streaming_data.schema = music_streaming_data.schema.remove_by_tag(Tags.CONTINUOUS)
continuous_block = mm.Filter(music_streaming_data.schema.select_by_tag(Tags.CONTINUOUS))
embedding_block = mm.EmbeddingFeatures.from_schema(
music_streaming_data.schema.select_by_tag(Tags.CATEGORICAL)
)
if name_branches:
branches = {"continuous": continuous_block, "embedding": embedding_block}
else:
branches = [continuous_block, embedding_block]
input_block = mm.ParallelBlock(branches, schema=music_streaming_data.schema)
features = mm.sample_batch(music_streaming_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_combinators.py:28: AttributeError
______________________ test_parallel_block_serialization _______________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897e01fca0>
def test_parallel_block_serialization(music_streaming_data: Dataset):
unknown_filter = mm.Filter(["none"])
block = mm.ParallelBlock(mm.Filter(["position"]), unknown_filter, automatic_pruning=False)
block_copy = block.from_config(block.get_config())
assert not block_copy.automatic_pruning
assert unknown_filter not in block_copy.parallel_values
features = mm.sample_batch(music_streaming_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_combinators.py:44: AttributeError
_________________ test_parallel_block_schema_propagation[True] _________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f8986edfbe0>
name_branches = True
@pytest.mark.parametrize("name_branches", [True, False])
def test_parallel_block_schema_propagation(music_streaming_data, name_branches: bool):
continuous_block = mm.Filter(Tags.CONTINUOUS)
embedding_block = mm.EmbeddingFeatures.from_schema(
music_streaming_data.schema.select_by_tag(Tags.CATEGORICAL)
)
if name_branches:
branches = {"continuous": continuous_block, "embedding": embedding_block}
else:
branches = [continuous_block, embedding_block]
input_block = mm.ParallelBlock(branches, schema=music_streaming_data.schema)
features = mm.sample_batch(music_streaming_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_combinators.py:67: AttributeError
________________ test_parallel_block_schema_propagation[False] _________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f898c3dbb50>
name_branches = False
@pytest.mark.parametrize("name_branches", [True, False])
def test_parallel_block_schema_propagation(music_streaming_data, name_branches: bool):
continuous_block = mm.Filter(Tags.CONTINUOUS)
embedding_block = mm.EmbeddingFeatures.from_schema(
music_streaming_data.schema.select_by_tag(Tags.CATEGORICAL)
)
if name_branches:
branches = {"continuous": continuous_block, "embedding": embedding_block}
else:
branches = [continuous_block, embedding_block]
input_block = mm.ParallelBlock(branches, schema=music_streaming_data.schema)
features = mm.sample_batch(music_streaming_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_combinators.py:67: AttributeError
____________________ test_parallel_block_with_layers[True] _____________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897e6d30d0>
name_branches = True
@pytest.mark.parametrize("name_branches", [True, False])
def test_parallel_block_with_layers(music_streaming_data, name_branches: bool):
d, d_1 = tf.keras.layers.Dense(32), tf.keras.layers.Dense(32)
if name_branches:
branches = {"d": d, "d_1": d_1}
else:
branches = [d, d_1]
block = mm.ParallelBlock(branches, aggregation="concat")
model = mm.Model.from_block(block, music_streaming_data.schema)
outputs = block(tf.constant([[2.0]]))
assert outputs.shape == tf.TensorShape([1, 64])
features = mm.sample_batch(music_streaming_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_combinators.py:87: AttributeError
____________________ test_parallel_block_with_layers[False] ____________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897d465b20>
name_branches = False
@pytest.mark.parametrize("name_branches", [True, False])
def test_parallel_block_with_layers(music_streaming_data, name_branches: bool):
d, d_1 = tf.keras.layers.Dense(32), tf.keras.layers.Dense(32)
if name_branches:
branches = {"d": d, "d_1": d_1}
else:
branches = [d, d_1]
block = mm.ParallelBlock(branches, aggregation="concat")
model = mm.Model.from_block(block, music_streaming_data.schema)
outputs = block(tf.constant([[2.0]]))
assert outputs.shape == tf.TensorShape([1, 64])
features = mm.sample_batch(music_streaming_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_combinators.py:87: AttributeError
_______________________________ test_topk_index ________________________________
ecommerce_data = <merlin.io.dataset.Dataset object at 0x7f897f2dfaf0>
def test_topk_index(ecommerce_data: Dataset):
import tensorflow as tf
from merlin.models.tf.metrics.evaluation import ItemCoverageAt, PopularityBiasAt
model: mm.RetrievalModel = mm.TwoTowerModel(
ecommerce_data.schema, query_tower=mm.MLPBlock([64, 128])
)
model.compile(run_eagerly=False, optimizer="adam")
model.fit(ecommerce_data, epochs=1, batch_size=50)
item_features = ecommerce_data.schema.select_by_tag(Tags.ITEM).column_names
item_dataset = ecommerce_data.to_ddf()[item_features].drop_duplicates().compute()
item_dataset = Dataset(item_dataset)
recommender = model.to_top_k_recommender(item_dataset, k=20)
NUM_ITEMS = 1001
item_frequency = tf.sort(
tf.random.uniform((NUM_ITEMS,), minval=0, maxval=NUM_ITEMS, dtype=tf.int32)
)
eval_metrics = [
PopularityBiasAt(item_freq_probs=item_frequency, is_prob_distribution=False, k=10),
ItemCoverageAt(num_unique_items=NUM_ITEMS, k=10),
]
batch = mm.sample_batch(ecommerce_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_index.py:47: AttributeError
----------------------------- Captured stdout call -----------------------------
1/2 [==============>...............] - ETA: 6s - loss: 3.8977 - recall_at_10: 0.3200 - mrr_at_10: 0.0615 - ndcg_at_10: 0.1196 - map_at_10: 0.0615 - precision_at_10: 0.0320 - regularization_loss: 0.0000e+00
2/2 [==============================] - 7s 17ms/step - loss: 3.8971 - recall_at_10: 0.3200 - mrr_at_10: 0.0645 - ndcg_at_10: 0.1223 - map_at_10: 0.0645 - precision_at_10: 0.0320 - regularization_loss: 0.0000e+00
----------------------------- Captured stderr call -----------------------------
WARNING:tensorflow:AutoGraph could not transform <bound method NullHandler.handle of <_LiveLoggingNullHandler (NOTSET)>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output.
Cause: expected an indented block (__autograph_generated_filebx77h5hu.py, line 9)
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:No training configuration found in save file, so the model was not compiled. Compile it manually.
------------------------------ Captured log call -------------------------------
WARNING tensorflow:ag_logging.py:142 AutoGraph could not transform <bound method NullHandler.handle of <_LiveLoggingNullHandler (NOTSET)>> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, export AUTOGRAPH_VERBOSITY=10) and attach the full output.
Cause: expected an indented block (__autograph_generated_filebx77h5hu.py, line 9)
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING merlin_models:api.py:446 The sampler InBatchSampler returned no samples for this batch.
WARNING absl:save.py:233 Found untraced functions such as model_context_layer_call_fn, model_context_layer_call_and_return_conditional_losses, parallel_block_layer_call_fn, parallel_block_layer_call_and_return_conditional_losses, sequential_block_1_layer_call_fn while saving (showing 5 of 58). These functions will not be directly callable after loading.
WARNING tensorflow:load.py:167 No training configuration found in save file, so the model was not compiled. Compile it manually.
________________________ test_topk_recommender_outputs _________________________
ecommerce_data = <merlin.io.dataset.Dataset object at 0x7f897c4e5e20>
batch_size = 100
def test_topk_recommender_outputs(ecommerce_data: Dataset, batch_size=100):
import numpy as np
import tensorflow as tf
from merlin.models.tf.core.index import IndexBlock
from merlin.models.utils.dataset import unique_rows_by_features
def numpy_recall(labels, top_item_ids, k):
return np.equal(np.expand_dims(labels, -1), top_item_ids[:, :k]).max(axis=-1).mean()
model = mm.TwoTowerModel(
ecommerce_data.schema,
query_tower=mm.MLPBlock([64]),
samplers=[mm.InBatchSampler()],
)
model.compile("adam", metrics=[mm.RecallAt(10)], run_eagerly=False)
model.fit(ecommerce_data, batch_size=batch_size, epochs=3)
eval_metrics = model.evaluate(
ecommerce_data, item_corpus=ecommerce_data, batch_size=batch_size, return_dict=True
)
# Manually compute top-k ids for a given batch
batch = mm.sample_batch(ecommerce_data, batch_size=batch_size, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_index.py:99: AttributeError
----------------------------- Captured stdout call -----------------------------
Epoch 1/3
1/1 [==============================] - ETA: 0s - loss: 4.5842 - recall_at_10: 0.1300 - regularization_loss: 0.0000e+00
1/1 [==============================] - 3s 3s/step - loss: 4.5842 - recall_at_10: 0.1300 - regularization_loss: 0.0000e+00
Epoch 2/3
1/1 [==============================] - ETA: 0s - loss: 4.5774 - recall_at_10: 0.2600 - regularization_loss: 0.0000e+00
1/1 [==============================] - 0s 56ms/step - loss: 4.5774 - recall_at_10: 0.2600 - regularization_loss: 0.0000e+00
Epoch 3/3
1/1 [==============================] - ETA: 0s - loss: 4.5707 - recall_at_10: 0.3000 - regularization_loss: 0.0000e+00
1/1 [==============================] - 0s 64ms/step - loss: 4.5707 - recall_at_10: 0.3000 - regularization_loss: 0.0000e+00
1/1 [==============================] - ETA: 0s - loss: 1.3298 - recall_at_10: 0.5800 - regularization_loss: 0.0000e+00
1/1 [==============================] - 1s 1s/step - loss: 1.3298 - recall_at_10: 0.5800 - regularization_loss: 0.0000e+00
----------------------------- Captured stderr call -----------------------------
WARNING:tensorflow:No training configuration found in save file, so the model was not compiled. Compile it manually.
------------------------------ Captured log call -------------------------------
WARNING merlin_models:api.py:446 The sampler InBatchSampler returned no samples for this batch.
WARNING absl:save.py:233 Found untraced functions such as model_context_layer_call_fn, model_context_layer_call_and_return_conditional_losses, parallel_block_layer_call_fn, parallel_block_layer_call_and_return_conditional_losses, sequential_block_1_layer_call_fn while saving (showing 5 of 54). These functions will not be directly callable after loading.
WARNING tensorflow:load.py:167 No training configuration found in save file, so the model was not compiled. Compile it manually.
WARNING merlin_models:api.py:446 The sampler InBatchSampler returned no samples for this batch.
__________________ test_model_pre_transforming_targets[True] ___________________
ecommerce_data = <merlin.io.dataset.Dataset object at 0x7f897e48c100>
run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True])
def test_model_pre_transforming_targets(ecommerce_data: Dataset, run_eagerly):
class FlipTargets(tf.keras.layers.Layer):
def call(self, inputs: Dict[str, tf.Tensor], targets=None):
if targets:
if isinstance(targets, dict):
flipped = {}
for key in targets:
flipped[key] = self.flip_target(targets[key])
else:
flipped = self.flip_target(targets)
return Prediction(inputs, flipped)
return inputs
@staticmethod
def flip_target(target):
dtype = target.dtype
return tf.cast(tf.math.logical_not(tf.cast(target, tf.bool)), dtype)
model = mm.Model(
mm.InputBlock(ecommerce_data.schema),
mm.MLPBlock([64]),
mm.BinaryClassificationTask("click"),
pre=FlipTargets(),
)
features, targets = mm.sample_batch(ecommerce_data, batch_size=100)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/core/test_prediction.py:70: AttributeError
_______ TestAddRandomNegativesToBatch.test_calling_without_targets[True] _______
self = <tests.unit.tf.data_augmentation.test_negative_sampling.TestAddRandomNegativesToBatch object at 0x7f8a407112e0>
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897d695130>
to_dense = True, tf_random_seed = 1
@pytest.mark.parametrize("to_dense", [True, False])
def test_calling_without_targets(
self, music_streaming_data: Dataset, to_dense: bool, tf_random_seed: int
):
schema = music_streaming_data.schema
batch_size, n_per_positive = 10, 5
features = mm.sample_batch(
music_streaming_data, batch_size=batch_size, include_targets=False, to_dense=to_dense
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/data_augmentation/test_negative_sampling.py:111: AttributeError
______ TestAddRandomNegativesToBatch.test_calling_without_targets[False] _______
self = <tests.unit.tf.data_augmentation.test_negative_sampling.TestAddRandomNegativesToBatch object at 0x7f8a40711250>
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897e5c0c40>
to_dense = False, tf_random_seed = 1
@pytest.mark.parametrize("to_dense", [True, False])
def test_calling_without_targets(
self, music_streaming_data: Dataset, to_dense: bool, tf_random_seed: int
):
schema = music_streaming_data.schema
batch_size, n_per_positive = 10, 5
features = mm.sample_batch(
music_streaming_data, batch_size=batch_size, include_targets=False, to_dense=to_dense
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/data_augmentation/test_negative_sampling.py:111: AttributeError
_______________ TestAddRandomNegativesToBatch.test_calling[True] _______________
self = <tests.unit.tf.data_augmentation.test_negative_sampling.TestAddRandomNegativesToBatch object at 0x7f8a40711520>
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f896e2877f0>
to_dense = True, tf_random_seed = 1
@pytest.mark.parametrize("to_dense", [True, False])
def test_calling(self, music_streaming_data: Dataset, to_dense: bool, tf_random_seed: int):
schema = music_streaming_data.schema
batch_size, n_per_positive = 10, 5
inputs, targets = mm.sample_batch(
music_streaming_data, batch_size=batch_size, include_targets=True, to_dense=to_dense
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/data_augmentation/test_negative_sampling.py:129: AttributeError
______________ TestAddRandomNegativesToBatch.test_calling[False] _______________
self = <tests.unit.tf.data_augmentation.test_negative_sampling.TestAddRandomNegativesToBatch object at 0x7f8a407115b0>
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897e5c0af0>
to_dense = False, tf_random_seed = 1
@pytest.mark.parametrize("to_dense", [True, False])
def test_calling(self, music_streaming_data: Dataset, to_dense: bool, tf_random_seed: int):
schema = music_streaming_data.schema
batch_size, n_per_positive = 10, 5
inputs, targets = mm.sample_batch(
music_streaming_data, batch_size=batch_size, include_targets=True, to_dense=to_dense
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/data_augmentation/test_negative_sampling.py:129: AttributeError
__________ TestAddRandomNegativesToBatch.test_run_when_testing[True] ___________
self = <tests.unit.tf.data_augmentation.test_negative_sampling.TestAddRandomNegativesToBatch object at 0x7f8a40711430>
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897f2df730>
to_dense = True, tf_random_seed = 1
@pytest.mark.parametrize("to_dense", [True, False])
def test_run_when_testing(
self, music_streaming_data: Dataset, to_dense: bool, tf_random_seed: int
):
schema = music_streaming_data.schema
batch_size, n_per_positive = 10, 5
inputs, targets = mm.sample_batch(
music_streaming_data, batch_size=batch_size, include_targets=True, to_dense=to_dense
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/data_augmentation/test_negative_sampling.py:156: AttributeError
__________ TestAddRandomNegativesToBatch.test_run_when_testing[False] __________
self = <tests.unit.tf.data_augmentation.test_negative_sampling.TestAddRandomNegativesToBatch object at 0x7f8a40711370>
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897e5cc490>
to_dense = False, tf_random_seed = 1
@pytest.mark.parametrize("to_dense", [True, False])
def test_run_when_testing(
self, music_streaming_data: Dataset, to_dense: bool, tf_random_seed: int
):
schema = music_streaming_data.schema
batch_size, n_per_positive = 10, 5
inputs, targets = mm.sample_batch(
music_streaming_data, batch_size=batch_size, include_targets=True, to_dense=to_dense
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/data_augmentation/test_negative_sampling.py:156: AttributeError
______________ TestAddRandomNegativesToBatch.test_in_model[True] _______________
self = <tests.unit.tf.data_augmentation.test_negative_sampling.TestAddRandomNegativesToBatch object at 0x7f8a407119a0>
run_eagerly = True
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f898c5fa760>
tf_random_seed = 1
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_in_model(self, run_eagerly, music_streaming_data: Dataset, tf_random_seed: int):
dataset = music_streaming_data
schema = dataset.schema
sampling = mm.Cond(
ExampleIsTraining(),
UniformNegativeSampling(schema, 5, seed=tf_random_seed),
ExamplePredictionIdentity(),
)
model = mm.Model(
mm.InputBlock(schema),
sampling,
mm.MLPBlock([64]),
mm.BinaryClassificationTask("click"),
)
batch_size = 10
features, targets = mm.sample_batch(
music_streaming_data, batch_size=batch_size, to_dense=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/data_augmentation/test_negative_sampling.py:195: AttributeError
______________ TestAddRandomNegativesToBatch.test_in_model[False] ______________
self = <tests.unit.tf.data_augmentation.test_negative_sampling.TestAddRandomNegativesToBatch object at 0x7f8a40711940>
run_eagerly = False
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897d6b3580>
tf_random_seed = 1
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_in_model(self, run_eagerly, music_streaming_data: Dataset, tf_random_seed: int):
dataset = music_streaming_data
schema = dataset.schema
sampling = mm.Cond(
ExampleIsTraining(),
UniformNegativeSampling(schema, 5, seed=tf_random_seed),
ExamplePredictionIdentity(),
)
model = mm.Model(
mm.InputBlock(schema),
sampling,
mm.MLPBlock([64]),
mm.BinaryClassificationTask("click"),
)
batch_size = 10
features, targets = mm.sample_batch(
music_streaming_data, batch_size=batch_size, to_dense=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/data_augmentation/test_negative_sampling.py:195: AttributeError
__________________ test_example_06_defining_own_architecture ___________________
tb = <testbook.client.TestbookNotebookClient object at 0x7f89b70fbc10>
@testbook(
REPO_ROOT / "examples/06-Define-your-own-architecture-with-Merlin-Models.ipynb", execute=False
)
def test_example_06_defining_own_architecture(tb):
tb.inject(
"""
from unittest.mock import patch
from merlin.datasets.synthetic import generate_data
mock_train, mock_valid = generate_data(
input="movielens-1m",
num_rows=1000,
set_sizes=(0.8, 0.2)
)
p1 = patch(
"merlin.datasets.entertainment.get_movielens",
return_value=[mock_train, mock_valid]
)
p1.start()
"""
)
tb.execute()
tests/unit/tf/examples/test_06_advanced_own_architecture.py:28:
/usr/local/lib/python3.8/dist-packages/testbook/client.py:147: in execute
super().execute_cell(cell, index)
/usr/local/lib/python3.8/dist-packages/nbclient/util.py:85: in wrapped
return just_run(coro(*args, **kwargs))
/usr/local/lib/python3.8/dist-packages/nbclient/util.py:60: in just_run
return loop.run_until_complete(coro)
/usr/lib/python3.8/asyncio/base_events.py:616: in run_until_complete
return future.result()
/usr/local/lib/python3.8/dist-packages/nbclient/client.py:1025: in async_execute_cell
await self._check_raise_for_error(cell, cell_index, exec_reply)
self = <testbook.client.TestbookNotebookClient object at 0x7f89b70fbc10>
cell = {'cell_type': 'code', 'execution_count': 6, 'id': '932d878e', 'metadata': {'pycharm': {'name': '#%%\n'}, 'execution': ...'"]}], 'source': 'batch = mm.sample_batch(valid, batch_size=5, shuffle=False, include_targets=False)\nbatch["userId"]'}
cell_index = 22
exec_reply = {'buffers': [], 'content': {'ename': 'AttributeError', 'engine_info': {'engine_id': -1, 'engine_uuid': '9880031f-4cab-...e, 'engine': '9880031f-4cab-4529-abbe-364d1f50220a', 'started': '2022-09-02T08:19:11.756038Z', 'status': 'error'}, ...}
async def _check_raise_for_error(
self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:
if exec_reply is None:
return None
exec_reply_content = exec_reply['content']
if exec_reply_content['status'] != 'error':
return None
cell_allows_errors = (not self.force_raise_errors) and (
self.allow_errors
or exec_reply_content.get('ename') in self.allow_error_names
or "raises-exception" in cell.metadata.get("tags", [])
)
await run_hook(
self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
)
if not cell_allows_errors:
raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
E nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
E ------------------
E batch = mm.sample_batch(valid, batch_size=5, shuffle=False, include_targets=False)
E batch["userId"]
E ------------------
E
E [0;31m---------------------------------------------------------------------------[0m
E [0;31mAttributeError[0m Traceback (most recent call last)
E Input [0;32mIn [6][0m, in [0;36m<cell line: 1>[0;34m()[0m
E [0;32m----> 1[0m batch [38;5;241m=[39m [43mmm[49m[38;5;241;43m.[39;49m[43msample_batch[49m(valid, batch_size[38;5;241m=[39m[38;5;241m5[39m, shuffle[38;5;241m=[39m[38;5;28;01mFalse[39;00m, include_targets[38;5;241m=[39m[38;5;28;01mFalse[39;00m)
E [1;32m 2[0m batch[[38;5;124m"[39m[38;5;124muserId[39m[38;5;124m"[39m]
E
E [0;31mAttributeError[0m: module 'merlin.models.tf' has no attribute 'sample_batch'
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
/usr/local/lib/python3.8/dist-packages/nbclient/client.py:919: CellExecutionError
----------------------------- Captured stderr call -----------------------------
2022-09-02 08:19:10.315224: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-09-02 08:19:11.669652: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 0
2022-09-02 08:19:11.669819: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory: -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0
2022-09-02 08:19:11.670540: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 1
2022-09-02 08:19:11.670597: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 14508 MB memory: -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown
h.close()
File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close
self.stream.close()
File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close
self.watch_fd_thread.join()
AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'
_____________________ test_usecase_ecommerce_session_based _____________________
tb = <testbook.client.TestbookNotebookClient object at 0x7f89b7a6ee80>
@testbook(
REPO_ROOT / "examples/usecases/ecommerce-session-based-next-item-prediction-for-fashion.ipynb",
timeout=180,
execute=False,
)
def test_usecase_ecommerce_session_based(tb):
tb.inject(
"""
import os
from unittest.mock import patch
from merlin.datasets.synthetic import generate_data
mock_train, mock_valid = generate_data(
input="dressipi2022-preprocessed",
num_rows=10000,
set_sizes=(0.8, 0.2)
)
p1 = patch(
"merlin.datasets.ecommerce.get_dressipi2022",
return_value=[mock_train, mock_valid]
)
p1.start()
os.environ["DATA_FOLDER"] = "/tmp/dressipi2022/"
os.environ["EPOCHS"] = "1"
"""
)
tb.execute()
tests/unit/tf/examples/test_usecase_ecommerce_session_based.py:31:
/usr/local/lib/python3.8/dist-packages/testbook/client.py:147: in execute
super().execute_cell(cell, index)
/usr/local/lib/python3.8/dist-packages/nbclient/util.py:85: in wrapped
return just_run(coro(*args, **kwargs))
/usr/local/lib/python3.8/dist-packages/nbclient/util.py:60: in just_run
return loop.run_until_complete(coro)
/usr/lib/python3.8/asyncio/base_events.py:616: in run_until_complete
return future.result()
/usr/local/lib/python3.8/dist-packages/nbclient/client.py:1025: in async_execute_cell
await self._check_raise_for_error(cell, cell_index, exec_reply)
self = <testbook.client.TestbookNotebookClient object at 0x7f89b7a6ee80>
cell = {'cell_type': 'code', 'execution_count': 3, 'id': 'cb0e1975', 'metadata': {'execution': {'iopub.status.busy': '2022-09...tsTemperatureScaler,\n)\n\nfrom merlin.core.dispatch import get_lib\nimport merlin.models.tf.dataset as tf_dataloader'}
cell_index = 4
exec_reply = {'buffers': [], 'content': {'ename': 'ModuleNotFoundError', 'engine_info': {'engine_id': -1, 'engine_uuid': 'cb829626-...e, 'engine': 'cb829626-3e82-4d2a-ab06-c95addfece73', 'started': '2022-09-02T08:19:33.111430Z', 'status': 'error'}, ...}
async def _check_raise_for_error(
self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:
if exec_reply is None:
return None
exec_reply_content = exec_reply['content']
if exec_reply_content['status'] != 'error':
return None
cell_allows_errors = (not self.force_raise_errors) and (
self.allow_errors
or exec_reply_content.get('ename') in self.allow_error_names
or "raises-exception" in cell.metadata.get("tags", [])
)
await run_hook(
self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
)
if not cell_allows_errors:
raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
E nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
E ------------------
E import os
E import glob
E
E import nvtabular as nvt
E from merlin.io import Dataset
E from merlin.schema import Schema, Tags
E from nvtabular.ops import (
E AddMetadata,
E )
E
E
E import tensorflow as tf
E
E from tensorflow.keras import regularizers
E from merlin.models.tf.loader import Loader
E
E import merlin.models.tf as mm
E from merlin.models.tf import InputBlock
E from merlin.models.tf.models.base import Model
E from merlin.models.tf.core.transformations import (
E ItemsPredictionWeightTying,
E L2Norm,
E LogitsTemperatureScaler,
E )
E
E from merlin.core.dispatch import get_lib
E import merlin.models.tf.dataset as tf_dataloader
E ------------------
E
E [0;31m---------------------------------------------------------------------------[0m
E [0;31mModuleNotFoundError[0m Traceback (most recent call last)
E Input [0;32mIn [3][0m, in [0;36m<cell line: 27>[0;34m()[0m
E [1;32m 20[0m [38;5;28;01mfrom[39;00m [38;5;21;01mmerlin[39;00m[38;5;21;01m.[39;00m[38;5;21;01mmodels[39;00m[38;5;21;01m.[39;00m[38;5;21;01mtf[39;00m[38;5;21;01m.[39;00m[38;5;21;01mcore[39;00m[38;5;21;01m.[39;00m[38;5;21;01mtransformations[39;00m [38;5;28;01mimport[39;00m (
E [1;32m 21[0m ItemsPredictionWeightTying,
E [1;32m 22[0m L2Norm,
E [1;32m 23[0m LogitsTemperatureScaler,
E [1;32m 24[0m )
E [1;32m 26[0m [38;5;28;01mfrom[39;00m [38;5;21;01mmerlin[39;00m[38;5;21;01m.[39;00m[38;5;21;01mcore[39;00m[38;5;21;01m.[39;00m[38;5;21;01mdispatch[39;00m [38;5;28;01mimport[39;00m get_lib
E [0;32m---> 27[0m [38;5;28;01mimport[39;00m [38;5;21;01mmerlin[39;00m[38;5;21;01m.[39;00m[38;5;21;01mmodels[39;00m[38;5;21;01m.[39;00m[38;5;21;01mtf[39;00m[38;5;21;01m.[39;00m[38;5;21;01mdataset[39;00m [38;5;28;01mas[39;00m [38;5;21;01mtf_dataloader[39;00m
E
E [0;31mModuleNotFoundError[0m: No module named 'merlin.models.tf.dataset'
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
/usr/local/lib/python3.8/dist-packages/nbclient/client.py:919: CellExecutionError
----------------------------- Captured stderr call -----------------------------
2022-09-02 08:19:34.789022: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-09-02 08:19:36.144689: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 0
2022-09-02 08:19:36.144859: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory: -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0
2022-09-02 08:19:36.145630: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 1
2022-09-02 08:19:36.145690: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 14508 MB memory: -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown
h.close()
File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close
self.stream.close()
File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close
self.watch_fd_thread.join()
AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'
______________________ test_continuous_features_yoochoose ______________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c6a8250>
def test_continuous_features_yoochoose(testing_data: Dataset):
schema = testing_data.schema.select_by_tag([Tags.CONTINUOUS])
inputs = ml.ContinuousFeatures.from_schema(schema)
outputs = inputs(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_continuous.py:36: AttributeError
______________________ test_embedding_features_yoochoose _______________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e35c6a0>
def test_embedding_features_yoochoose(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
emb_module = mm.EmbeddingFeatures.from_schema(
schema,
embedding_options=mm.EmbeddingOptions(embedding_dim_default=512),
)
embeddings = emb_module(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:271: AttributeError
________________ test_embedding_features_yoochoose_custom_dims _________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c77a310>
def test_embedding_features_yoochoose_custom_dims(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
emb_module = mm.EmbeddingFeatures.from_schema(
schema,
embedding_options=mm.EmbeddingOptions(
embedding_dims={"item_id": 100}, embedding_dim_default=64
),
)
embeddings = emb_module(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:325: AttributeError
________________________ test_embedding_features_l2_reg ________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f8986cdf160>
def test_embedding_features_l2_reg(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
emb_module = mm.EmbeddingFeatures.from_schema(
schema,
embedding_options=mm.EmbeddingOptions(
embedding_dims={"item_id": 100}, embedding_dim_default=64, embeddings_l2_reg=0.1
),
)
_ = emb_module(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:346: AttributeError
___________ test_embedding_features_yoochoose_infer_embedding_sizes ____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f8965f37070>
def test_embedding_features_yoochoose_infer_embedding_sizes(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
emb_module = mm.EmbeddingFeatures.from_schema(
schema,
embedding_options=mm.EmbeddingOptions(
infer_embedding_sizes=True, infer_embedding_sizes_multiplier=3.0
),
)
embeddings = emb_module(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:368: AttributeError
______ test_embedding_features_yoochoose_infer_embedding_sizes_multiple_8 ______
testing_data = <merlin.io.dataset.Dataset object at 0x7f896e01da30>
def test_embedding_features_yoochoose_infer_embedding_sizes_multiple_8(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
emb_module = mm.EmbeddingFeatures.from_schema(
schema,
embedding_options=mm.EmbeddingOptions(
infer_embedding_sizes=True,
infer_embedding_sizes_multiplier=3.0,
infer_embeddings_ensure_dim_multiple_of_8=True,
),
)
embeddings = emb_module(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:404: AttributeError
______ test_embedding_features_yoochoose_partially_infer_embedding_sizes _______
testing_data = <merlin.io.dataset.Dataset object at 0x7f897d43c0a0>
def test_embedding_features_yoochoose_partially_infer_embedding_sizes(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
emb_module = mm.EmbeddingFeatures.from_schema(
schema,
embedding_options=mm.EmbeddingOptions(
embedding_dims={"user_id": 50, "user_country": 100},
infer_embedding_sizes=True,
infer_embedding_sizes_multiplier=3.0,
),
)
embeddings = emb_module(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:440: AttributeError
____________ test_embedding_features_yoochoose_custom_initializers _____________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c08b130>
def test_embedding_features_yoochoose_custom_initializers(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
random_max_abs_value = 0.3
emb_module = mm.EmbeddingFeatures.from_schema(
schema,
embedding_options=mm.EmbeddingOptions(
embedding_dim_default=512,
embeddings_initializers={
"user_id": RandomUniform(minval=-random_max_abs_value, maxval=random_max_abs_value),
"user_country": RandomUniform(
minval=-random_max_abs_value, maxval=random_max_abs_value
),
},
),
)
embeddings = emb_module(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:481: AttributeError
___________ test_embedding_features_yoochoose_pretrained_initializer ___________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897d0c9a30>
def test_embedding_features_yoochoose_pretrained_initializer(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
pretrained_emb_item_ids = np.random.random((51997, 64))
pretrained_emb_categories = np.random.random((332, 64))
emb_module = mm.EmbeddingFeatures.from_schema(
schema,
embedding_options=mm.EmbeddingOptions(
embeddings_initializers={
"item_id": mm.TensorInitializer(pretrained_emb_item_ids),
"categories": mm.TensorInitializer(pretrained_emb_categories),
},
),
)
# Calling the first batch, so that embedding tables are build
_ = emb_module(mm.sample_batch(testing_data, batch_size=10, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:522: AttributeError
_____ test_embedding_features_exporting_and_loading_pretrained_initializer _____
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c4e9160>
def test_embedding_features_exporting_and_loading_pretrained_initializer(testing_data: Dataset):
schema = testing_data.schema.select_by_tag(Tags.CATEGORICAL)
emb_module = mm.EmbeddingFeatures.from_schema(schema)
# Calling the first batch, so that embedding tables are build
_ = emb_module(mm.sample_batch(testing_data, batch_size=10, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_embedding.py:537: AttributeError
____________________________ test_tabular_features _____________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f898c30ff70>
def test_tabular_features(testing_data: Dataset):
tab_module = ml.InputBlock(testing_data.schema)
outputs = tab_module(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:30: AttributeError
____________________ test_tabular_features_with_projection _____________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f89b4d65640>
def test_tabular_features_with_projection(testing_data: Dataset):
tab_module = ml.InputBlock(testing_data.schema, continuous_projection=ml.MLPBlock([64]))
outputs = tab_module(ml.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:49: AttributeError
_________________ test_tabular_seq_features_ragged_embeddings __________________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f89671eeb80>
def test_tabular_seq_features_ragged_embeddings(sequence_testing_data: Dataset):
tab_module = ml.InputBlockV2(
sequence_testing_data.schema,
embeddings=ml.Embeddings(
sequence_testing_data.schema.select_by_tag(Tags.CATEGORICAL), sequence_combiner=None
),
aggregation=None,
)
batch = ml.sample_batch(
sequence_testing_data, batch_size=100, include_targets=False, to_ragged=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:104: AttributeError
_________ test_tabular_seq_features_ragged_emb_combiner[seq_combiner0] _________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f897e024880>
seq_combiner = Lambda()
@pytest.mark.parametrize(
"seq_combiner",
[tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=1)), "mean"],
)
def test_tabular_seq_features_ragged_emb_combiner(sequence_testing_data: Dataset, seq_combiner):
con2d = sequence_testing_data.schema.select_by_tag(Tags.CONTINUOUS).remove_by_tag(Tags.SEQUENCE)
input_block = ml.InputBlockV2(
sequence_testing_data.schema,
embeddings=ml.Embeddings(
sequence_testing_data.schema.select_by_tag(Tags.CATEGORICAL),
sequence_combiner=seq_combiner,
),
continuous_column_selector=con2d,
aggregation=None,
)
batch = ml.sample_batch(
sequence_testing_data, batch_size=100, include_targets=False, to_ragged=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:134: AttributeError
_____________ test_tabular_seq_features_ragged_emb_combiner[mean] ______________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f896efd00d0>
seq_combiner = 'mean'
@pytest.mark.parametrize(
"seq_combiner",
[tf.keras.layers.Lambda(lambda x: tf.reduce_mean(x, axis=1)), "mean"],
)
def test_tabular_seq_features_ragged_emb_combiner(sequence_testing_data: Dataset, seq_combiner):
con2d = sequence_testing_data.schema.select_by_tag(Tags.CONTINUOUS).remove_by_tag(Tags.SEQUENCE)
input_block = ml.InputBlockV2(
sequence_testing_data.schema,
embeddings=ml.Embeddings(
sequence_testing_data.schema.select_by_tag(Tags.CATEGORICAL),
sequence_combiner=seq_combiner,
),
continuous_column_selector=con2d,
aggregation=None,
)
batch = ml.sample_batch(
sequence_testing_data, batch_size=100, include_targets=False, to_ragged=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:134: AttributeError
_____________ test_tabular_seq_features_ragged_custom_emb_combiner _____________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f899402a4f0>
def test_tabular_seq_features_ragged_custom_emb_combiner(sequence_testing_data: Dataset):
schema = sequence_testing_data.schema
schema = schema + Schema([ColumnSchema("item_id_seq_weights")])
assert "item_id_seq_weights" in schema.column_names
batch = ml.sample_batch(
sequence_testing_data, batch_size=100, include_targets=False, to_ragged=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:152: AttributeError
___________ test_tabular_seq_features_avg_embeddings_with_mapvalues ____________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f896e12b8e0>
def test_tabular_seq_features_avg_embeddings_with_mapvalues(sequence_testing_data: Dataset):
cat_schema = sequence_testing_data.schema.select_by_tag(Tags.CATEGORICAL)
batch = ml.sample_batch(
sequence_testing_data, batch_size=100, include_targets=False, to_ragged=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:199: AttributeError
______________ test_embedding_tables_from_schema_infer_dims[None] ______________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f898c153340>
aggregation = None
@pytest.mark.parametrize("aggregation", [None, "concat"])
def test_embedding_tables_from_schema_infer_dims(sequence_testing_data: Dataset, aggregation: str):
cat_schema = sequence_testing_data.schema.select_by_tag(Tags.CATEGORICAL)
embeddings_block = ml.Embeddings(
cat_schema.select_by_tag(Tags.CATEGORICAL),
dim={"item_id_seq": 15, "test_user_id": 21},
embeddings_initializer="truncated_normal",
)
input_block = ml.InputBlockV2(cat_schema, embeddings=embeddings_block, aggregation=aggregation)
batch = ml.sample_batch(
sequence_testing_data, batch_size=100, include_targets=False, to_ragged=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:235: AttributeError
_____________ test_embedding_tables_from_schema_infer_dims[concat] _____________
sequence_testing_data = <merlin.io.dataset.Dataset object at 0x7f897cf49e80>
aggregation = 'concat'
@pytest.mark.parametrize("aggregation", [None, "concat"])
def test_embedding_tables_from_schema_infer_dims(sequence_testing_data: Dataset, aggregation: str):
cat_schema = sequence_testing_data.schema.select_by_tag(Tags.CATEGORICAL)
embeddings_block = ml.Embeddings(
cat_schema.select_by_tag(Tags.CATEGORICAL),
dim={"item_id_seq": 15, "test_user_id": 21},
embeddings_initializer="truncated_normal",
)
input_block = ml.InputBlockV2(cat_schema, embeddings=embeddings_block, aggregation=aggregation)
batch = ml.sample_batch(
sequence_testing_data, batch_size=100, include_targets=False, to_ragged=True
)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/inputs/test_tabular.py:235: AttributeError
_____ test_wide_deep_model_wide_onehot_multihot_feature_interaction[True] ______
ecommerce_data = <merlin.io.dataset.Dataset object at 0x7f896f272040>
run_eagerly = True
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_wide_deep_model_wide_onehot_multihot_feature_interaction(ecommerce_data, run_eagerly):
ml_dataset = generate_data("movielens-1m", 100)
# data_ddf = ml_dataset.to_ddf()
# data_ddf = data_ddf[[c for c in list(data_ddf.columns) if c != "rating"]]
# Removing the rating regression target
schema = ml_dataset.schema.remove_col("rating")
target_col = schema.select_by_tag(Tags.TARGET).column_names[0]
cat_schema = schema.select_by_tag(Tags.CATEGORICAL)
cat_schema_onehot = cat_schema.remove_col("genres")
cat_schema_multihot = cat_schema.select_by_name("genres")
ignore_combinations = [["age", "userId"], ["userId", "occupation"]]
wide_preprocessing_blocks = [
# One-hot features
ml.SequentialBlock(
ml.Filter(cat_schema_onehot),
ml.CategoryEncoding(cat_schema_onehot, sparse=True, output_mode="one_hot"),
),
# Multi-hot features
ml.SequentialBlock(
ml.Filter(cat_schema_multihot),
ml.AsDenseFeatures(max_seq_length=6),
ml.CategoryEncoding(cat_schema_multihot, sparse=True, output_mode="multi_hot"),
),
# 2nd level feature interactions of one-hot features
ml.SequentialBlock(
ml.Filter(cat_schema),
ml.AsDenseFeatures(max_seq_length=6),
ml.HashedCrossAll(
cat_schema,
num_bins=100,
max_level=2,
output_mode="multi_hot",
sparse=True,
ignore_combinations=ignore_combinations,
),
),
]
batch = ml.sample_batch(ml_dataset, batch_size=100, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/models/test_ranking.py:314: AttributeError
------------------------------ Captured log call -------------------------------
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_age_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_gender_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_occupation_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_zipcode_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_userId_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_movieId_rating, assuming [0, 1]
_____ test_wide_deep_model_wide_onehot_multihot_feature_interaction[False] _____
ecommerce_data = <merlin.io.dataset.Dataset object at 0x7f896d0102b0>
run_eagerly = False
@pytest.mark.parametrize("run_eagerly", [True, False])
def test_wide_deep_model_wide_onehot_multihot_feature_interaction(ecommerce_data, run_eagerly):
ml_dataset = generate_data("movielens-1m", 100)
# data_ddf = ml_dataset.to_ddf()
# data_ddf = data_ddf[[c for c in list(data_ddf.columns) if c != "rating"]]
# Removing the rating regression target
schema = ml_dataset.schema.remove_col("rating")
target_col = schema.select_by_tag(Tags.TARGET).column_names[0]
cat_schema = schema.select_by_tag(Tags.CATEGORICAL)
cat_schema_onehot = cat_schema.remove_col("genres")
cat_schema_multihot = cat_schema.select_by_name("genres")
ignore_combinations = [["age", "userId"], ["userId", "occupation"]]
wide_preprocessing_blocks = [
# One-hot features
ml.SequentialBlock(
ml.Filter(cat_schema_onehot),
ml.CategoryEncoding(cat_schema_onehot, sparse=True, output_mode="one_hot"),
),
# Multi-hot features
ml.SequentialBlock(
ml.Filter(cat_schema_multihot),
ml.AsDenseFeatures(max_seq_length=6),
ml.CategoryEncoding(cat_schema_multihot, sparse=True, output_mode="multi_hot"),
),
# 2nd level feature interactions of one-hot features
ml.SequentialBlock(
ml.Filter(cat_schema),
ml.AsDenseFeatures(max_seq_length=6),
ml.HashedCrossAll(
cat_schema,
num_bins=100,
max_level=2,
output_mode="multi_hot",
sparse=True,
ignore_combinations=ignore_combinations,
),
),
]
batch = ml.sample_batch(ml_dataset, batch_size=100, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/models/test_ranking.py:314: AttributeError
------------------------------ Captured log call -------------------------------
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_age_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_gender_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_occupation_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_zipcode_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_userId_rating, assuming [0, 1]
WARNING root:synthetic.py:333 Couldn't find the float-domain for feature TE_movieId_rating, assuming [0, 1]
____________________ test_matrix_factorization_model_l2_reg ____________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f8986ce2490>
def test_matrix_factorization_model_l2_reg(testing_data: Dataset):
model = mm.MatrixFactorizationModel(testing_data.schema, dim=4, embeddings_l2_reg=0.1)
_ = model(mm.sample_batch(testing_data, batch_size=100, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/models/test_retrieval.py:36: AttributeError
_________________________ test_two_tower_model_l2_reg __________________________
testing_data = <merlin.io.dataset.Dataset object at 0x7f897e433fd0>
def test_two_tower_model_l2_reg(testing_data: Dataset):
model = mm.TwoTowerModel(
testing_data.schema,
query_tower=mm.MLPBlock([2]),
embedding_options=mm.EmbeddingOptions(
embedding_dim_default=2,
embeddings_l2_reg=0.1,
),
)
_ = model(mm.sample_batch(testing_data, batch_size=10, include_targets=False))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/models/test_retrieval.py:99: AttributeError
__________________ test_model_with_multiple_tasks[None-True] ___________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f895f4e8d30>
task_blocks = None, run_eagerly = True
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
__________________ test_model_with_multiple_tasks[None-False] __________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f896549fe20>
task_blocks = None, run_eagerly = False
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
______________ test_model_with_multiple_tasks[task_blocks1-True] _______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f896e5caa30>
task_blocks = MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(32, activation=relu, use_bias=True)
)
)
)
run_eagerly = True
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
______________ test_model_with_multiple_tasks[task_blocks1-False] ______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f896f686bb0>
task_blocks = MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(32, activation=relu, use_bias=True)
)
)
)
run_eagerly = False
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
______________ test_model_with_multiple_tasks[task_blocks2-True] _______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897c76ea60>
task_blocks = {'click': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(16, activation=relu, use_bias=True)
)
...ge': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(20, activation=relu, use_bias=True)
)
)
)}
run_eagerly = True
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
______________ test_model_with_multiple_tasks[task_blocks2-False] ______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f896e5ca2e0>
task_blocks = {'click': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(16, activation=relu, use_bias=True)
)
...ge': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(20, activation=relu, use_bias=True)
)
)
)}
run_eagerly = False
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
______________ test_model_with_multiple_tasks[task_blocks3-True] _______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f898c573190>
task_blocks = {'binary_classification_task': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(16, activation=relu, ...sk': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(20, activation=relu, use_bias=True)
)
)
)}
run_eagerly = True
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
______________ test_model_with_multiple_tasks[task_blocks3-False] ______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f8965d7e1f0>
task_blocks = {'binary_classification_task': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(16, activation=relu, ...sk': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(20, activation=relu, use_bias=True)
)
)
)}
run_eagerly = False
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
______________ test_model_with_multiple_tasks[task_blocks4-True] _______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f89658b8d30>
task_blocks = {'click/binary_classification_task': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(16, activation=...sk': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(20, activation=relu, use_bias=True)
)
)
)}
run_eagerly = True
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
______________ test_model_with_multiple_tasks[task_blocks4-False] ______________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f8964cbaa90>
task_blocks = {'click/binary_classification_task': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(16, activation=...sk': MLPBlock(
(layers): List(
(0): _Dense(
(dense): Dense(20, activation=relu, use_bias=True)
)
)
)}
run_eagerly = False
@testing_utils.mark_run_eagerly_modes
@pytest.mark.parametrize(
"task_blocks",
[
None,
ml.MLPBlock([32]),
dict(click=ml.MLPBlock([16]), play_percentage=ml.MLPBlock([20])),
dict(binary_classification_task=ml.MLPBlock([16]), regression_task=ml.MLPBlock([20])),
{
"click/binary_classification_task": ml.MLPBlock([16]),
"play_percentage/regression_task": ml.MLPBlock([20]),
},
],
)
def test_model_with_multiple_tasks(music_streaming_data: Dataset, task_blocks, run_eagerly: bool):
music_streaming_data.schema = music_streaming_data.schema.without("like")
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema, task_blocks=task_blocks)
model = ml.Model(inputs, ml.MLPBlock([64]), prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:34: AttributeError
_____________________________ test_mmoe_head[True] _____________________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f896caa3b20>
run_eagerly = True
@testing_utils.mark_run_eagerly_modes
def test_mmoe_head(music_streaming_data: Dataset, run_eagerly: bool):
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema)
mmoe = ml.MMOEBlock(prediction_tasks, expert_block=ml.MLPBlock([64]), num_experts=4)
model = ml.Model(inputs, ml.MLPBlock([64]), mmoe, prediction_tasks)
loss_weights = {
"click/binary_classification_task": 1.0,
"like/binary_classification_task": 2.0,
"play_percentage/regression_task": 3.0,
}
model.compile(optimizer="adam", run_eagerly=run_eagerly, loss_weights=loss_weights)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:69: AttributeError
____________________________ test_mmoe_head[False] _____________________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f89673b58b0>
run_eagerly = False
@testing_utils.mark_run_eagerly_modes
def test_mmoe_head(music_streaming_data: Dataset, run_eagerly: bool):
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema)
mmoe = ml.MMOEBlock(prediction_tasks, expert_block=ml.MLPBlock([64]), num_experts=4)
model = ml.Model(inputs, ml.MLPBlock([64]), mmoe, prediction_tasks)
loss_weights = {
"click/binary_classification_task": 1.0,
"like/binary_classification_task": 2.0,
"play_percentage/regression_task": 3.0,
}
model.compile(optimizer="adam", run_eagerly=run_eagerly, loss_weights=loss_weights)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:69: AttributeError
____ test_mmoe_head_task_specific_sample_weight_and_weighted_metrics[True] _____
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f89b5350460>
run_eagerly = True
@testing_utils.mark_run_eagerly_modes
def test_mmoe_head_task_specific_sample_weight_and_weighted_metrics(
music_streaming_data: Dataset, run_eagerly: bool
):
class CustomSampleWeight(Block):
def call_outputs(
self,
outputs: PredictionOutput,
features: Dict[str, tf.Tensor] = None,
targets: Dict[str, tf.Tensor] = None,
training=True,
testing=False,
**kwargs,
) -> PredictionOutput:
# Computes loss for the like loss only for clicked items
outputs = outputs.copy_with_updates(sample_weight=targets["click"])
return outputs
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(
music_streaming_data.schema, task_pre_dict={"like": CustomSampleWeight()}
)
mmoe = ml.MMOEBlock(prediction_tasks, expert_block=ml.MLPBlock([64]), num_experts=4)
model = ml.Model(inputs, ml.MLPBlock([64]), mmoe, prediction_tasks)
loss_weights = {
"click/binary_classification_task": 1.0,
"like/binary_classification_task": 2.0,
"play_percentage/regression_task": 3.0,
}
weighted_metrics = {
"click/binary_classification_task": (
tf.keras.metrics.Precision(name="weighted_precision"),
tf.keras.metrics.Recall(name="weighted_recall"),
tf.keras.metrics.BinaryAccuracy(name="weighted_binary_accuracy"),
tf.keras.metrics.AUC(name="weighted_auc"),
),
"like/binary_classification_task": (
tf.keras.metrics.Precision(name="weighted_precision"),
tf.keras.metrics.Recall(name="weighted_recall"),
tf.keras.metrics.BinaryAccuracy(name="weighted_binary_accuracy"),
tf.keras.metrics.AUC(name="weighted_auc"),
),
"play_percentage/regression_task": (
tf.keras.metrics.RootMeanSquaredError(name="weighted_root_mean_squared_error"),
),
}
model.compile(
optimizer="adam",
run_eagerly=run_eagerly,
loss_weights=loss_weights,
weighted_metrics=weighted_metrics,
)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:148: AttributeError
____ test_mmoe_head_task_specific_sample_weight_and_weighted_metrics[False] ____
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f89659a8340>
run_eagerly = False
@testing_utils.mark_run_eagerly_modes
def test_mmoe_head_task_specific_sample_weight_and_weighted_metrics(
music_streaming_data: Dataset, run_eagerly: bool
):
class CustomSampleWeight(Block):
def call_outputs(
self,
outputs: PredictionOutput,
features: Dict[str, tf.Tensor] = None,
targets: Dict[str, tf.Tensor] = None,
training=True,
testing=False,
**kwargs,
) -> PredictionOutput:
# Computes loss for the like loss only for clicked items
outputs = outputs.copy_with_updates(sample_weight=targets["click"])
return outputs
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(
music_streaming_data.schema, task_pre_dict={"like": CustomSampleWeight()}
)
mmoe = ml.MMOEBlock(prediction_tasks, expert_block=ml.MLPBlock([64]), num_experts=4)
model = ml.Model(inputs, ml.MLPBlock([64]), mmoe, prediction_tasks)
loss_weights = {
"click/binary_classification_task": 1.0,
"like/binary_classification_task": 2.0,
"play_percentage/regression_task": 3.0,
}
weighted_metrics = {
"click/binary_classification_task": (
tf.keras.metrics.Precision(name="weighted_precision"),
tf.keras.metrics.Recall(name="weighted_recall"),
tf.keras.metrics.BinaryAccuracy(name="weighted_binary_accuracy"),
tf.keras.metrics.AUC(name="weighted_auc"),
),
"like/binary_classification_task": (
tf.keras.metrics.Precision(name="weighted_precision"),
tf.keras.metrics.Recall(name="weighted_recall"),
tf.keras.metrics.BinaryAccuracy(name="weighted_binary_accuracy"),
tf.keras.metrics.AUC(name="weighted_auc"),
),
"play_percentage/regression_task": (
tf.keras.metrics.RootMeanSquaredError(name="weighted_root_mean_squared_error"),
),
}
model.compile(
optimizer="adam",
run_eagerly=run_eagerly,
loss_weights=loss_weights,
weighted_metrics=weighted_metrics,
)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:148: AttributeError
_____________________________ test_ple_head[True] ______________________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f897c834a60>
run_eagerly = True
@testing_utils.mark_run_eagerly_modes
def test_ple_head(music_streaming_data: Dataset, run_eagerly: bool):
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema)
cgc = ml.CGCBlock(
prediction_tasks, expert_block=ml.MLPBlock([64]), num_task_experts=2, num_shared_experts=2
)
model = ml.Model(inputs, ml.MLPBlock([64]), cgc, prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:190: AttributeError
_____________________________ test_ple_head[False] _____________________________
music_streaming_data = <merlin.io.dataset.Dataset object at 0x7f896d1db5b0>
run_eagerly = False
@testing_utils.mark_run_eagerly_modes
def test_ple_head(music_streaming_data: Dataset, run_eagerly: bool):
inputs = ml.InputBlock(music_streaming_data.schema)
prediction_tasks = ml.PredictionTasks(music_streaming_data.schema)
cgc = ml.CGCBlock(
prediction_tasks, expert_block=ml.MLPBlock([64]), num_task_experts=2, num_shared_experts=2
)
model = ml.Model(inputs, ml.MLPBlock([64]), cgc, prediction_tasks)
model.compile(optimizer="adam", run_eagerly=run_eagerly)
metrics = model.train_step(ml.sample_batch(music_streaming_data, batch_size=50))
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/prediction_tasks/test_multi_task.py:190: AttributeError
______________________________ test_logits_scaler ______________________________
ecommerce_data = <merlin.io.dataset.Dataset object at 0x7f896ee98970>
def test_logits_scaler(ecommerce_data: Dataset):
import numpy as np
from tensorflow.keras.utils import set_random_seed
set_random_seed(42)
logits_temperature = 0.5
model_1 = mm.Model(
mm.InputBlock(ecommerce_data.schema),
mm.MLPBlock([8]),
_BinaryPrediction("click", logits_temperature=logits_temperature),
)
inputs = mm.sample_batch(ecommerce_data, batch_size=10, include_targets=False)
E AttributeError: module 'merlin.models.tf' has no attribute 'sample_batch'
tests/unit/tf/predictions/test_base.py:54: AttributeError
=============================== warnings summary ===============================
../../../../../usr/lib/python3/dist-packages/requests/init.py:89
/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 6 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 9 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_index.py: 6 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 13 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/data_augmentation/test_noise.py: 1 warning
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 34 warnings
tests/unit/tf/models/test_retrieval.py: 60 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings
tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 9 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 3 warnings
tests/unit/xgb/test_xgboost.py: 18 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 5 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 9 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_index.py: 3 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 10 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 32 warnings
tests/unit/tf/models/test_retrieval.py: 32 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 7 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 2 warnings
tests/unit/xgb/test_xgboost.py: 17 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]
tests/unit/tf/test_dataset.py::test_tf_catname_ordering
tests/unit/tf/test_dataset.py::test_tf_map
/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.
warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_prediction.py: 1 warning
tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings
tests/unit/tf/inputs/test_continuous.py: 2 warnings
tests/unit/tf/inputs/test_embedding.py: 9 warnings
tests/unit/tf/inputs/test_tabular.py: 8 warnings
tests/unit/tf/models/test_ranking.py: 16 warnings
tests/unit/tf/models/test_retrieval.py: 4 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/xgb/test_xgboost.py: 12 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:807: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack
embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings
tests/unit/tf/models/test_retrieval.py: 54 warnings
tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings
tests/unit/tf/predictions/test_classification.py: 12 warnings
tests/unit/tf/predictions/test_dot_product.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 2 warnings
/tmp/autograph_generated_filei23twww6.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]
tests/unit/tf/models/test_base.py::test_model_pre_post[True]
tests/unit/tf/models/test_base.py::test_model_pre_post[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.
return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]
tests/unit/tf/models/test_base.py::test_freeze_sequential_block
tests/unit/tf/models/test_base.py::test_freeze_unfreeze
tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.
super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:980: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block
/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)
return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]
tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple
tests/unit/xgb/test_xgboost.py::TestEvals::test_default
tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid
tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data
/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].
warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective
/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first
self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.
==== 110 failed, 557 passed, 11 skipped, 1005 warnings in 856.24s (0:14:16) ====
Build step 'Execute shell' marked build as failure
Performing Post build task...
Match found for : : True
Logical operation result is TRUE
Running script : #!/bin/bash
cd /var/jenkins_home/
CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"
[merlin_models] $ /bin/bash /tmp/jenkins9174653993458499703.sh
Click to view CI Results
GitHub pull request #709 of commit 7a5d70ba766a394d2d3cd7df03b03d1e183eb444, no merge conflicts.
Running as SYSTEM
Setting status of 7a5d70ba766a394d2d3cd7df03b03d1e183eb444 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1121/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
> git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
> git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
> git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
> git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/709/*:refs/remotes/origin/pr/709/* # timeout=10
> git rev-parse 7a5d70ba766a394d2d3cd7df03b03d1e183eb444^{commit} # timeout=10
Checking out Revision 7a5d70ba766a394d2d3cd7df03b03d1e183eb444 (detached)
> git config core.sparsecheckout # timeout=10
> git checkout -f 7a5d70ba766a394d2d3cd7df03b03d1e183eb444 # timeout=10
Commit message: "Add sample_batch import to tf/__init__"
> git rev-list --no-walk 5183a24aeae9a6a4dcafb09441f0b94fca78fbe0 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins13441880748224719458.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 678 items
tests/unit/config/test_schema.py .... [ 0%]
tests/unit/datasets/test_advertising.py .s [ 0%]
tests/unit/datasets/test_ecommerce.py ..sss [ 1%]
tests/unit/datasets/test_entertainment.py ....sss. [ 2%]
tests/unit/datasets/test_social.py . [ 2%]
tests/unit/datasets/test_synthetic.py ...... [ 3%]
tests/unit/implicit/test_implicit.py . [ 3%]
tests/unit/lightfm/test_lightfm.py . [ 4%]
tests/unit/tf/test_core.py ...... [ 5%]
tests/unit/tf/test_dataset.py ................ [ 7%]
tests/unit/tf/test_public_api.py . [ 7%]
tests/unit/tf/blocks/test_cross.py ........... [ 9%]
tests/unit/tf/blocks/test_dlrm.py .......... [ 10%]
tests/unit/tf/blocks/test_interactions.py . [ 10%]
tests/unit/tf/blocks/test_mlp.py ................................. [ 15%]
tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]
..................... [ 23%]
tests/unit/tf/blocks/retrieval/test_base.py . [ 23%]
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py .. [ 24%]
tests/unit/tf/blocks/retrieval/test_two_tower.py ........... [ 25%]
tests/unit/tf/blocks/sampling/test_cross_batch.py . [ 25%]
tests/unit/tf/blocks/sampling/test_in_batch.py . [ 25%]
tests/unit/tf/core/test_aggregation.py ......... [ 27%]
tests/unit/tf/core/test_base.py .. [ 27%]
tests/unit/tf/core/test_combinators.py s................... [ 30%]
tests/unit/tf/core/test_index.py ... [ 30%]
tests/unit/tf/core/test_prediction.py .. [ 31%]
tests/unit/tf/core/test_tabular.py .... [ 31%]
tests/unit/tf/core/test_transformations.py s............................ [ 36%]
.................. [ 38%]
tests/unit/tf/data_augmentation/test_misc.py . [ 38%]
tests/unit/tf/data_augmentation/test_negative_sampling.py .......... [ 40%]
tests/unit/tf/data_augmentation/test_noise.py ..... [ 41%]
tests/unit/tf/examples/test_01_getting_started.py . [ 41%]
tests/unit/tf/examples/test_02_dataschema.py . [ 41%]
tests/unit/tf/examples/test_03_exploring_different_models.py . [ 41%]
tests/unit/tf/examples/test_04_export_ranking_models.py . [ 41%]
tests/unit/tf/examples/test_05_export_retrieval_model.py . [ 41%]
tests/unit/tf/examples/test_06_advanced_own_architecture.py . [ 42%]
tests/unit/tf/examples/test_07_train_traditional_models.py . [ 42%]
tests/unit/tf/examples/test_usecase_ecommerce_session_based.py F [ 42%]
tests/unit/tf/examples/test_usecase_pretrained_embeddings.py . [ 42%]
tests/unit/tf/inputs/test_continuous.py ..... [ 43%]
tests/unit/tf/inputs/test_embedding.py ................................. [ 48%]
.. [ 48%]
tests/unit/tf/inputs/test_tabular.py .................. [ 51%]
tests/unit/tf/layers/test_queue.py .............. [ 53%]
tests/unit/tf/losses/test_losses.py ....................... [ 56%]
tests/unit/tf/metrics/test_metrics_popularity.py ..... [ 57%]
tests/unit/tf/metrics/test_metrics_topk.py ....................... [ 60%]
tests/unit/tf/models/test_base.py s................ [ 63%]
tests/unit/tf/models/test_benchmark.py .. [ 63%]
tests/unit/tf/models/test_ranking.py .............................. [ 67%]
tests/unit/tf/models/test_retrieval.py ................................ [ 72%]
tests/unit/tf/prediction_tasks/test_classification.py .. [ 72%]
tests/unit/tf/prediction_tasks/test_multi_task.py ................ [ 75%]
tests/unit/tf/prediction_tasks/test_next_item.py ..... [ 75%]
tests/unit/tf/prediction_tasks/test_regression.py .. [ 76%]
tests/unit/tf/prediction_tasks/test_retrieval.py . [ 76%]
tests/unit/tf/prediction_tasks/test_sampling.py ...... [ 77%]
tests/unit/tf/predictions/test_base.py ..... [ 78%]
tests/unit/tf/predictions/test_classification.py ....... [ 79%]
tests/unit/tf/predictions/test_dot_product.py ........ [ 80%]
tests/unit/tf/predictions/test_regression.py .. [ 80%]
tests/unit/tf/predictions/test_sampling.py .... [ 81%]
tests/unit/tf/utils/test_batch.py .... [ 81%]
tests/unit/tf/utils/test_tf_utils.py ..... [ 82%]
tests/unit/torch/test_dataset.py ......... [ 83%]
tests/unit/torch/test_public_api.py . [ 83%]
tests/unit/torch/block/test_base.py .... [ 84%]
tests/unit/torch/block/test_mlp.py . [ 84%]
tests/unit/torch/features/test_continuous.py .. [ 84%]
tests/unit/torch/features/test_embedding.py .............. [ 87%]
tests/unit/torch/features/test_tabular.py .... [ 87%]
tests/unit/torch/model/test_head.py ............ [ 89%]
tests/unit/torch/model/test_model.py .. [ 89%]
tests/unit/torch/tabular/test_aggregation.py ........ [ 90%]
tests/unit/torch/tabular/test_tabular.py ... [ 91%]
tests/unit/torch/tabular/test_transformations.py ....... [ 92%]
tests/unit/utils/test_schema_utils.py ................................ [ 97%]
tests/unit/xgb/test_xgboost.py .................... [100%]
=================================== FAILURES ===================================
_____________________ test_usecase_ecommerce_session_based _____________________
tb = <testbook.client.TestbookNotebookClient object at 0x7f92e4f6cbb0>
@testbook(
REPO_ROOT / "examples/usecases/ecommerce-session-based-next-item-prediction-for-fashion.ipynb",
timeout=180,
execute=False,
)
def test_usecase_ecommerce_session_based(tb):
tb.inject(
"""
import os
from unittest.mock import patch
from merlin.datasets.synthetic import generate_data
mock_train, mock_valid = generate_data(
input="dressipi2022-preprocessed",
num_rows=10000,
set_sizes=(0.8, 0.2)
)
p1 = patch(
"merlin.datasets.ecommerce.get_dressipi2022",
return_value=[mock_train, mock_valid]
)
p1.start()
os.environ["DATA_FOLDER"] = "/tmp/dressipi2022/"
os.environ["EPOCHS"] = "1"
"""
)
tb.execute()
tests/unit/tf/examples/test_usecase_ecommerce_session_based.py:31:
/usr/local/lib/python3.8/dist-packages/testbook/client.py:147: in execute
super().execute_cell(cell, index)
/usr/local/lib/python3.8/dist-packages/nbclient/util.py:85: in wrapped
return just_run(coro(*args, **kwargs))
/usr/local/lib/python3.8/dist-packages/nbclient/util.py:60: in just_run
return loop.run_until_complete(coro)
/usr/lib/python3.8/asyncio/base_events.py:616: in run_until_complete
return future.result()
/usr/local/lib/python3.8/dist-packages/nbclient/client.py:1025: in async_execute_cell
await self._check_raise_for_error(cell, cell_index, exec_reply)
self = <testbook.client.TestbookNotebookClient object at 0x7f92e4f6cbb0>
cell = {'cell_type': 'code', 'execution_count': 3, 'id': 'cb0e1975', 'metadata': {'execution': {'iopub.status.busy': '2022-09...tsTemperatureScaler,\n)\n\nfrom merlin.core.dispatch import get_lib\nimport merlin.models.tf.dataset as tf_dataloader'}
cell_index = 4
exec_reply = {'buffers': [], 'content': {'ename': 'ModuleNotFoundError', 'engine_info': {'engine_id': -1, 'engine_uuid': 'df019f44-...e, 'engine': 'df019f44-1eaa-4d2c-b74b-13109617dd8e', 'started': '2022-09-02T08:34:42.394438Z', 'status': 'error'}, ...}
async def _check_raise_for_error(
self, cell: NotebookNode, cell_index: int, exec_reply: t.Optional[t.Dict]
) -> None:
if exec_reply is None:
return None
exec_reply_content = exec_reply['content']
if exec_reply_content['status'] != 'error':
return None
cell_allows_errors = (not self.force_raise_errors) and (
self.allow_errors
or exec_reply_content.get('ename') in self.allow_error_names
or "raises-exception" in cell.metadata.get("tags", [])
)
await run_hook(
self.on_cell_error, cell=cell, cell_index=cell_index, execute_reply=exec_reply
)
if not cell_allows_errors:
raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
E nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
E ------------------
E import os
E import glob
E
E import nvtabular as nvt
E from merlin.io import Dataset
E from merlin.schema import Schema, Tags
E from nvtabular.ops import (
E AddMetadata,
E )
E
E
E import tensorflow as tf
E
E from tensorflow.keras import regularizers
E from merlin.models.tf.loader import Loader
E
E import merlin.models.tf as mm
E from merlin.models.tf import InputBlock
E from merlin.models.tf.models.base import Model
E from merlin.models.tf.core.transformations import (
E ItemsPredictionWeightTying,
E L2Norm,
E LogitsTemperatureScaler,
E )
E
E from merlin.core.dispatch import get_lib
E import merlin.models.tf.dataset as tf_dataloader
E ------------------
E
E [0;31m---------------------------------------------------------------------------[0m
E [0;31mModuleNotFoundError[0m Traceback (most recent call last)
E Input [0;32mIn [3][0m, in [0;36m<cell line: 27>[0;34m()[0m
E [1;32m 20[0m [38;5;28;01mfrom[39;00m [38;5;21;01mmerlin[39;00m[38;5;21;01m.[39;00m[38;5;21;01mmodels[39;00m[38;5;21;01m.[39;00m[38;5;21;01mtf[39;00m[38;5;21;01m.[39;00m[38;5;21;01mcore[39;00m[38;5;21;01m.[39;00m[38;5;21;01mtransformations[39;00m [38;5;28;01mimport[39;00m (
E [1;32m 21[0m ItemsPredictionWeightTying,
E [1;32m 22[0m L2Norm,
E [1;32m 23[0m LogitsTemperatureScaler,
E [1;32m 24[0m )
E [1;32m 26[0m [38;5;28;01mfrom[39;00m [38;5;21;01mmerlin[39;00m[38;5;21;01m.[39;00m[38;5;21;01mcore[39;00m[38;5;21;01m.[39;00m[38;5;21;01mdispatch[39;00m [38;5;28;01mimport[39;00m get_lib
E [0;32m---> 27[0m [38;5;28;01mimport[39;00m [38;5;21;01mmerlin[39;00m[38;5;21;01m.[39;00m[38;5;21;01mmodels[39;00m[38;5;21;01m.[39;00m[38;5;21;01mtf[39;00m[38;5;21;01m.[39;00m[38;5;21;01mdataset[39;00m [38;5;28;01mas[39;00m [38;5;21;01mtf_dataloader[39;00m
E
E [0;31mModuleNotFoundError[0m: No module named 'merlin.models.tf.dataset'
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
/usr/local/lib/python3.8/dist-packages/nbclient/client.py:919: CellExecutionError
----------------------------- Captured stderr call -----------------------------
2022-09-02 08:34:44.096924: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-09-02 08:34:45.452328: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 0
2022-09-02 08:34:45.452489: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1627 MB memory: -> device: 0, name: Tesla P100-DGXS-16GB, pci bus id: 0000:07:00.0, compute capability: 6.0
2022-09-02 08:34:45.453159: I tensorflow/core/common_runtime/gpu/gpu_process_state.cc:222] Using CUDA malloc Async allocator for GPU: 1
2022-09-02 08:34:45.453215: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 14508 MB memory: -> device: 1, name: Tesla P100-DGXS-16GB, pci bus id: 0000:08:00.0, compute capability: 6.0
Error in atexit._run_exitfuncs:
Traceback (most recent call last):
File "/usr/lib/python3.8/logging/init.py", line 2127, in shutdown
h.close()
File "/usr/local/lib/python3.8/dist-packages/absl/logging/init.py", line 934, in close
self.stream.close()
File "/usr/local/lib/python3.8/dist-packages/ipykernel/iostream.py", line 438, in close
self.watch_fd_thread.join()
AttributeError: 'OutStream' object has no attribute 'watch_fd_thread'
=============================== warnings summary ===============================
../../../../../usr/lib/python3/dist-packages/requests/init.py:89
/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 6 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_index.py: 8 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 13 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/data_augmentation/test_noise.py: 1 warning
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 34 warnings
tests/unit/tf/models/test_retrieval.py: 60 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings
tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 9 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 3 warnings
tests/unit/xgb/test_xgboost.py: 18 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 5 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_index.py: 3 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 10 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 32 warnings
tests/unit/tf/models/test_retrieval.py: 32 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 7 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 2 warnings
tests/unit/xgb/test_xgboost.py: 17 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]
tests/unit/tf/test_dataset.py::test_tf_catname_ordering
tests/unit/tf/test_dataset.py::test_tf_map
/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.
warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_prediction.py: 1 warning
tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings
tests/unit/tf/inputs/test_continuous.py: 2 warnings
tests/unit/tf/inputs/test_embedding.py: 9 warnings
tests/unit/tf/inputs/test_tabular.py: 8 warnings
tests/unit/tf/models/test_ranking.py: 16 warnings
tests/unit/tf/models/test_retrieval.py: 4 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/xgb/test_xgboost.py: 12 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:807: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack
embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings
tests/unit/tf/models/test_retrieval.py: 54 warnings
tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings
tests/unit/tf/predictions/test_classification.py: 12 warnings
tests/unit/tf/predictions/test_dot_product.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 2 warnings
/tmp/autograph_generated_fileru_rhi3d.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]
tests/unit/tf/models/test_base.py::test_model_pre_post[True]
tests/unit/tf/models/test_base.py::test_model_pre_post[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.
return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]
tests/unit/tf/models/test_base.py::test_freeze_sequential_block
tests/unit/tf/models/test_base.py::test_freeze_unfreeze
tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.
super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:980: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block
/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)
return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]
tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple
tests/unit/xgb/test_xgboost.py::TestEvals::test_default
tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid
tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data
/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].
warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective
/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first
self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.
===== 1 failed, 666 passed, 11 skipped, 1011 warnings in 922.13s (0:15:22) =====
Build step 'Execute shell' marked build as failure
Performing Post build task...
Match found for : : True
Logical operation result is TRUE
Running script : #!/bin/bash
cd /var/jenkins_home/
CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"
[merlin_models] $ /bin/bash /tmp/jenkins12372311489932649828.sh
![github-actions[bot] avatar](https://avatars.githubusercontent.com/in/15368?v=4 "Published by a pub.dev verified publisher"){kind=small}
Click to view CI Results
GitHub pull request #709 of commit 51fd864cef9fb54f3da9e08a7e49ae16951db461, no merge conflicts.
Running as SYSTEM
Setting status of 51fd864cef9fb54f3da9e08a7e49ae16951db461 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1122/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
> git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
> git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
> git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
> git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/709/*:refs/remotes/origin/pr/709/* # timeout=10
> git rev-parse 51fd864cef9fb54f3da9e08a7e49ae16951db461^{commit} # timeout=10
Checking out Revision 51fd864cef9fb54f3da9e08a7e49ae16951db461 (detached)
> git config core.sparsecheckout # timeout=10
> git checkout -f 51fd864cef9fb54f3da9e08a7e49ae16951db461 # timeout=10
Commit message: "Trying to fix failing example tests"
> git rev-list --no-walk 7a5d70ba766a394d2d3cd7df03b03d1e183eb444 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins1159272990785754169.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
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Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 678 items
tests/unit/config/test_schema.py .... [ 0%]
tests/unit/datasets/test_advertising.py .s [ 0%]
tests/unit/datasets/test_ecommerce.py ..sss [ 1%]
tests/unit/datasets/test_entertainment.py ....sss. [ 2%]
tests/unit/datasets/test_social.py . [ 2%]
tests/unit/datasets/test_synthetic.py ...... [ 3%]
tests/unit/implicit/test_implicit.py . [ 3%]
tests/unit/lightfm/test_lightfm.py . [ 4%]
tests/unit/tf/test_core.py ...... [ 5%]
tests/unit/tf/test_dataset.py ................ [ 7%]
tests/unit/tf/test_public_api.py . [ 7%]
tests/unit/tf/blocks/test_cross.py ........... [ 9%]
tests/unit/tf/blocks/test_dlrm.py .......... [ 10%]
tests/unit/tf/blocks/test_interactions.py . [ 10%]
tests/unit/tf/blocks/test_mlp.py ................................. [ 15%]
tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]
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tests/unit/tf/blocks/retrieval/test_base.py . [ 23%]
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py .. [ 24%]
tests/unit/tf/blocks/retrieval/test_two_tower.py ........... [ 25%]
tests/unit/tf/blocks/sampling/test_cross_batch.py . [ 25%]
tests/unit/tf/blocks/sampling/test_in_batch.py . [ 25%]
tests/unit/tf/core/test_aggregation.py ......... [ 27%]
tests/unit/tf/core/test_base.py .. [ 27%]
tests/unit/tf/core/test_combinators.py s................... [ 30%]
tests/unit/tf/core/test_index.py ... [ 30%]
tests/unit/tf/core/test_prediction.py .. [ 31%]
tests/unit/tf/core/test_tabular.py .... [ 31%]
tests/unit/tf/core/test_transformations.py s............................ [ 36%]
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tests/unit/tf/data_augmentation/test_misc.py . [ 38%]
tests/unit/tf/data_augmentation/test_negative_sampling.py .......... [ 40%]
tests/unit/tf/data_augmentation/test_noise.py ..... [ 41%]
tests/unit/tf/examples/test_01_getting_started.py . [ 41%]
tests/unit/tf/examples/test_02_dataschema.py . [ 41%]
tests/unit/tf/examples/test_03_exploring_different_models.py . [ 41%]
tests/unit/tf/examples/test_04_export_ranking_models.py . [ 41%]
tests/unit/tf/examples/test_05_export_retrieval_model.py . [ 41%]
tests/unit/tf/examples/test_06_advanced_own_architecture.py . [ 42%]
tests/unit/tf/examples/test_07_train_traditional_models.py . [ 42%]
tests/unit/tf/examples/test_usecase_ecommerce_session_based.py . [ 42%]
tests/unit/tf/examples/test_usecase_pretrained_embeddings.py . [ 42%]
tests/unit/tf/inputs/test_continuous.py ..... [ 43%]
tests/unit/tf/inputs/test_embedding.py ................................. [ 48%]
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tests/unit/tf/inputs/test_tabular.py .................. [ 51%]
tests/unit/tf/layers/test_queue.py .............. [ 53%]
tests/unit/tf/losses/test_losses.py ....................... [ 56%]
tests/unit/tf/metrics/test_metrics_popularity.py ..... [ 57%]
tests/unit/tf/metrics/test_metrics_topk.py ....................... [ 60%]
tests/unit/tf/models/test_base.py s................ [ 63%]
tests/unit/tf/models/test_benchmark.py .. [ 63%]
tests/unit/tf/models/test_ranking.py .............................. [ 67%]
tests/unit/tf/models/test_retrieval.py ................................ [ 72%]
tests/unit/tf/prediction_tasks/test_classification.py .. [ 72%]
tests/unit/tf/prediction_tasks/test_multi_task.py ................ [ 75%]
tests/unit/tf/prediction_tasks/test_next_item.py ..... [ 75%]
tests/unit/tf/prediction_tasks/test_regression.py .. [ 76%]
tests/unit/tf/prediction_tasks/test_retrieval.py . [ 76%]
tests/unit/tf/prediction_tasks/test_sampling.py ...... [ 77%]
tests/unit/tf/predictions/test_base.py ..... [ 78%]
tests/unit/tf/predictions/test_classification.py ....... [ 79%]
tests/unit/tf/predictions/test_dot_product.py ........ [ 80%]
tests/unit/tf/predictions/test_regression.py .. [ 80%]
tests/unit/tf/predictions/test_sampling.py .... [ 81%]
tests/unit/tf/utils/test_batch.py .... [ 81%]
tests/unit/tf/utils/test_tf_utils.py ..... [ 82%]
tests/unit/torch/test_dataset.py ......... [ 83%]
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tests/unit/torch/block/test_base.py .... [ 84%]
tests/unit/torch/block/test_mlp.py . [ 84%]
tests/unit/torch/features/test_continuous.py .. [ 84%]
tests/unit/torch/features/test_embedding.py .............. [ 87%]
tests/unit/torch/features/test_tabular.py .... [ 87%]
tests/unit/torch/model/test_head.py ............ [ 89%]
tests/unit/torch/model/test_model.py .. [ 89%]
tests/unit/torch/tabular/test_aggregation.py ........ [ 90%]
tests/unit/torch/tabular/test_tabular.py ... [ 91%]
tests/unit/torch/tabular/test_transformations.py ....... [ 92%]
tests/unit/utils/test_schema_utils.py ................................ [ 97%]
tests/unit/xgb/test_xgboost.py .................... [100%]
=============================== warnings summary ===============================
../../../../../usr/lib/python3/dist-packages/requests/init.py:89
/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 6 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_index.py: 8 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 13 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/data_augmentation/test_noise.py: 1 warning
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 34 warnings
tests/unit/tf/models/test_retrieval.py: 60 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings
tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 9 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 3 warnings
tests/unit/xgb/test_xgboost.py: 18 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 5 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_index.py: 3 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 10 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 32 warnings
tests/unit/tf/models/test_retrieval.py: 32 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 7 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 2 warnings
tests/unit/xgb/test_xgboost.py: 17 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]
tests/unit/tf/test_dataset.py::test_tf_catname_ordering
tests/unit/tf/test_dataset.py::test_tf_map
/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.
warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_prediction.py: 1 warning
tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings
tests/unit/tf/inputs/test_continuous.py: 2 warnings
tests/unit/tf/inputs/test_embedding.py: 9 warnings
tests/unit/tf/inputs/test_tabular.py: 8 warnings
tests/unit/tf/models/test_ranking.py: 16 warnings
tests/unit/tf/models/test_retrieval.py: 4 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/xgb/test_xgboost.py: 12 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:807: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack
embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings
tests/unit/tf/models/test_retrieval.py: 54 warnings
tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings
tests/unit/tf/predictions/test_classification.py: 12 warnings
tests/unit/tf/predictions/test_dot_product.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 2 warnings
/tmp/autograph_generated_file44la0dmc.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]
tests/unit/tf/models/test_base.py::test_model_pre_post[True]
tests/unit/tf/models/test_base.py::test_model_pre_post[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.
return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]
tests/unit/tf/models/test_base.py::test_freeze_sequential_block
tests/unit/tf/models/test_base.py::test_freeze_unfreeze
tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.
super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:980: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block
/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)
return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]
tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple
tests/unit/xgb/test_xgboost.py::TestEvals::test_default
tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid
tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data
/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].
warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective
/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first
self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.
========== 667 passed, 11 skipped, 1011 warnings in 959.62s (0:15:59) ==========
Performing Post build task...
Match found for : : True
Logical operation result is TRUE
Running script : #!/bin/bash
cd /var/jenkins_home/
CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"
[merlin_models] $ /bin/bash /tmp/jenkins14003877979863025848.sh
Click to view CI Results
GitHub pull request #709 of commit f1bf99be670573aec555592c106e58a7b44b3bfb, no merge conflicts.
Running as SYSTEM
Setting status of f1bf99be670573aec555592c106e58a7b44b3bfb to PENDING with url https://10.20.13.93:8080/job/merlin_models/1135/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
> git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
> git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
> git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
> git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/709/*:refs/remotes/origin/pr/709/* # timeout=10
> git rev-parse f1bf99be670573aec555592c106e58a7b44b3bfb^{commit} # timeout=10
Checking out Revision f1bf99be670573aec555592c106e58a7b44b3bfb (detached)
> git config core.sparsecheckout # timeout=10
> git checkout -f f1bf99be670573aec555592c106e58a7b44b3bfb # timeout=10
Commit message: "Merge branch 'main' into tf/loader"
> git rev-list --no-walk 4872e24413f19d79246151f07142a0dfc0a097e6 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins14980384792266204826.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
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============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 681 items
tests/unit/config/test_schema.py .... [ 0%]
tests/unit/datasets/test_advertising.py .s [ 0%]
tests/unit/datasets/test_ecommerce.py ..sss [ 1%]
tests/unit/datasets/test_entertainment.py ....sss. [ 2%]
tests/unit/datasets/test_social.py . [ 2%]
tests/unit/datasets/test_synthetic.py ...... [ 3%]
tests/unit/implicit/test_implicit.py . [ 3%]
tests/unit/lightfm/test_lightfm.py . [ 4%]
tests/unit/tf/test_core.py ...... [ 4%]
tests/unit/tf/test_dataset.py ................ [ 7%]
tests/unit/tf/test_public_api.py . [ 7%]
tests/unit/tf/blocks/test_cross.py ........... [ 9%]
tests/unit/tf/blocks/test_dlrm.py .......... [ 10%]
tests/unit/tf/blocks/test_interactions.py . [ 10%]
tests/unit/tf/blocks/test_mlp.py ................................. [ 15%]
tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]
..................... [ 23%]
tests/unit/tf/blocks/retrieval/test_base.py . [ 23%]
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py .. [ 23%]
tests/unit/tf/blocks/retrieval/test_two_tower.py ........... [ 25%]
tests/unit/tf/blocks/sampling/test_cross_batch.py . [ 25%]
tests/unit/tf/blocks/sampling/test_in_batch.py . [ 25%]
tests/unit/tf/core/test_aggregation.py ......... [ 27%]
tests/unit/tf/core/test_base.py .. [ 27%]
tests/unit/tf/core/test_combinators.py s................... [ 30%]
tests/unit/tf/core/test_index.py ... [ 30%]
tests/unit/tf/core/test_prediction.py .. [ 31%]
tests/unit/tf/core/test_tabular.py .... [ 31%]
tests/unit/tf/core/test_transformations.py s............................ [ 35%]
.................. [ 38%]
tests/unit/tf/data_augmentation/test_misc.py . [ 38%]
tests/unit/tf/data_augmentation/test_negative_sampling.py .......... [ 40%]
tests/unit/tf/data_augmentation/test_noise.py ..... [ 40%]
tests/unit/tf/examples/test_01_getting_started.py . [ 41%]
tests/unit/tf/examples/test_02_dataschema.py . [ 41%]
tests/unit/tf/examples/test_03_exploring_different_models.py . [ 41%]
tests/unit/tf/examples/test_04_export_ranking_models.py . [ 41%]
tests/unit/tf/examples/test_05_export_retrieval_model.py . [ 41%]
tests/unit/tf/examples/test_06_advanced_own_architecture.py . [ 41%]
tests/unit/tf/examples/test_07_train_traditional_models.py . [ 41%]
tests/unit/tf/examples/test_usecase_ecommerce_session_based.py . [ 42%]
tests/unit/tf/examples/test_usecase_pretrained_embeddings.py . [ 42%]
tests/unit/tf/inputs/test_continuous.py ..... [ 43%]
tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]
..... [ 48%]
tests/unit/tf/inputs/test_tabular.py .................. [ 51%]
tests/unit/tf/layers/test_queue.py .............. [ 53%]
tests/unit/tf/losses/test_losses.py ....................... [ 56%]
tests/unit/tf/metrics/test_metrics_popularity.py ..... [ 57%]
tests/unit/tf/metrics/test_metrics_topk.py ....................... [ 60%]
tests/unit/tf/models/test_base.py s................ [ 63%]
tests/unit/tf/models/test_benchmark.py .. [ 63%]
tests/unit/tf/models/test_ranking.py .............................. [ 67%]
tests/unit/tf/models/test_retrieval.py ................................ [ 72%]
tests/unit/tf/prediction_tasks/test_classification.py .. [ 72%]
tests/unit/tf/prediction_tasks/test_multi_task.py ................ [ 75%]
tests/unit/tf/prediction_tasks/test_next_item.py ..... [ 76%]
tests/unit/tf/prediction_tasks/test_regression.py .. [ 76%]
tests/unit/tf/prediction_tasks/test_retrieval.py . [ 76%]
tests/unit/tf/prediction_tasks/test_sampling.py ...... [ 77%]
tests/unit/tf/predictions/test_base.py ..... [ 78%]
tests/unit/tf/predictions/test_classification.py ....... [ 79%]
tests/unit/tf/predictions/test_dot_product.py ........ [ 80%]
tests/unit/tf/predictions/test_regression.py .. [ 80%]
tests/unit/tf/predictions/test_sampling.py .... [ 81%]
tests/unit/tf/utils/test_batch.py .... [ 81%]
tests/unit/tf/utils/test_tf_utils.py ..... [ 82%]
tests/unit/torch/test_dataset.py ......... [ 83%]
tests/unit/torch/test_public_api.py . [ 83%]
tests/unit/torch/block/test_base.py .... [ 84%]
tests/unit/torch/block/test_mlp.py . [ 84%]
tests/unit/torch/features/test_continuous.py .. [ 85%]
tests/unit/torch/features/test_embedding.py .............. [ 87%]
tests/unit/torch/features/test_tabular.py .... [ 87%]
tests/unit/torch/model/test_head.py ............ [ 89%]
tests/unit/torch/model/test_model.py .. [ 89%]
tests/unit/torch/tabular/test_aggregation.py ........ [ 90%]
tests/unit/torch/tabular/test_tabular.py ... [ 91%]
tests/unit/torch/tabular/test_transformations.py ....... [ 92%]
tests/unit/utils/test_schema_utils.py ................................ [ 97%]
tests/unit/xgb/test_xgboost.py .................... [100%]
=============================== warnings summary ===============================
../../../../../usr/lib/python3/dist-packages/requests/init.py:89
/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 6 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_index.py: 8 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 13 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/data_augmentation/test_noise.py: 1 warning
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 34 warnings
tests/unit/tf/models/test_retrieval.py: 60 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings
tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 9 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 3 warnings
tests/unit/xgb/test_xgboost.py: 18 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 5 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_index.py: 3 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 10 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 32 warnings
tests/unit/tf/models/test_retrieval.py: 32 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 2 warnings
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 7 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 2 warnings
tests/unit/xgb/test_xgboost.py: 17 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]
tests/unit/tf/test_dataset.py::test_tf_catname_ordering
tests/unit/tf/test_dataset.py::test_tf_map
/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.
warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_prediction.py: 1 warning
tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings
tests/unit/tf/inputs/test_continuous.py: 2 warnings
tests/unit/tf/inputs/test_embedding.py: 9 warnings
tests/unit/tf/inputs/test_tabular.py: 8 warnings
tests/unit/tf/models/test_ranking.py: 16 warnings
tests/unit/tf/models/test_retrieval.py: 4 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/xgb/test_xgboost.py: 12 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack
embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings
tests/unit/tf/models/test_retrieval.py: 54 warnings
tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings
tests/unit/tf/predictions/test_classification.py: 12 warnings
tests/unit/tf/predictions/test_dot_product.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 2 warnings
/tmp/autograph_generated_file9ktr1qlm.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]
tests/unit/tf/models/test_base.py::test_model_pre_post[True]
tests/unit/tf/models/test_base.py::test_model_pre_post[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.
return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]
tests/unit/tf/models/test_base.py::test_freeze_sequential_block
tests/unit/tf/models/test_base.py::test_freeze_unfreeze
tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.
super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:980: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block
/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)
return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]
tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple
tests/unit/xgb/test_xgboost.py::TestEvals::test_default
tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid
tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data
/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].
warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective
/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first
self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.
========== 670 passed, 11 skipped, 1011 warnings in 973.15s (0:16:13) ==========
Performing Post build task...
Match found for : : True
Logical operation result is TRUE
Running script : #!/bin/bash
cd /var/jenkins_home/
CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"
[merlin_models] $ /bin/bash /tmp/jenkins14565781351065718792.sh
This seems like a very trivial PR or am I missing something @benfred @jperez999? This PR is blocking a bunch of other PRs we’d like to get in for the release, so a review would be very much appreciated!
Click to view CI Results
GitHub pull request #709 of commit ba5c54b424b4b1fa849e8d876affb31106acf49d, no merge conflicts.
Running as SYSTEM
Setting status of ba5c54b424b4b1fa849e8d876affb31106acf49d to PENDING with url https://10.20.13.93:8080/job/merlin_models/1215/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
> git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
> git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
> git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
> git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/709/*:refs/remotes/origin/pr/709/* # timeout=10
> git rev-parse ba5c54b424b4b1fa849e8d876affb31106acf49d^{commit} # timeout=10
Checking out Revision ba5c54b424b4b1fa849e8d876affb31106acf49d (detached)
> git config core.sparsecheckout # timeout=10
> git checkout -f ba5c54b424b4b1fa849e8d876affb31106acf49d # timeout=10
Commit message: "Merge branch 'main' into tf/loader"
> git rev-list --no-walk a3e1a0a1faedc40331e635cfe26b02d5cb69546f # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins15547996113276016848.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
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Requirement already satisfied: pkgutil-resolve-name>=1.3.10; python_version =2.6->nbformat>=5.0.4->testbook) (1.3.10)
Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (0.18.1)
Requirement already satisfied: entrypoints in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (0.4)
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Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (23.2.1)
Requirement already satisfied: tornado>=6.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: zipp>=3.1.0; python_version =1.4.0; python_version jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 685 items
tests/unit/config/test_schema.py .... [ 0%]
tests/unit/datasets/test_advertising.py .s [ 0%]
tests/unit/datasets/test_ecommerce.py ..sss [ 1%]
tests/unit/datasets/test_entertainment.py ....sss. [ 2%]
tests/unit/datasets/test_social.py . [ 2%]
tests/unit/datasets/test_synthetic.py ...... [ 3%]
tests/unit/implicit/test_implicit.py . [ 3%]
tests/unit/lightfm/test_lightfm.py . [ 4%]
tests/unit/tf/test_core.py ...... [ 4%]
tests/unit/tf/test_dataset.py ................ [ 7%]
tests/unit/tf/test_public_api.py . [ 7%]
tests/unit/tf/blocks/test_cross.py ........... [ 9%]
tests/unit/tf/blocks/test_dlrm.py .......... [ 10%]
tests/unit/tf/blocks/test_interactions.py . [ 10%]
tests/unit/tf/blocks/test_mlp.py ................................. [ 15%]
tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]
..................... [ 23%]
tests/unit/tf/blocks/retrieval/test_base.py . [ 23%]
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py .. [ 23%]
tests/unit/tf/blocks/retrieval/test_two_tower.py ........... [ 25%]
tests/unit/tf/blocks/sampling/test_cross_batch.py . [ 25%]
tests/unit/tf/blocks/sampling/test_in_batch.py . [ 25%]
tests/unit/tf/core/test_aggregation.py ......... [ 27%]
tests/unit/tf/core/test_base.py .. [ 27%]
tests/unit/tf/core/test_combinators.py s................... [ 30%]
tests/unit/tf/core/test_encoder.py . [ 30%]
tests/unit/tf/core/test_index.py ... [ 30%]
tests/unit/tf/core/test_prediction.py .. [ 31%]
tests/unit/tf/core/test_tabular.py .... [ 31%]
tests/unit/tf/core/test_transformations.py s............................ [ 35%]
.................. [ 38%]
tests/unit/tf/data_augmentation/test_misc.py . [ 38%]
tests/unit/tf/data_augmentation/test_negative_sampling.py .......... [ 40%]
tests/unit/tf/data_augmentation/test_noise.py ..... [ 40%]
tests/unit/tf/examples/test_01_getting_started.py . [ 41%]
tests/unit/tf/examples/test_02_dataschema.py . [ 41%]
tests/unit/tf/examples/test_03_exploring_different_models.py . [ 41%]
tests/unit/tf/examples/test_04_export_ranking_models.py . [ 41%]
tests/unit/tf/examples/test_05_export_retrieval_model.py . [ 41%]
tests/unit/tf/examples/test_06_advanced_own_architecture.py . [ 41%]
tests/unit/tf/examples/test_07_train_traditional_models.py . [ 41%]
tests/unit/tf/examples/test_usecase_ecommerce_session_based.py . [ 42%]
tests/unit/tf/examples/test_usecase_pretrained_embeddings.py . [ 42%]
tests/unit/tf/inputs/test_continuous.py ..... [ 42%]
tests/unit/tf/inputs/test_embedding.py ................................. [ 47%]
..... [ 48%]
tests/unit/tf/inputs/test_tabular.py .................. [ 51%]
tests/unit/tf/layers/test_queue.py .............. [ 53%]
tests/unit/tf/losses/test_losses.py ....................... [ 56%]
tests/unit/tf/metrics/test_metrics_popularity.py ..... [ 57%]
tests/unit/tf/metrics/test_metrics_topk.py ....................... [ 60%]
tests/unit/tf/models/test_base.py s................ [ 63%]
tests/unit/tf/models/test_benchmark.py .. [ 63%]
tests/unit/tf/models/test_ranking.py .............................. [ 67%]
tests/unit/tf/models/test_retrieval.py ................................ [ 72%]
tests/unit/tf/prediction_tasks/test_classification.py .. [ 72%]
tests/unit/tf/prediction_tasks/test_multi_task.py ................ [ 75%]
tests/unit/tf/prediction_tasks/test_next_item.py ..... [ 75%]
tests/unit/tf/prediction_tasks/test_regression.py ..... [ 76%]
tests/unit/tf/prediction_tasks/test_retrieval.py . [ 76%]
tests/unit/tf/prediction_tasks/test_sampling.py ...... [ 77%]
tests/unit/tf/predictions/test_base.py ..... [ 78%]
tests/unit/tf/predictions/test_classification.py ....... [ 79%]
tests/unit/tf/predictions/test_dot_product.py ........ [ 80%]
tests/unit/tf/predictions/test_regression.py .. [ 80%]
tests/unit/tf/predictions/test_sampling.py .... [ 81%]
tests/unit/tf/utils/test_batch.py .... [ 81%]
tests/unit/tf/utils/test_tf_utils.py ..... [ 82%]
tests/unit/torch/test_dataset.py ......... [ 83%]
tests/unit/torch/test_public_api.py . [ 84%]
tests/unit/torch/block/test_base.py .... [ 84%]
tests/unit/torch/block/test_mlp.py . [ 84%]
tests/unit/torch/features/test_continuous.py .. [ 85%]
tests/unit/torch/features/test_embedding.py .............. [ 87%]
tests/unit/torch/features/test_tabular.py .... [ 87%]
tests/unit/torch/model/test_head.py ............ [ 89%]
tests/unit/torch/model/test_model.py .. [ 89%]
tests/unit/torch/tabular/test_aggregation.py ........ [ 90%]
tests/unit/torch/tabular/test_tabular.py ... [ 91%]
tests/unit/torch/tabular/test_transformations.py ....... [ 92%]
tests/unit/utils/test_schema_utils.py ................................ [ 97%]
tests/unit/xgb/test_xgboost.py .................... [100%]
=============================== warnings summary ===============================
../../../../../usr/lib/python3/dist-packages/requests/init.py:89
/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 6 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_encoder.py: 2 warnings
tests/unit/tf/core/test_index.py: 8 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 13 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/data_augmentation/test_noise.py: 1 warning
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 34 warnings
tests/unit/tf/models/test_retrieval.py: 60 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings
tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 9 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 3 warnings
tests/unit/xgb/test_xgboost.py: 18 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 5 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_encoder.py: 2 warnings
tests/unit/tf/core/test_index.py: 3 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/core/test_transformations.py: 10 warnings
tests/unit/tf/data_augmentation/test_negative_sampling.py: 10 warnings
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 32 warnings
tests/unit/tf/models/test_retrieval.py: 32 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 7 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 2 warnings
tests/unit/xgb/test_xgboost.py: 17 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]
tests/unit/tf/test_dataset.py::test_tf_catname_ordering
tests/unit/tf/test_dataset.py::test_tf_map
/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.
warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_encoder.py: 1 warning
tests/unit/tf/core/test_prediction.py: 1 warning
tests/unit/tf/data_augmentation/test_negative_sampling.py: 9 warnings
tests/unit/tf/inputs/test_continuous.py: 2 warnings
tests/unit/tf/inputs/test_embedding.py: 9 warnings
tests/unit/tf/inputs/test_tabular.py: 8 warnings
tests/unit/tf/models/test_ranking.py: 16 warnings
tests/unit/tf/models/test_retrieval.py: 4 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings
tests/unit/xgb/test_xgboost.py: 12 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack
embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings
tests/unit/tf/models/test_retrieval.py: 54 warnings
tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings
tests/unit/tf/predictions/test_classification.py: 12 warnings
tests/unit/tf/predictions/test_dot_product.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 2 warnings
/tmp/autograph_generated_filejvt8wig3.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.1]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.3]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.5]
tests/unit/tf/data_augmentation/test_noise.py::test_stochastic_swap_noise[0.7]
tests/unit/tf/models/test_base.py::test_model_pre_post[True]
tests/unit/tf/models/test_base.py::test_model_pre_post[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.
return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]
tests/unit/tf/models/test_base.py::test_freeze_sequential_block
tests/unit/tf/models/test_base.py::test_freeze_unfreeze
tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.
super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/core/transformations.py:980: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block
/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)
return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]
tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple
tests/unit/xgb/test_xgboost.py::TestEvals::test_default
tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid
tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data
/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].
warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective
/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first
self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.
========== 674 passed, 11 skipped, 1025 warnings in 967.63s (0:16:07) ==========
Performing Post build task...
Match found for : : True
Logical operation result is TRUE
Running script : #!/bin/bash
cd /var/jenkins_home/
CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"
[merlin_models] $ /bin/bash /tmp/jenkins6230964465697657282.sh
Click to view CI Results
GitHub pull request #709 of commit 5de7cb1691716f1f65332d0381b3e04dd7b87c39, no merge conflicts.
Running as SYSTEM
Setting status of 5de7cb1691716f1f65332d0381b3e04dd7b87c39 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1224/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
> git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
> git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
> git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
> git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/709/*:refs/remotes/origin/pr/709/* # timeout=10
> git rev-parse 5de7cb1691716f1f65332d0381b3e04dd7b87c39^{commit} # timeout=10
Checking out Revision 5de7cb1691716f1f65332d0381b3e04dd7b87c39 (detached)
> git config core.sparsecheckout # timeout=10
> git checkout -f 5de7cb1691716f1f65332d0381b3e04dd7b87c39 # timeout=10
Commit message: "Trying to fix failing example tests"
> git rev-list --no-walk 0c80a206459c8064cdf939842b47444fb74faa9d # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins7667518232112570503.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
Requirement already satisfied: nbformat>=5.0.4 in /usr/local/lib/python3.8/dist-packages (from testbook) (5.4.0)
Requirement already satisfied: nbclient>=0.4.0 in /usr/local/lib/python3.8/dist-packages (from testbook) (0.6.6)
Requirement already satisfied: traitlets>=5.1 in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (5.3.0)
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Requirement already satisfied: entrypoints in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (0.4)
Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (2.8.2)
Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (23.2.1)
Requirement already satisfied: tornado>=6.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: zipp>=3.1.0; python_version =1.4.0; python_version jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 163 items / 47 errors
==================================== ERRORS ====================================
_________________ ERROR collecting tests/unit/tf/test_core.py __________________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/test_core.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/test_core.py:4: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
________________ ERROR collecting tests/unit/tf/test_dataset.py ________________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/test_dataset.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/test_dataset.py:26: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______________ ERROR collecting tests/unit/tf/test_public_api.py _______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/test_public_api.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/test_public_api.py:19: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_____________ ERROR collecting tests/unit/tf/blocks/test_cross.py ______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/test_cross.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/test_cross.py:20: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______________ ERROR collecting tests/unit/tf/blocks/test_dlrm.py ______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/test_dlrm.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/test_dlrm.py:19: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
__________ ERROR collecting tests/unit/tf/blocks/test_interactions.py __________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/test_interactions.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/test_interactions.py:19: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______________ ERROR collecting tests/unit/tf/blocks/test_mlp.py _______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/test_mlp.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/test_mlp.py:21: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/blocks/test_optimizer.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/test_optimizer.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/test_optimizer.py:22: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_________ ERROR collecting tests/unit/tf/blocks/retrieval/test_base.py _________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/retrieval/test_base.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/retrieval/test_base.py:18: in
from merlin.models.tf.blocks.retrieval.base import ItemRetrievalScorer
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_ ERROR collecting tests/unit/tf/blocks/retrieval/test_matrix_factorization.py _
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/retrieval/test_matrix_factorization.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py:20: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______ ERROR collecting tests/unit/tf/blocks/retrieval/test_two_tower.py _______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/retrieval/test_two_tower.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/retrieval/test_two_tower.py:22: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______ ERROR collecting tests/unit/tf/blocks/sampling/test_cross_batch.py ______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/sampling/test_cross_batch.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/sampling/test_cross_batch.py:18: in
from merlin.models.tf.blocks.sampling.cross_batch import PopularityBasedSampler
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_______ ERROR collecting tests/unit/tf/blocks/sampling/test_in_batch.py ________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/blocks/sampling/test_in_batch.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/blocks/sampling/test_in_batch.py:18: in
from merlin.models.tf.blocks.sampling.in_batch import InBatchSampler
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/core/test_aggregation.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/core/test_aggregation.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/core/test_aggregation.py:21: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_______________ ERROR collecting tests/unit/tf/core/test_base.py _______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/core/test_base.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/core/test_base.py:19: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/core/test_combinators.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/core/test_combinators.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/core/test_combinators.py:6: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_____________ ERROR collecting tests/unit/tf/core/test_encoder.py ______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/core/test_encoder.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/core/test_encoder.py:3: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______________ ERROR collecting tests/unit/tf/core/test_index.py _______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/core/test_index.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/core/test_index.py:19: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
____________ ERROR collecting tests/unit/tf/core/test_prediction.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/core/test_prediction.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/core/test_prediction.py:7: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_____________ ERROR collecting tests/unit/tf/core/test_tabular.py ______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/core/test_tabular.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/core/test_tabular.py:1: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/inputs/test_continuous.py ___________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/inputs/test_continuous.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/inputs/test_continuous.py:19: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/inputs/test_embedding.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/inputs/test_embedding.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/inputs/test_embedding.py:23: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
____________ ERROR collecting tests/unit/tf/inputs/test_tabular.py _____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/inputs/test_tabular.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/inputs/test_tabular.py:21: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_____________ ERROR collecting tests/unit/tf/layers/test_queue.py ______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/layers/test_queue.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/layers/test_queue.py:19: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_____________ ERROR collecting tests/unit/tf/losses/test_losses.py _____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/losses/test_losses.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/losses/test_losses.py:20: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______ ERROR collecting tests/unit/tf/metrics/test_metrics_popularity.py _______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/metrics/test_metrics_popularity.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/metrics/test_metrics_popularity.py:20: in
from merlin.models.tf.metrics.evaluation import ItemCoverageAt, NoveltyAt, PopularityBiasAt
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_________ ERROR collecting tests/unit/tf/metrics/test_metrics_topk.py __________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/metrics/test_metrics_topk.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/metrics/test_metrics_topk.py:23: in
from merlin.models.tf.metrics.topk import (
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______________ ERROR collecting tests/unit/tf/models/test_base.py ______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/models/test_base.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/models/test_base.py:22: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/models/test_benchmark.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/models/test_benchmark.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/models/test_benchmark.py:19: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
____________ ERROR collecting tests/unit/tf/models/test_ranking.py _____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/models/test_ranking.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/models/test_ranking.py:22: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/models/test_retrieval.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/models/test_retrieval.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/models/test_retrieval.py:6: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
____ ERROR collecting tests/unit/tf/prediction_tasks/test_classification.py ____
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/prediction_tasks/test_classification.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/prediction_tasks/test_classification.py:18: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______ ERROR collecting tests/unit/tf/prediction_tasks/test_multi_task.py ______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/prediction_tasks/test_multi_task.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/prediction_tasks/test_multi_task.py:6: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______ ERROR collecting tests/unit/tf/prediction_tasks/test_next_item.py _______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/prediction_tasks/test_next_item.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/prediction_tasks/test_next_item.py:20: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______ ERROR collecting tests/unit/tf/prediction_tasks/test_regression.py ______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/prediction_tasks/test_regression.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/prediction_tasks/test_regression.py:18: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______ ERROR collecting tests/unit/tf/prediction_tasks/test_retrieval.py _______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/prediction_tasks/test_retrieval.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/prediction_tasks/test_retrieval.py:18: in
from merlin.models.tf.blocks.sampling.in_batch import InBatchSampler
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_______ ERROR collecting tests/unit/tf/prediction_tasks/test_sampling.py _______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/prediction_tasks/test_sampling.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/prediction_tasks/test_sampling.py:20: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/predictions/test_base.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/predictions/test_base.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/predictions/test_base.py:19: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______ ERROR collecting tests/unit/tf/predictions/test_classification.py _______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/predictions/test_classification.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/predictions/test_classification.py:19: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
________ ERROR collecting tests/unit/tf/predictions/test_dot_product.py ________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/predictions/test_dot_product.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/predictions/test_dot_product.py:4: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
________ ERROR collecting tests/unit/tf/predictions/test_regression.py _________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/predictions/test_regression.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/predictions/test_regression.py:18: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_________ ERROR collecting tests/unit/tf/predictions/test_sampling.py __________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/predictions/test_sampling.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/predictions/test_sampling.py:20: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
__________ ERROR collecting tests/unit/tf/transforms/test_features.py __________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/transforms/test_features.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/transforms/test_features.py:21: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
_____ ERROR collecting tests/unit/tf/transforms/test_negative_sampling.py ______
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/transforms/test_negative_sampling.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/transforms/test_negative_sampling.py:21: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/transforms/test_noise.py ____________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/transforms/test_noise.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/transforms/test_noise.py:19: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
___________ ERROR collecting tests/unit/tf/transforms/test_tensor.py ___________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/transforms/test_tensor.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/transforms/test_tensor.py:18: in
import merlin.models.tf as mm
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
______________ ERROR collecting tests/unit/tf/utils/test_batch.py ______________
ImportError while importing test module '/var/jenkins_home/workspace/merlin_models/models/tests/unit/tf/utils/test_batch.py'.
Hint: make sure your test modules/packages have valid Python names.
Traceback:
/usr/lib/python3.8/importlib/init.py:127: in import_module
return _bootstrap._gcd_import(name[level:], package, level)
tests/unit/tf/utils/test_batch.py:4: in
import merlin.models.tf as ml
merlin/models/tf/init.py:21: in
from merlin.models.tf.core.index import IndexBlock, TopKIndexBlock
merlin/models/tf/core/index.py:26: in
from merlin.models.tf.utils.batch_utils import TFModelEncode
merlin/models/tf/utils/batch_utils.py:10: in
from merlin.models.tf.models.base import Model, RetrievalModel
merlin/models/tf/models/base.py:21: in
from merlin.models.tf.dataset import BatchedDataset
E ModuleNotFoundError: No module named 'merlin.models.tf.dataset'
=============================== warnings summary ===============================
../../../../../usr/lib/python3/dist-packages/requests/init.py:89
/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
'lanczos': pil_image.LANCZOS,
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
!!!!!!!!!!!!!!!!!!! Interrupted: 47 errors during collection !!!!!!!!!!!!!!!!!!!
======================== 7 warnings, 47 errors in 3.91s ========================
Build step 'Execute shell' marked build as failure
Performing Post build task...
Match found for : : True
Logical operation result is TRUE
Running script : #!/bin/bash
cd /var/jenkins_home/
CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"
[merlin_models] $ /bin/bash /tmp/jenkins11635702706158684292.sh
Click to view CI Results
GitHub pull request #709 of commit e9b45df9fd80e07c22653341032c93d2a026a108, no merge conflicts.
Running as SYSTEM
Setting status of e9b45df9fd80e07c22653341032c93d2a026a108 to PENDING with url https://10.20.13.93:8080/job/merlin_models/1225/console and message: 'Pending'
Using context: Jenkins
Building on master in workspace /var/jenkins_home/workspace/merlin_models
using credential nvidia-merlin-bot
> git rev-parse --is-inside-work-tree # timeout=10
Fetching changes from the remote Git repository
> git config remote.origin.url https://github.com/NVIDIA-Merlin/models/ # timeout=10
Fetching upstream changes from https://github.com/NVIDIA-Merlin/models/
> git --version # timeout=10
using GIT_ASKPASS to set credentials This is the bot credentials for our CI/CD
> git fetch --tags --force --progress -- https://github.com/NVIDIA-Merlin/models/ +refs/pull/709/*:refs/remotes/origin/pr/709/* # timeout=10
> git rev-parse e9b45df9fd80e07c22653341032c93d2a026a108^{commit} # timeout=10
Checking out Revision e9b45df9fd80e07c22653341032c93d2a026a108 (detached)
> git config core.sparsecheckout # timeout=10
> git checkout -f e9b45df9fd80e07c22653341032c93d2a026a108 # timeout=10
Commit message: "Fix wrong import in models/base.py"
> git rev-list --no-walk 5de7cb1691716f1f65332d0381b3e04dd7b87c39 # timeout=10
[merlin_models] $ /bin/bash /tmp/jenkins6902011165658361471.sh
Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
Requirement already satisfied: testbook in /usr/local/lib/python3.8/dist-packages (0.4.2)
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Requirement already satisfied: traitlets>=5.1 in /usr/local/lib/python3.8/dist-packages (from nbformat>=5.0.4->testbook) (5.3.0)
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Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.8/dist-packages (from jsonschema>=2.6->nbformat>=5.0.4->testbook) (0.18.1)
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Requirement already satisfied: pyzmq>=23.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (23.2.1)
Requirement already satisfied: tornado>=6.0 in /usr/local/lib/python3.8/dist-packages (from jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (6.2)
Requirement already satisfied: zipp>=3.1.0; python_version =1.4.0; python_version jsonschema>=2.6->nbformat>=5.0.4->testbook) (3.8.1)
Requirement already satisfied: six>=1.5 in /var/jenkins_home/.local/lib/python3.8/site-packages (from python-dateutil>=2.8.2->jupyter-client>=6.1.5->nbclient>=0.4.0->testbook) (1.15.0)
============================= test session starts ==============================
platform linux -- Python 3.8.10, pytest-7.1.2, pluggy-1.0.0
rootdir: /var/jenkins_home/workspace/merlin_models/models, configfile: pyproject.toml
plugins: anyio-3.6.1, xdist-2.5.0, forked-1.4.0, cov-3.0.0
collected 684 items
tests/unit/config/test_schema.py .... [ 0%]
tests/unit/datasets/test_advertising.py .s [ 0%]
tests/unit/datasets/test_ecommerce.py ..sss [ 1%]
tests/unit/datasets/test_entertainment.py ....sss. [ 2%]
tests/unit/datasets/test_social.py . [ 2%]
tests/unit/datasets/test_synthetic.py ...... [ 3%]
tests/unit/implicit/test_implicit.py . [ 3%]
tests/unit/lightfm/test_lightfm.py . [ 4%]
tests/unit/tf/test_core.py ...... [ 4%]
tests/unit/tf/test_dataset.py ................ [ 7%]
tests/unit/tf/test_public_api.py . [ 7%]
tests/unit/tf/blocks/test_cross.py ........... [ 9%]
tests/unit/tf/blocks/test_dlrm.py .......... [ 10%]
tests/unit/tf/blocks/test_interactions.py . [ 10%]
tests/unit/tf/blocks/test_mlp.py ................................. [ 15%]
tests/unit/tf/blocks/test_optimizer.py s................................ [ 20%]
..................... [ 23%]
tests/unit/tf/blocks/retrieval/test_base.py . [ 23%]
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py .. [ 23%]
tests/unit/tf/blocks/retrieval/test_two_tower.py ........... [ 25%]
tests/unit/tf/blocks/sampling/test_cross_batch.py . [ 25%]
tests/unit/tf/blocks/sampling/test_in_batch.py . [ 25%]
tests/unit/tf/core/test_aggregation.py ......... [ 27%]
tests/unit/tf/core/test_base.py .. [ 27%]
tests/unit/tf/core/test_combinators.py s................... [ 30%]
tests/unit/tf/core/test_encoder.py . [ 30%]
tests/unit/tf/core/test_index.py ... [ 30%]
tests/unit/tf/core/test_prediction.py .. [ 31%]
tests/unit/tf/core/test_tabular.py .... [ 31%]
tests/unit/tf/examples/test_01_getting_started.py . [ 31%]
tests/unit/tf/examples/test_02_dataschema.py . [ 32%]
tests/unit/tf/examples/test_03_exploring_different_models.py . [ 32%]
tests/unit/tf/examples/test_04_export_ranking_models.py . [ 32%]
tests/unit/tf/examples/test_05_export_retrieval_model.py . [ 32%]
tests/unit/tf/examples/test_06_advanced_own_architecture.py . [ 32%]
tests/unit/tf/examples/test_07_train_traditional_models.py . [ 32%]
tests/unit/tf/examples/test_usecase_ecommerce_session_based.py . [ 32%]
tests/unit/tf/examples/test_usecase_pretrained_embeddings.py . [ 33%]
tests/unit/tf/inputs/test_continuous.py ..... [ 33%]
tests/unit/tf/inputs/test_embedding.py ................................. [ 38%]
..... [ 39%]
tests/unit/tf/inputs/test_tabular.py .................. [ 41%]
tests/unit/tf/layers/test_queue.py .............. [ 44%]
tests/unit/tf/losses/test_losses.py ....................... [ 47%]
tests/unit/tf/metrics/test_metrics_popularity.py ..... [ 48%]
tests/unit/tf/metrics/test_metrics_topk.py ....................... [ 51%]
tests/unit/tf/models/test_base.py s................ [ 53%]
tests/unit/tf/models/test_benchmark.py .. [ 54%]
tests/unit/tf/models/test_ranking.py .............................. [ 58%]
tests/unit/tf/models/test_retrieval.py ................................ [ 63%]
tests/unit/tf/prediction_tasks/test_classification.py .. [ 63%]
tests/unit/tf/prediction_tasks/test_multi_task.py ................ [ 65%]
tests/unit/tf/prediction_tasks/test_next_item.py ..... [ 66%]
tests/unit/tf/prediction_tasks/test_regression.py ..... [ 67%]
tests/unit/tf/prediction_tasks/test_retrieval.py . [ 67%]
tests/unit/tf/prediction_tasks/test_sampling.py ...... [ 68%]
tests/unit/tf/predictions/test_base.py ..... [ 69%]
tests/unit/tf/predictions/test_classification.py ....... [ 70%]
tests/unit/tf/predictions/test_dot_product.py ........ [ 71%]
tests/unit/tf/predictions/test_regression.py .. [ 71%]
tests/unit/tf/predictions/test_sampling.py .... [ 72%]
tests/unit/tf/transforms/test_bias.py .. [ 72%]
tests/unit/tf/transforms/test_features.py s............................. [ 76%]
............. [ 78%]
tests/unit/tf/transforms/test_negative_sampling.py .......... [ 80%]
tests/unit/tf/transforms/test_noise.py ..... [ 80%]
tests/unit/tf/transforms/test_tensor.py .. [ 81%]
tests/unit/tf/utils/test_batch.py .... [ 81%]
tests/unit/tf/utils/test_tf_utils.py ..... [ 82%]
tests/unit/torch/test_dataset.py ......... [ 83%]
tests/unit/torch/test_public_api.py . [ 84%]
tests/unit/torch/block/test_base.py .... [ 84%]
tests/unit/torch/block/test_mlp.py . [ 84%]
tests/unit/torch/features/test_continuous.py .. [ 85%]
tests/unit/torch/features/test_embedding.py .............. [ 87%]
tests/unit/torch/features/test_tabular.py .... [ 87%]
tests/unit/torch/model/test_head.py ............ [ 89%]
tests/unit/torch/model/test_model.py .. [ 89%]
tests/unit/torch/tabular/test_aggregation.py ........ [ 90%]
tests/unit/torch/tabular/test_tabular.py ... [ 91%]
tests/unit/torch/tabular/test_transformations.py ....... [ 92%]
tests/unit/utils/test_schema_utils.py ................................ [ 97%]
tests/unit/xgb/test_xgboost.py .................... [100%]
=============================== warnings summary ===============================
../../../../../usr/lib/python3/dist-packages/requests/init.py:89
/usr/lib/python3/dist-packages/requests/init.py:89: RequestsDependencyWarning: urllib3 (1.26.11) or chardet (3.0.4) doesn't match a supported version!
warnings.warn("urllib3 ({}) or chardet ({}) doesn't match a supported "
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:36: DeprecationWarning: NEAREST is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.NEAREST or Dither.NONE instead.
'nearest': pil_image.NEAREST,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:37: DeprecationWarning: BILINEAR is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BILINEAR instead.
'bilinear': pil_image.BILINEAR,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:38: DeprecationWarning: BICUBIC is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BICUBIC instead.
'bicubic': pil_image.BICUBIC,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:39: DeprecationWarning: HAMMING is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.HAMMING instead.
'hamming': pil_image.HAMMING,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:40: DeprecationWarning: BOX is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.BOX instead.
'box': pil_image.BOX,
../../../../../usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41
/usr/local/lib/python3.8/dist-packages/keras/utils/image_utils.py:41: DeprecationWarning: LANCZOS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead.
'lanczos': pil_image.LANCZOS,
tests/unit/datasets/test_advertising.py: 1 warning
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 6 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_encoder.py: 2 warnings
tests/unit/tf/core/test_index.py: 8 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 34 warnings
tests/unit/tf/models/test_retrieval.py: 60 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings
tests/unit/tf/prediction_tasks/test_retrieval.py: 1 warning
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/transforms/test_bias.py: 2 warnings
tests/unit/tf/transforms/test_features.py: 10 warnings
tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings
tests/unit/tf/transforms/test_noise.py: 1 warning
tests/unit/tf/utils/test_batch.py: 9 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 3 warnings
tests/unit/xgb/test_xgboost.py: 18 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.ITEM_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.ITEM: 'item'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py: 2 warnings
tests/unit/datasets/test_entertainment.py: 4 warnings
tests/unit/datasets/test_social.py: 1 warning
tests/unit/datasets/test_synthetic.py: 5 warnings
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_core.py: 6 warnings
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/test_cross.py: 5 warnings
tests/unit/tf/blocks/test_dlrm.py: 9 warnings
tests/unit/tf/blocks/test_mlp.py: 26 warnings
tests/unit/tf/blocks/test_optimizer.py: 30 warnings
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 10 warnings
tests/unit/tf/core/test_aggregation.py: 6 warnings
tests/unit/tf/core/test_base.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_encoder.py: 2 warnings
tests/unit/tf/core/test_index.py: 3 warnings
tests/unit/tf/core/test_prediction.py: 2 warnings
tests/unit/tf/inputs/test_continuous.py: 4 warnings
tests/unit/tf/inputs/test_embedding.py: 19 warnings
tests/unit/tf/inputs/test_tabular.py: 18 warnings
tests/unit/tf/models/test_base.py: 17 warnings
tests/unit/tf/models/test_benchmark.py: 2 warnings
tests/unit/tf/models/test_ranking.py: 32 warnings
tests/unit/tf/models/test_retrieval.py: 32 warnings
tests/unit/tf/prediction_tasks/test_classification.py: 2 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 5 warnings
tests/unit/tf/predictions/test_base.py: 5 warnings
tests/unit/tf/predictions/test_classification.py: 7 warnings
tests/unit/tf/predictions/test_dot_product.py: 8 warnings
tests/unit/tf/predictions/test_regression.py: 2 warnings
tests/unit/tf/transforms/test_features.py: 10 warnings
tests/unit/tf/transforms/test_negative_sampling.py: 10 warnings
tests/unit/tf/utils/test_batch.py: 7 warnings
tests/unit/torch/block/test_base.py: 4 warnings
tests/unit/torch/block/test_mlp.py: 1 warning
tests/unit/torch/features/test_continuous.py: 1 warning
tests/unit/torch/features/test_embedding.py: 4 warnings
tests/unit/torch/features/test_tabular.py: 4 warnings
tests/unit/torch/model/test_head.py: 12 warnings
tests/unit/torch/model/test_model.py: 2 warnings
tests/unit/torch/tabular/test_aggregation.py: 6 warnings
tests/unit/torch/tabular/test_transformations.py: 2 warnings
tests/unit/xgb/test_xgboost.py: 17 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.USER_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.USER: 'user'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/datasets/test_ecommerce.py::test_synthetic_aliccp_raw_data
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-True-8]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-10]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-9]
tests/unit/tf/test_dataset.py::test_tf_drp_reset[100-False-8]
tests/unit/tf/test_dataset.py::test_tf_catname_ordering
tests/unit/tf/test_dataset.py::test_tf_map
/usr/local/lib/python3.8/dist-packages/cudf/core/frame.py:384: UserWarning: The deep parameter is ignored and is only included for pandas compatibility.
warnings.warn(
tests/unit/datasets/test_entertainment.py: 1 warning
tests/unit/implicit/test_implicit.py: 1 warning
tests/unit/lightfm/test_lightfm.py: 1 warning
tests/unit/tf/test_dataset.py: 1 warning
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py: 2 warnings
tests/unit/tf/blocks/retrieval/test_two_tower.py: 2 warnings
tests/unit/tf/core/test_combinators.py: 10 warnings
tests/unit/tf/core/test_encoder.py: 1 warning
tests/unit/tf/core/test_prediction.py: 1 warning
tests/unit/tf/inputs/test_continuous.py: 2 warnings
tests/unit/tf/inputs/test_embedding.py: 9 warnings
tests/unit/tf/inputs/test_tabular.py: 8 warnings
tests/unit/tf/models/test_ranking.py: 16 warnings
tests/unit/tf/models/test_retrieval.py: 4 warnings
tests/unit/tf/prediction_tasks/test_multi_task.py: 16 warnings
tests/unit/tf/prediction_tasks/test_regression.py: 3 warnings
tests/unit/tf/transforms/test_negative_sampling.py: 9 warnings
tests/unit/xgb/test_xgboost.py: 12 warnings
/usr/local/lib/python3.8/dist-packages/merlin/schema/tags.py:148: UserWarning: Compound tags like Tags.SESSION_ID have been deprecated and will be removed in a future version. Please use the atomic versions of these tags, like [<Tags.SESSION: 'session'>, <Tags.ID: 'id'>].
warnings.warn(
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_matrix_factorization.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/blocks/retrieval/test_two_tower.py::test_matrix_factorization_embedding_export
tests/unit/tf/inputs/test_embedding.py::test_embedding_features_exporting_and_loading_pretrained_initializer
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/inputs/embedding.py:879: DeprecationWarning: This function is deprecated in favor of cupy.from_dlpack
embeddings_cupy = cupy.fromDlpack(to_dlpack(tf.convert_to_tensor(embeddings)))
tests/unit/tf/core/test_index.py: 4 warnings
tests/unit/tf/models/test_retrieval.py: 54 warnings
tests/unit/tf/prediction_tasks/test_next_item.py: 3 warnings
tests/unit/tf/predictions/test_classification.py: 12 warnings
tests/unit/tf/predictions/test_dot_product.py: 2 warnings
tests/unit/tf/utils/test_batch.py: 2 warnings
/tmp/autograph_generated_filel5myinly.py:8: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
ag.converted_call(ag__.ld(warnings).warn, ("The 'warn' method is deprecated, use 'warning' instead", ag__.ld(DeprecationWarning), 2), None, fscope)
tests/unit/tf/models/test_base.py::test_model_pre_post[True]
tests/unit/tf/models/test_base.py::test_model_pre_post[False]
tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.1]
tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.3]
tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.5]
tests/unit/tf/transforms/test_noise.py::test_stochastic_swap_noise[0.7]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: tf.keras.backend.random_binomial is deprecated, and will be removed in a future version.Please use tf.keras.backend.random_bernoulli instead.
return dispatch_target(*args, **kwargs)
tests/unit/tf/models/test_base.py::test_freeze_parallel_block[True]
tests/unit/tf/models/test_base.py::test_freeze_sequential_block
tests/unit/tf/models/test_base.py::test_freeze_unfreeze
tests/unit/tf/models/test_base.py::test_unfreeze_all_blocks
/usr/local/lib/python3.8/dist-packages/keras/optimizers/optimizer_v2/gradient_descent.py:108: UserWarning: The lr argument is deprecated, use learning_rate instead.
super(SGD, self).init(name, **kwargs)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_categorical_one_hot[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_hashed_cross[False]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_2/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[True]
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/var/jenkins_home/workspace/merlin_models/models/merlin/models/tf/transforms/features.py:481: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_embedding_custom_inputblock[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:371: UserWarning: Please make sure input features to be categorical, detect user_age has no categorical tag
return py_builtins.overload_of(f)(*args)
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_onehot_multihot_feature_interaction[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_5/sequential_block_9/sequential_block_8/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/tf/models/test_ranking.py::test_wide_deep_model_wide_feature_interaction_multi_optimizer[False]
/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/indexed_slices.py:444: UserWarning: Converting sparse IndexedSlices(IndexedSlices(indices=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape_1:0", shape=(None,), dtype=int32), values=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Reshape:0", shape=(None, 1), dtype=float32), dense_shape=Tensor("gradient_tape/model/parallel_block_4/sequential_block_6/sequential_block_5/private__dense_3/dense_3/embedding_lookup_sparse/Cast:0", shape=(2,), dtype=int32))) to a dense Tensor of unknown shape. This may consume a large amount of memory.
warnings.warn(
tests/unit/torch/block/test_mlp.py::test_mlp_block
/var/jenkins_home/workspace/merlin_models/models/tests/unit/torch/_conftest.py:151: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:201.)
return {key: torch.tensor(value) for key, value in data.items()}
tests/unit/xgb/test_xgboost.py::test_without_dask_client
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_music_regression
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs0-DaskDeviceQuantileDMatrix]
tests/unit/xgb/test_xgboost.py::test_gpu_hist_dmatrix[fit_kwargs1-DaskDMatrix]
tests/unit/xgb/test_xgboost.py::TestEvals::test_multiple
tests/unit/xgb/test_xgboost.py::TestEvals::test_default
tests/unit/xgb/test_xgboost.py::TestEvals::test_train_and_valid
tests/unit/xgb/test_xgboost.py::TestEvals::test_invalid_data
/var/jenkins_home/workspace/merlin_models/models/merlin/models/xgb/init.py:335: UserWarning: Ignoring list columns as inputs to XGBoost model: ['item_genres', 'user_genres'].
warnings.warn(f"Ignoring list columns as inputs to XGBoost model: {list_column_names}.")
tests/unit/xgb/test_xgboost.py::TestXGBoost::test_unsupported_objective
/usr/local/lib/python3.8/dist-packages/tornado/ioloop.py:350: DeprecationWarning: make_current is deprecated; start the event loop first
self.make_current()
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=========================== short test summary info ============================
SKIPPED [1] tests/unit/datasets/test_advertising.py:20: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:62: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:78: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [1] tests/unit/datasets/test_ecommerce.py:92: ALI-CCP data is not available, pass it through env variable $DATA_PATH_ALICCP
SKIPPED [3] tests/unit/datasets/test_entertainment.py:44: No data-dir available, pass it through env variable $INPUT_DATA_DIR
SKIPPED [4] ../../../../../usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/test_util.py:2746: Not a test.
========== 673 passed, 11 skipped, 1024 warnings in 960.89s (0:16:00) ==========
Performing Post build task...
Match found for : : True
Logical operation result is TRUE
Running script : #!/bin/bash
cd /var/jenkins_home/
CUDA_VISIBLE_DEVICES=1 python test_res_push.py "https://api.GitHub.com/repos/NVIDIA-Merlin/models/issues/$ghprbPullId/comments" "/var/jenkins_home/jobs/$JOB_NAME/builds/$BUILD_NUMBER/log"
[merlin_models] $ /bin/bash /tmp/jenkins5354609797065381522.sh