小墨鱼

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I am aware of the MMLSpark solution you referenced. I use the dist XGBoost(not use spark), It support input the dir[mutil files]. But I want use LGBM to train my...

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inputs_cfg = { 'username': 'Admin', 'password': 'password', 'auth_url': 'http://192.168.84.88:5000/v2.0', 'region': 'RegionOne', 'tenant_name': 'demo' } import openstack_plugin_common as common nova_client, neut_client, cind_client, keys_client = common.NovaClient().get(config=inputs_cfg), common.NeutronClient().get(config=inputs_cfg), common.CinderClient().get(config=inputs_cfg), common.KeystoneClient().get(config=inputs_cfg), ## output: common.get_resource_by_name_or_id('default',...

if has big offline data(on hdfs), how can I prepare train data use feast? Can write pyspark file, and submit spark task like below ? spark-submit \ --master yarn \...

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./build/bin/main -m ../GGUF_Models/chatglm-ggml_q4_0.bin -l 256 -p "你好" GGML_ASSERT: /Users/apple/PycharmProjects/NLPProject/chatglm.cpp/third_party/ggml/src/ggml-metal.m:1453: false

我理解的配置如下后: project: feast_spark_project registry: data/registry.db provider: local offline_store: type: spark spark_conf: spark.master: yarn spark.ui.enabled: "true" spark.eventLog.enabled: "true" spark.sql.catalogImplementation: "hive" spark.sql.parser.quotedRegexColumnNames: "true" spark.sql.session.timeZone: "UTC" 然后使用如下代码定义sparksource from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import ( SparkSource,) driver_hourly_stats=...

wontfix
kind/bug
priority/p2