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42 lines
1.9 KiB
Python
42 lines
1.9 KiB
Python
from azureml.core import Environment
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from azureml.core.conda_dependencies import CondaDependencies
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from azureml.train.estimator import Estimator
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from azureml.core.run import Run
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def run_rolling_forecast(test_experiment, compute_target, train_run, test_dataset,
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max_horizon, target_column_name, time_column_name,
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freq='D', inference_folder='./forecast'):
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condafile = inference_folder + '/condafile.yml'
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train_run.download_file('outputs/model.pkl',
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inference_folder + '/model.pkl')
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train_run.download_file('outputs/conda_env_v_1_0_0.yml', condafile)
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inference_env = Environment("myenv")
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inference_env.docker.enabled = True
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inference_env.python.conda_dependencies = CondaDependencies(
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conda_dependencies_file_path=condafile)
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est = Estimator(source_directory=inference_folder,
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entry_script='forecasting_script.py',
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script_params={
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'--max_horizon': max_horizon,
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'--target_column_name': target_column_name,
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'--time_column_name': time_column_name,
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'--frequency': freq
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},
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inputs=[test_dataset.as_named_input('test_data')],
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compute_target=compute_target,
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environment_definition=inference_env)
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run = test_experiment.submit(est,
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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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run.log("run_algorithm", run.tags['run_algorithm'])
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return run
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