Setting up the Development Environment

Development environment

We suggest using Visual Studio Code (VSCode), available for multiple platforms here. On Windows system, we recommend using WSL, the Windows Subsystem for Linux, because some PyTorch features are not available on Windows. Inside VSCode, please install the extensions that are recommended for this project - they are available in .vscode/extensions.json in the repository root.

Opening the repository

Once you have the repository on your computer, you can open either all projects at once or individual projects separately in VSCode.

  • To open all projects at once, use VSCode’s “Open Workspace from File”, and select himl-projects.code-workspace.

  • To open individual projects, use VSCode’s “Open Folder”, and select one of the folders hi-ml-azure, hi-ml, or hi-ml-cpath

Creating a Conda environment

Different projects in this repository use different Conda environments:

  • The himl Conda environment should be used for work on the hi-ml and hi-ml-azure projects.

  • The HimlHisto Conda environment should be used for work on hi-ml-cpath.

Please select the right Python interpreter for your project (or all projects if using the himl-projects workspace) inside VSCode, by choosing “Python: Select Interpreter” from the command palette (Ctrl-Shift-P on VSCode for Windows)

To create the Conda environment himl, please use either

conda env create --file hi-ml/environment.yml

or use make in the repository root folder:

make env

Please see the project-specific README files for instructions how to set up the other Conda environments.

Installing pyright

We are using static typechecking for our code via mypy and pyright. The latter requires a separate installation outside the Conda environment. For WSL, these are the required steps (see also here):

curl -o- | bash

Close your terminal and re-open it, then run:

nvm install node
npm install -g pyright

Using specific versions of hi-ml in your Python environments

If you’d like to test specific changes to the hi-ml package in your code, you can use two different routes:

  • You can clone the hi-ml repository on your machine, and use hi-ml in your Python environment via a local package install:

pip install -e <your_git_folder>/hi-ml
  • You can consume an early version of the package from via pip:

pip install --extra-index-url hi-ml==0.1.0.post165
  • If you are using Conda, you can add an additional parameter for pip into the Conda environment.yml file like this:

name: foo
  - pip=20.1.1
  - python=3.7.3
  - pip:
      - --extra-index-url
      - hi-ml==0.1.0.post165

Common things to do

The repository contains a makefile with definitions for common operations.

  • make check: Run flake8, mypy and black on the repository.

  • make test: Run flake8 and mypy on the repository, then all tests via pytest

  • make pip: Install all packages for running and testing in the current interpreter.

  • make conda: Update the hi-ml Conda environment and activate it

Building documentation

To build the sphinx documentation, you must have sphinx and related packages installed (see build_requirements.txt in the repository root). Then run:

cd docs
make html

This will build all your documentation in docs/build/html.

Setting up your AzureML workspace

  • In the browser, navigate to the AzureML workspace that you want to use for running your tests.

  • In the top right section, there will be a dropdown menu showing the name of your AzureML workspace. Expand that.

  • In the panel, there is a link “Download config file”. Click that.

  • This will download a file config.json. Move that file to both of the folders hi-ml/testhiml and hi-ml/testazure The file config.json is already present in .gitignore, and will hence not be checked in.

Creating and Deleting Docker Environments in AzureML

  • Passing a docker_base_image into submit_to_azure_if_needed causes a new image to be built and registered in your workspace (see docs for more information).

  • To remove an environment use the az ml environment delete function in the AzureML CLI (note that all the parameters need to be set, none are optional).


For all of the tests to work locally you will need to cache your AzureML credentials. One simple way to do this is to run the example in src/health/azure/examples (i.e. run python --message='Hello World' --azureml or make example) after editing to reference your compute cluster.

When running the tests locally, they can either be run against the source directly, or the source built into a package.

  • To run the tests against the source directly in the local src folder, ensure that there is no wheel in the dist folder (for example by running make clean). If a wheel is not detected, then the local src folder will be copied into the temporary test folder as part of the test process.

  • To run the tests against the source as a package, build it with make build. This will build the local src folder into a new wheel in the dist folder. This wheel will be detected and passed to AzureML as a private package as part of the test process.

Test discovery in VSCode

All tests in the repository should be picked up automatically by VSCode. In particular, this includes the tests in the hi-ml-cpath folder, which are not always necessary when working on the core hi-ml projects.

Creating a New Release

To create a new package release, follow these steps:

  • On the repository’s github page, click on “Releases”, then “Draft a new release”

  • In the “Draft a new release” page, click “Choose a tag”. In the text box, enter a (new) tag name that has the desired version number, plus a “v” prefix. For example, to create package version 0.12.17, create a tag v0.12.17. Then choose “+ Create new tag” below the text box.

  • Enter a “Release title” that highlights the main feature(s) of this new package version.

  • Click “Auto-generate release notes” to pull in the titles of the Pull Requests since the last release.

  • Before the auto-generated “What’s changed” section, add a few sentences that summarize what’s new.

  • Click “Publish release”


Debugging a test in VSCode fails on Windows

  • Symptom: Debugging just does not seem to do anything

  • Check: Debug Console shows error from _sqlite3 import *: ImportError: DLL load failed: The specified module could not be found.

  • Fix: see here

  • Run conda info --envs to see where your Conda environment lives, then place sqlite3.dll into the DLLs folder inside of the environment