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47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
import argparse
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import os
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import azureml.core
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from datetime import datetime
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import pandas as pd
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import pytz
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from azureml.core import Dataset, Model
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from azureml.core.run import Run, _OfflineRun
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from azureml.core import Workspace
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run = Run.get_context()
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ws = None
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if type(run) == _OfflineRun:
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ws = Workspace.from_config()
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else:
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ws = run.experiment.workspace
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print("Check for new data.")
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parser = argparse.ArgumentParser("split")
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parser.add_argument("--ds_name", help="input dataset name")
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parser.add_argument("--model_name", help="name of the deployed model")
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args = parser.parse_args()
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print("Argument 1(ds_name): %s" % args.ds_name)
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print("Argument 2(model_name): %s" % args.model_name)
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# Get the latest registered model
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try:
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model = Model(ws, args.model_name)
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last_train_time = model.created_time
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print("Model was last trained on {0}.".format(last_train_time))
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except Exception:
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print("Could not get last model train time.")
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last_train_time = datetime.min.replace(tzinfo=pytz.UTC)
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train_ds = Dataset.get_by_name(ws, args.ds_name)
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dataset_changed_time = train_ds.data_changed_time
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if not dataset_changed_time > last_train_time:
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print("Cancelling run since there is no new data.")
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run.parent.cancel()
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else:
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# New data is available since the model was last trained
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print("Dataset was last updated on {0}. Retraining...".format(dataset_changed_time))
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