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* Prepare infra * Store experiment information in MySQL & MinIO * Use CreateMlModelEntityRequest instead of MlModel for PUT operations * Update MlFlow infra * Prepare MlFlow source * Prepare Mlflow workflow * Simplify test and prepare README * Revert compose * Fix compose * Prepare warnings and fix features * Use non-default port for integration test * Use mlflow-skinny for the client application
23 lines
504 B
Python
23 lines
504 B
Python
"""
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This is a simple script for testing the environment.
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You can run it as `python experiment.py`, and a new
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experiment should appear in the remote server.
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"""
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import mlflow
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# Create experiment to the remote server
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mlflow_uri = "http://localhost:5000"
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mlflow.set_tracking_uri(mlflow_uri)
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mlflow.set_experiment("mlflow_test1")
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mlflow.log_param("param1", "value1")
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mlflow.log_param("param2", "value2")
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for i in range(10):
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mlflow.log_metric("metric1", value=1 / (i + 1), step=i)
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mlflow.end_run()
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