health_azure Package
Functions
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Creates an AzureML run configuration, that contains information about environment, multi node execution, and Docker. |
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Creates an AzureML ScriptRunConfig object, that holds the information about the snapshot, the entry script, and its arguments. |
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For a given Azure ML run id, first retrieve the Run, and then download all files, which optionally start with a given prefix. |
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Given an Azure ML run id, download all files from a given checkpoint directory within that run, to the path specified by output_path. |
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Download file(s) from an Azure ML Datastore that are registered within a given Workspace. |
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Finds an existing run in an experiment, based on a recovery ID that contains the experiment ID and the actual RunId. |
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Gets the name of the most recently executed AzureML run, instantiates that Run object and returns it. |
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Retrieve an Azure ML Workspace from one of several places: |
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Returns True if the given run is inside of an AzureML machine, or False if it is on a machine outside AzureML. |
Sets the environment variables that PyTorch Lightning needs for multi-node training. |
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Splits a run ID into the experiment name and the actual run. |
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Starts an AzureML run on a given workspace, via the script_run_config. |
Submit a folder to Azure, if needed and run it. |
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This is a barrier to use in distributed jobs. |
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Upload a folder to an Azure ML Datastore that is registered within a given Workspace. |
Classes
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This class stores all information that a script needs to run inside and outside of AzureML. |
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Contains information to use AzureML datasets as inputs or outputs. |
health_ml.utils Package
Functions
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Logs the learning rate(s) used by the given module. |
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Write a dictionary with metrics and/or an individual metric as a name/value pair to the loggers of the given module. |
Classes
A Pytorch Lightning logger that stores metrics in the current AzureML run. |