Metadata-Version: 2.1
Name: hi-ml
Version: 0.1.4
Summary: Microsoft Health Intelligence package to elevate and monitor scripts to an AzureML workspace
Home-page: https://github.com/microsoft/hi-ml
Author: Microsoft Research Cambridge InnerEye Team 
Author-email: innereyedev@microsoft.com
License: MIT License
Description: # Microsoft Health Intelligence Machine Learning Toolbox
        
        ## Overview
        
        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.
        
        This toolbox is still in very early stages, and presently offers only the cloud integration components. ML components
        will be added in the next few weeks.
        
        ## Getting started
        
        * Install from `pypi` via `pip`, by running `pip install hi-ml`
        
        ## Documentation
        
        The detailed package documentation, with examples and API reference, is on 
        [readthedocs](https://hi-ml.readthedocs.io/en/latest/).
        
        
        ## Quick start: Using the Azure layer
        
        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:
        ```python
        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**.
        
        With the `hi-ml` package, you can turn that script into one that runs on the cloud by adding one function call:
        
        ```python
        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(compute_cluster_name="preprocess-ds12",
                                                 input_datasets=["images123"],
                                                 # Omit this line if you don't create an output dataset (for example, in
                                                 # model training scripts)
                                                 output_datasets=["images123_resized"],
                                                 default_datastore="my_datastore")
            # 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`.
        
        That's it!
        
        For details, please refer to the [onboarding page](docs/source/first_steps.md).
         
        For more examples, please see [examples.md](docs/source/examples.md).
        
        ## Issues
        If you've found a bug in the code, please check the [issues](https://github.com/microsoft/hi-ml/issues) page. 
        If no existing issue exists, please open a new one. Be sure to include 
        - A descriptive title
        - Expected behaviour (including a code sample if possible)
        - Actual behavior
        
        
        ## Contributing
        We welcome all contributions that help us achieve our aim of speeding up ML/AI research in health and life sciences.
        Examples of contributions are
        * Data loaders for specific health & life sciences data
        * Network architectures and components for deep learning models
        * Tools to analyze and/or visualize data
        * ...
        
        Please check the [detailed page about contributions](docs/source/contributing.md).
        
        ## Licensing
        
        [MIT License](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.**
        
        ## Contact
        
        If you have any feature requests, or find issues in the code, please create an 
        [issue on GitHub](https://github.com/microsoft/hi-ml/issues).
        
        ## Contributing
        
        This project welcomes contributions and suggestions.  Most contributions require you to agree to a
        Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
        the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
        
        When you submit a pull request, a CLA bot will automatically determine whether you need to provide
        a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
        provided by the bot. You will only need to do this once across all repos using our CLA.
        
        This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
        For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
        contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
        
        ## Trademarks
        
        This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft 
        trademarks or logos is subject to and must follow 
        [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
        Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
        Any use of third-party trademarks or logos are subject to those third-party's policies.
        
Keywords: InnerEye,HealthIntelligence,AzureML
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
