clearml pipeline demo missing execution queue, no default queue defined and no specific node queue defined

[mullerhai]

HI

 when I  want to run the  demo  apply by our web doc  office ,

from clearml.automation.controller import PipelineDecorator,CreateFromFunction,PipelineController
from clearml import TaskTypes


@PipelineDecorator.component(return_values=['data_frame'], cache=True, task_type=TaskTypes.data_processing)
def step_one(pickle_data_url: str, extra: int = 43):
    import sklearn  # noqa
    import pickle
    import pandas as pd
    from clearml import StorageManager
    local_iris_pkl = StorageManager.get_local_copy(remote_url=pickle_data_url)
    with open(local_iris_pkl, 'rb') as f:
        iris = pickle.load(f)
    data_frame = pd.DataFrame(iris['data'], columns=iris['feature_names'])
    data_frame.columns += ['target']
    data_frame['target'] = iris['target']
    return data_frame

@PipelineDecorator.component(
  return_values=['X_train, X_test, y_train, y_test'], cache=True, task_type=TaskTypes.data_processing)
def step_two(data_frame, test_size=0.21, random_state=421):
    import pandas as pd  # noqa
    from sklearn.model_selection import train_test_split
    y = data_frame['target']
    X = data_frame[(c for c in data_frame.columns if c != 'target')]
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=test_size, random_state=random_state)
    return X_train, X_test, y_train, y_test

@PipelineDecorator.component(return_values=['model'], cache=True, task_type=TaskTypes.training)
def step_three(X_train, y_train):
    print('step_three')
    # make sure we have pandas for this step, we need it to use the data_frame
    import pandas as pd  # noqa
    from sklearn.linear_model import LogisticRegression
    model = LogisticRegression(solver='liblinear', multi_class='auto')
    model.fit(X_train, y_train)
    return model

@PipelineDecorator.component(return_values=['accuracy'], cache=True, task_type=TaskTypes.qc)
def step_four(model, X_data, Y_data):
    from sklearn.linear_model import LogisticRegression  # noqa
    from sklearn.metrics import accuracy_score
    Y_pred = model.predict(X_data)
    return accuracy_score(Y_data, Y_pred, normalize=True)

@PipelineDecorator.pipeline(name='pipeline', project='examples', version='0.1')
def main(pickle_url, mock_parameter='mock'):
    data_frame = step_one(pickle_url)
    X_train, X_test, y_train, y_test = step_two(data_frame)
    model = step_three(X_train, y_train)
    accuracy = 100 * step_four(model, X_data=X_test, Y_data=y_test)
    print(f"Accuracy={accuracy}%")

meet the error

ValueError: Node 'step_one' missing execution queue, no default queue defined and no specific node queue defined I dont know where to define the execution queue?

[Rizwan-Hasan]

I've reproduced the issue. And It can be solved by using this method PipelineDecorator.set_default_execution_queue('default'), here's the doc link for the method's usecase. You can also check out this example pipeline_from_decorator.py

[mullerhai]

I've reproduced the issue. And It can be solved by using this method PipelineDecorator.set_default_execution_queue('default'), here's he doc link for the method's usecase. You can also check out this example pipeline_from_decorator.py

thank you，I will try again