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50 lines
1.4 KiB
Python
50 lines
1.4 KiB
Python
import os
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import shutil
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from azureml.core import ScriptRunConfig
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def run_remote_inference(
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test_experiment,
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compute_target,
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train_run,
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test_dataset,
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target_column_name,
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inference_folder="./forecast",
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):
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# Create local directory to copy the model.pkl and forecsting_script.py files into.
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# These files will be uploaded to and executed on the compute instance.
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os.makedirs(inference_folder, exist_ok=True)
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shutil.copy("forecasting_script.py", inference_folder)
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train_run.download_file(
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"outputs/model.pkl", os.path.join(inference_folder, "model.pkl")
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)
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inference_env = train_run.get_environment()
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config = ScriptRunConfig(
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source_directory=inference_folder,
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script="forecasting_script.py",
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arguments=[
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"--target_column_name",
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target_column_name,
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"--test_dataset",
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test_dataset.as_named_input(test_dataset.name),
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],
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compute_target=compute_target,
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environment=inference_env,
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)
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run = test_experiment.submit(
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config,
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tags={
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"training_run_id": train_run.id,
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"run_algorithm": train_run.properties["run_algorithm"],
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"valid_score": train_run.properties["score"],
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"primary_metric": train_run.properties["primary_metric"],
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},
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)
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run.log("run_algorithm", run.tags["run_algorithm"])
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return run
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