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39 lines
1.8 KiB
Python
39 lines
1.8 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(test_experiment, compute_target, train_run,
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test_dataset, target_column_name, inference_folder='./forecast'):
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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('outputs/model.pkl',
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os.path.join(inference_folder, 'model.pkl'))
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inference_env = train_run.get_environment()
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config = ScriptRunConfig(source_directory=inference_folder,
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script='forecasting_script.py',
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arguments=['--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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compute_target=compute_target,
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environment=inference_env)
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run = test_experiment.submit(config,
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tags={'training_run_id':
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train_run.id,
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'run_algorithm':
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train_run.properties['run_algorithm'],
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'valid_score':
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train_run.properties['score'],
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'primary_metric':
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train_run.properties['primary_metric']})
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run.log("run_algorithm", run.tags['run_algorithm'])
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return run
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