![]() ![]() Retrieve the active Run object using Run.get_context() and then retrieve the dictionary of named inputs using input_datasets. Named inputs to your pipeline step script are available as a dictionary within the Run object. Train, test = smaller_dataset.random_split(percentage=0.8, seed=seed) ![]() Smaller_dataset = iris_dataset.take_sample(0.1, seed=seed) # 10% You can also use methods such as random_split() and take_sample() to create multiple inputs or reduce the amount of data passed to your pipeline step: seed = 42 # PRNG seed The above snippet just shows the form of the call and is not part of a Microsoft sample. You would need to replace the values for all these arguments (that is, "train_data", "train.py", cluster, and iris_dataset) with your own data. The following snippet shows the common pattern of combining these steps within the PythonScriptStep constructor:
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