71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
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"""
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Script to train a model from a tabular dataset using a remote compute
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Based on:
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https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn
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"""
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from azure.ai.ml import Input, command
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from azure.ai.ml.constants import AssetTypes
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from compute_aml import create_or_load_aml
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from data_tabular import create_tabular_dataset, name_dataset
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from environment import custom_env_name
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from initialize_constants import AML_COMPUTE_NAME
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from ml_client import create_or_load_ml_client
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experiment_name = "mslearn-train-diabetes"
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experiment_folder = "./diabetes_training"
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script_name = "diabetes_training.py"
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registered_model_name = "diabetes_model"
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def main():
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# 1. Create or Load a ML client
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ml_client = create_or_load_ml_client()
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# 2. Create compute resources
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create_or_load_aml()
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# 3. Create and register a File Dataset
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create_tabular_dataset()
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latest_version_dataset = next(
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dataset.latest_version
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for dataset in ml_client.data.list()
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if dataset.name == name_dataset
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)
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print(list(ml_client.data.list()))
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# 4. Run Job
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job = command(
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inputs=dict(
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script_name=script_name,
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data=Input(
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type=AssetTypes.URI_FILE,
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# @latest doesn't work with dataset paths
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path=f"azureml:{name_dataset}:{latest_version_dataset}",
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),
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registered_model_name=registered_model_name,
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),
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code=experiment_folder,
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command=(
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"python ${{inputs.script_name}}"
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+ " --data ${{inputs.data}}"
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+ " --registered_model_name ${{inputs.registered_model_name}}"
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),
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environment=f"{custom_env_name}@latest",
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compute=AML_COMPUTE_NAME,
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experiment_name=experiment_name,
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display_name=experiment_name,
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)
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# submit the command
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returned_job = ml_client.jobs.create_or_update(job)
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# stream the output and wait until the job is finished
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ml_client.jobs.stream(returned_job.name)
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# refresh the latest status of the job after streaming
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returned_job = ml_client.jobs.get(name=returned_job.name)
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if __name__ == "__main__":
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main()
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