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Contributing to Azure-Quantum-Python

If you would like to become an active contributor to this project please follow the instructions provided in Microsoft Azure Projects Contribution Guidelines.

Pre-requisites

Install pre-reqs:

pip install azure_devtools pytest pytest-azurepipelines pytest-cov

Building and testing

The Azure Quantum team uses Anaconda to create virtual environments for local unit and integration testing as well as in CI/CD.

To create a new conda environment for the azure-quantum package, run at the root of the azure-quantum directory:

conda env create -f environment.yml

Then to activate the environment:

conda activate azurequantum

In case you have created the conda environment a while ago, you can make sure you have the latest versions of all dependencies by updating your environment:

conda env update -f environment.yml --prune

Install the local development package

To install the package in development mode, run:

pip install -e .

Unit tests

To run the unit tests, run pytest from the root of the azure-quantum directory:

pytest

To run the a specific unit test class, run:

pytest ./tests/unit/test_job.py

To run the a specific unit test case, run:

pytest -k test_job_refresh

Recordings

To read more about how to create and update recordings for testing code that interacts with a live API, see the Azure Quantum Unit tests README.

Before merging your code contribution to main, make sure that all new code is covered by unit tests and that the unit tests have up-to-date recordings. If you recorded your tests and then updated or refactored the code afterwards, remember to re-record the tests.

Update/re-generate the Azure Quantum internal SDK client

The internal Azure Quantum Python SDK client (azure/quantum/_client) needs to be re-generated every time there is a change in the Azure Quantum Service API definition (aka Swagger).

Prerequisites

Python 3.8 or later is required

linux

sudo apt install python3

sudo apt install python3-pip

sudo apt install python3.{?}-venv explicitly if needed

Node.js 18.3 LTS or later is required

Setup your repo

Fork and clone the azure-sdk-for-python repo (we call it's name SDK repo and it's absolute path)

Create a branch in SDK repo to work in

Make sure your typespec definition is merged into main branch of public rest repo (we call it rest repo) or you already make a PR in rest repo so that you could get the github link of your typespec definition which contains commit id (e.g. https://github.com/Azure/azure-rest-api-specs/blob/46ca83821edd120552403d4d11cf1dd22360c0b5/specification/contosowidgetmanager/Contoso.WidgetManager/tspconfig.yaml)

Project service name and package name

Two key pieces of information for your project are the service_name and package_name.

The service_name is the short name for the Azure service. The service_name should match across all the SDK language repos and should be name of the directory in the specification folder of the azure-rest-api-specs repo that contains the REST API definition file. An example is Service Bus, whose API definitions are in the specification/servicebus folder of the azure-rest-api-specs repo, and uses the service_name "servicebus". Not every service follows this convention, but it should be the standard unless there are strong reasons to deviate.

In Python, a project's package name is the name used to publish the package in PyPI. For data plane libraries (management plane uses a different convention), the package_name could be just azure-{service_name}. An example is "azure-servicebus".

Some services may need several different packages. For these cases a third component, the module_name, is added to the package_name, as azure-{service_name}-{module_name}. The module_name usually comes from the name of the REST API file itself or one of the directories toward the end of the file path. An example is the Synapse service, with packages azure-synapse, azure-synapse-accesscontrol, azure-synapse-artifacts, etc.

Project folder structure

Before we start, we probably should get to know the project folder for SDK repo.

Normally, the folder structure would be something like:

sdk/{service_name}/{package_name}: the PROJECT_ROOT folder /azure/{service_name}/{module_name} : folder where generated code is. /tests: folder of test files /samples: folder of sample files azure-{service_name}-{module_name}: package name. Usually, package name is same with part of ${PROJECT_ROOT} folder. After release, you can find it in pypi. For example: you can find azure-messaging-webpubsubservice in pypi. there are also some other files (like setup.py, README.md, etc.) which are necessary for a complete package. More details on the structure of Azure SDK repos is available in the Azure SDK common repo.

How to generate SDK code with Dataplane Codegen

We are working on to automatically generate everything right now, but currently we still need some manual work to get a releasable package. Here're the steps of how to get the package.

  1. Configure python emitter in tspconfig.yaml In rest repo, there shall be tspconfig.yaml where main.tsp of your service is. Make sure there are configuration for Python SDK like:

parameters: "service-dir": default: "YOUR_SERVICE_DIRECTORY"

emit: [ "@azure-tools/typespec-autorest", // this value does not affect python code generation ]

options: "@azure-tools/typespec-python": package-dir: "YOUR_PACKAGE_NAME" package-name: "{package-dir}" flavor: "azure" YOUR_PACKAGE_NAME is your package name; YOUR_SERVICE_DIRECTORY is SDK directory name. For example, assume that package name is "azure-ai-anomalydetector" and you want to put it in folder "azure-sdk-for-python/sdk/anomalydetector", then "YOUR_PACKAGE_NAME" is "azure-ai-anomalydetector" and "YOUR_SERVICE_DIRECTORY" is "sdk/anomalydetector"

  1. Run cmd to generate the SDK Install tsp-client CLI tool:

npm install -g @azure-tools/typespec-client-generator-cli For initial set up, from the root of the SDK repo, call:

D:\dev\azure-sdk-for-python> tsp-client init -c YOUR_REMOTE_TSPCONFIG_URL An example of YOUR_REMOTE_TSPCONFIG_URL is https://github.com/Azure/azure-rest-api-specs/blob/46ca83821edd120552403d4d11cf1dd22360c0b5/specification/contosowidgetmanager/Contoso.WidgetManager/tspconfig.yaml

To update your TypeSpec generated SDK, go to your SDK folder where your tsp-location.yaml is located, call:

D:\dev\azure-sdk-for-python\sdk\contoso\azure-contoso-widget> tox run -e generate -c ......\eng\tox\tox.ini --root . Note: To know more about tox, read our contributing guidelines

The tox run -e generate call will look for a tsp-location.yaml file in your local directory. tsp-location.yaml contains the configuration information that will be used to sync your TypeSpec project and generate your SDK. Please make sure that the commit is targeting the correct TypeSpec project updates you wish to generate your SDK from.

After re-generating the client make sure to:

  1. Re-run/Re-record all unit tests against the live-service (you can run ./eng/Record-Tests.ps1)
  2. If necessary, adjust the convenience layer for breaking-changes or to expose new features
  3. Add new unit-tests for new features and record them too

Building the azure-quantum Package wheel

The Azure Quantum Python SDK uses a standard setuptools-based packaging strategy. To build a platform-independent wheel, run the setup script with bdist_wheel instead:

python setup.py bdist_wheel

By default, this will create a azure-quantum wheel in dist/ with the version number set to 0.0.0.1. To provide a more useful version number, set the PYTHON_VERSION environment variable before running setup.py.

Environment Variables

In addition to the common Azure SDK environment variables, you can also set the following environment variables to change the behaviour of the Azure Quantum SDK for Python:

Environment Variable Description
AZURE_QUANTUM_PYTHON_APPID Prefixes the HTTP User-Agent header with the specified value

Code of Conduct

This project's code of conduct can be found in the CODE_OF_CONDUCT.md file.