add explicit credentials to AzureMLAssetDataset #161
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catalog already has a __contains__ method, which ensures that the dataset name (or input/output name in some cases) fits either with a known dataset, or a dataset pattern, which enables it to work with dataset factory
…alog (for dataset factories)
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| catalog.add(dataset_name, dataset, replace=True) | ||
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| for input in pipeline.all_inputs(): |
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This is where the main difference happens. Instead of looping over the catalog, we loop over the pipeline's input, and then verify within the catalog if we have that input. That way, it gives a chance for the dataset factories to be instantiated, and they are then handled as usual (call as_remote(), etc...)
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Fixing #160
Essentially, I explicitly inject the azureml's credential with after_context_created into a azureml key, then I used this key in the AzureMLAssetDataset as "credentials".