Database name: point_in_time_demo_50316e10366349078610bfb4b3bd28dd
Model name: pit_demo_model_50316e10366349078610bfb4b3bd28dd
DataFrame[]
2024/08/11 22:32:12 INFO databricks.ml_features._compute_client._compute_client: Created feature table 'hive_metastore.point_in_time_demo_50316e10366349078610bfb4b3bd28dd.temp_sensors'.
2024/08/11 22:32:29 INFO databricks.ml_features._compute_client._compute_client: Created feature table 'hive_metastore.point_in_time_demo_50316e10366349078610bfb4b3bd28dd.light_sensors'.
2024/08/11 22:32:44 INFO databricks.ml_features._compute_client._compute_client: Created feature table 'hive_metastore.point_in_time_demo_50316e10366349078610bfb4b3bd28dd.co2_sensors'.
<FeatureTable: name='point_in_time_demo_50316e10366349078610bfb4b3bd28dd.co2_sensors', table_id='8a91fbbdf3614a22aed89e17439240bb', description='Readings from CO2 sensors', primary_keys=['r', 'co2_ts'], partition_columns=[], features=['co2_ts', 'ppm', 'r'], creation_timestamp=1723415553482, online_stores=[], notebook_producers=[notebook_id: 4161531970048598
revision_id: 1723415563747
creation_timestamp: 1723415564210
creator_id: "andrea.kress@databricks.com"
notebook_workspace_id: 8498204313176882
feature_table_workspace_id: 8498204313176882
notebook_workspace_url: "https://db-sme-demo-docs.cloud.databricks.com"
producer_action: CREATE
], job_producers=[], table_data_sources=[], path_data_sources=[], custom_data_sources=[], timestamp_keys=['co2_ts'], tags={}>
2024/08/11 22:33:23 INFO mlflow.utils.autologging_utils: Created MLflow autologging run with ID '345c93ec0df845a58dfbd3a0b4a5dc1b', which will track hyperparameters, performance metrics, model artifacts, and lineage information for the current lightgbm workflow
[LightGBM] [Info] Number of positive: 7021, number of negative: 36446
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001138 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1020
[LightGBM] [Info] Number of data points in the train set: 43467, number of used features: 4
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.161525 -> initscore=-1.646926
[LightGBM] [Info] Start training from score -1.646926
2024/08/11 22:33:29 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "/databricks/python/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils."
Uploading artifacts: 0%| | 0/5 [00:00<?, ?it/s]
2024/08/11 22:33:36 WARNING mlflow.models.model: Model logged without a signature. Signatures will be required for upcoming model registry features as they validate model inputs and denote the expected schema of model outputs. Please visit https://www.mlflow.org/docs/2.9.2/models.html#set-signature-on-logged-model for instructions on setting a model signature on your logged model.
Uploading artifacts: 0%| | 0/10 [00:00<?, ?it/s]
2024/08/11 22:33:36 INFO mlflow.store.artifact.cloud_artifact_repo: The progress bar can be disabled by setting the environment variable MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR to false
Successfully registered model 'pit_demo_model_50316e10366349078610bfb4b3bd28dd'.
2024/08/11 22:33:38 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: pit_demo_model_50316e10366349078610bfb4b3bd28dd, version 1
Created version '1' of model 'pit_demo_model_50316e10366349078610bfb4b3bd28dd'.
Downloading artifacts: 0%| | 0/10 [00:00<?, ?it/s]
2024/08/11 22:33:46 INFO mlflow.store.artifact.artifact_repo: The progress bar can be disabled by setting the environment variable MLFLOW_ENABLE_ARTIFACTS_PROGRESS_BAR to false
Downloading artifacts: 0%| | 0/5 [00:00<?, ?it/s]
2024/08/11 22:33:47 WARNING mlflow.pyfunc: Calling `spark_udf()` with `env_manager="local"` does not recreate the same environment that was used during training, which may lead to errors or inaccurate predictions. We recommend specifying `env_manager="conda"`, which automatically recreates the environment that was used to train the model and performs inference in the recreated environment.
Downloading artifacts: 0%| | 0/1 [00:00<?, ?it/s]
2024/08/11 22:33:47 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function'