This toolbox aims at providing low-level and high-level building blocks for Machine Learning / AI researchers and practitioners. It helps to simplify and streamline work on deep learning models for healthcare and life sciences, by providing tested components (data loaders, pre-processing), deep learning models, and cloud integration tools.

• Install from pypi via pip, by running pip install hi-ml

Use case: you have a Python script that does something - that could be training a model, or pre-processing some data. The hi-ml package can help easily run that on Azure Machine Learning (AML) services.

Here is an example script that reads images from a folder, resizes and saves them to an output folder:

from pathlib import Path
if __name__ == '__main__':
    input_folder = Path("/tmp/my_dataset")
    output_folder = Path("/tmp/my_output")
    for file in input_folder.glob("*.jpg"):
        contents = read_image(file)
        resized = contents.resize(0.5)
        write_image(output_folder / file.name)

Doing that at scale can take a long time. We'd like to run that script in AzureML, consume the data from a folder in blob storage, and write the results back to blob storage.

Pre-requisite: Download the config file from your AzureML workspace, as described here. Put this file into the folder where your script lives.

from pathlib import Path
from health.azure.himl import submit_to_azure_if_needed
if __name__ == '__main__':
    current_file = Path(__file__)
    run_info = submit_to_azure_if_needed(entry_script=current_file, 
                                          snapshot_root_directory=current_file.parent,
                                          workspace_config_path=Path("config.json"),
                                          compute_cluster_name="preprocess-ds12",
                                          conda_environment_file=Path("environment.yml"),
                                          input_datasets=["images123"],
                                          # Omit this line if you don't create an output dataset (for example, in
                                          # model training scripts)
                                          output_datasets=["images123_resized"],)
    # When running in AzureML, run_info.input_datasets and run_info.output_datasets will be populated,
    # and point to the data coming from blob storage. For runs outside AML, the paths will be None.
    # Replace the None with a meaningful path, so that we can still run the script easily outside AML.
    input_dataset = run_info.input_datasets[0] or Path("/tmp/my_dataset")
    output_dataset = run_info.output_datasets[0] or Path("/tmp/my_output")
    files_processed = []
    for file in input_dataset.glob("*.jpg"):
        contents = read_image(file)
        resized = contents.resize(0.5)
        write_image(output_dataset / file.name)
        files_processed.append(file.name)
    # Any other files that you would not consider an "output dataset", like metrics, etc, should be written to
    # a folder "./outputs". Any files written into that folder will later be visible in the AzureML UI.
    # run_info.output_folder already points to the correct folder.
    stats_file = run_info.output_folder / "processed_files.txt"
    stats_file.write_text("\n".join(files_processed))

Once these changes are in place, you can submit the script to AzureML by supplying an additional --azureml flag on the commandline, like python myscript.py --azureml

In the added code, you need to supply the following parameters:

• entry_script: The script that should be run
• snapshot_root_directory: The directory that contains all code that should be packaged and sent to AzureML. All Python code that the script uses must be copied over.
• compute_cluster_name: The name of the AzureML cluster that should run the job. This can be a cluster with CPU or GPU machines. See here for documentation
• conda_environment_file: The conda configuration file that describes which packages are necessary for your script to run.

You also need to supply an input dataset. For data pre-processing scripts, you also supply an output dataset - you can omit this for ML training scripts.

• You need to provision a data store in your AML workspace, that points to your training data in blob storage. This is described here.
• input_datasets=["images123"] in the code above means that the script will consume all data in folder images123 in blob storage as the input. The folder must exist in blob storage, in the location that you gave when creating the datastore. Once the script has run, it will also register the data in this folder as an AML dataset.
• output_datasets=["images123_resized"] means that the script will create a temporary folder when running in AML, and while the job writes data to that folder, upload it to blob storage, in the data store.

To be filled in:

• default_datastore
• Complex dataset setup.

MIT License

You are responsible for the performance, the necessary testing, and if needed any regulatory clearance for any of the models produced by this toolbox.

If you have any feature requests, or find issues in the code, please create an issue on GitHub.

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