04-10-2021

Python Job Runner

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Azure Pipelines

Running a Python Job Interactively on a CPU. It is not advised to run Python jobs directly on the head nodes. The prefered method, even if. Interactively on a GPU. Using a SLURM Submission Script. Once your tests are done and you're ready to run. In addition, your Python script will need to be able to accept arguments from the command line. There is an explanation of how to do this in this Software Carpentry lesson. Submitting Jobs. A sample submit file can be found in our hello world example page. You should make the following changes in order to run Python jobs. # Show the overall status of each partition sinfo # Submit a job sbatch.jobs/jobFile.job # See the entire job queue squeue # See only jobs for a given user squeue -u username # Count number of running / in queue jobs squeue -u username wc-l # Get estimated start times for your jobs (when Sherlock is busy) squeue -start-u username # Show the.

You can use Azure Pipelines to build, test, and deploy Python apps and scripts as part of your CI/CD system. This article focuses on creating a simple pipeline.

If you want an end-to-end walkthrough, see Use CI/CD to deploy a Python web app to Azure App Service on Linux.

To create and activate an Anaconda environment and install Anaconda packages with conda, see Run pipelines with Anaconda environments.

Create your first pipeline

Are you new to Azure Pipelines? If so, then we recommend you try this section before moving on to other sections.

Get the code

Import this repo into your Git repo in Azure DevOps Server 2019:

Sign in to Azure Pipelines

Sign in to Azure Pipelines. After you sign in, your browser goes to https://dev.azure.com/my-organization-name and displays your Azure DevOps dashboard.

Within your selected organization, create a project. If you don't have any projects in your organization, you see a Create a project to get started screen. Otherwise, select the Create Project button in the upper-right corner of the dashboard.

Create the pipeline

  1. Sign in to your Azure DevOps organization and navigate to your project.

  2. Go to Pipelines, and then select New Pipeline.

  3. Walk through the steps of the wizard by first selecting GitHub as the location of your source code.

  4. You might be redirected to GitHub to sign in. If so, enter your GitHub credentials.

  5. When the list of repositories appears, select your repository.

  6. You might be redirected to GitHub to install the Azure Pipelines app. If so, select Approve & install.

When the Configure tab appears, select Python package. This will create a Python package to test on multiple Python versions.

  1. When your new pipeline appears, take a look at the YAML to see what it does. When you're ready, select Save and run.

  2. You're prompted to commit a new azure-pipelines.yml file to your repository. After you're happy with the message, select Save and run again.

    If you want to watch your pipeline in action, select the build job.

    You just created and ran a pipeline that we automatically created for you, because your code appeared to be a good match for the Python package template.

    You now have a working YAML pipeline (azure-pipelines.yml) in your repository that's ready for you to customize!

  3. When you're ready to make changes to your pipeline, select it in the Pipelines page, and then Edit the azure-pipelines.yml file.

See the sections below to learn some of the more common ways to customize your pipeline.

YAML

  1. Add an azure-pipelines.yml file in your repository. Customize this snippet for your build.

  1. Create a pipeline (if you don't know how, see Create your first pipeline), and for the template select YAML.

  2. Set the Agent pool and YAML file path for your pipeline.

  3. Save the pipeline and queue a build. When the Build #nnnnnnnn.n has been queued message appears, select the number link to see your pipeline in action.

  4. When you're ready to make changes to your pipeline, Edit it.

  5. See the sections below to learn some of the more common ways to customize your pipeline.

Build environment

You don't have to set up anything for Azure Pipelines to build Python projects. Python is preinstalled on Microsoft-hosted build agents for Linux, macOS, or Windows. To see which Python versions are preinstalled, see Use a Microsoft-hosted agent.

Use a specific Python version

To use a specific version of Python in your pipeline, add the Use Python Version task to azure-pipelines.yml. This snippet sets the pipeline to use Python 3.6:

Use multiple Python versions

To run a pipeline with multiple Python versions, for example to test a package against those versions, define a job with a matrix of Python versions. Then set the UsePythonVersion task to reference the matrix variable.

You can add tasks to run using each Python version in the matrix.

Run Python scripts

To run Python scripts in your repository, use a script element and specify a filename. For example:

You can also run inline Python scripts with the Python Script task:

To parameterize script execution, use the PythonScript task with arguments values to pass arguments into the executing process. You can use sys.argv or the more sophisticated argparse library to parse the arguments.

Install dependencies

You can use scripts to install specific PyPI packages with pip. For example, this YAML installs or upgrades pip and the setuptools and wheel packages.

Install requirements

After you update pip and friends, a typical next step is to install dependencies from requirements.txt:

Python Job Runners

Run tests

You can use scripts to install and run various tests in your pipeline.

Run lint tests with flake8

To install or upgrade flake8 and use it to run lint tests, use this YAML:

Python Job Runner

Test with pytest and collect coverage metrics with pytest-cov

Use this YAML to install pytest and pytest-cov, run tests, output test results in JUnit format, and output code coverage results in Cobertura XML format:

Run tests with Tox

Azure Pipelines can run parallel Tox test jobs to split up the work. On a development computer, you have to run your test environments in series. This sample uses tox -e py to run whichever version of Python is active for the current job.

Publish test results

Job

Add the Publish Test Results task to publish JUnit or xUnit test results to the server:

Publish code coverage results

Add the Publish Code Coverage Results task to publish code coverage results to the server. You can see coverage metrics in the build summary, and download HTML reports for further analysis.

Package and deliver code

To authenticate with twine, use the Twine Authenticate task to store authentication credentials in the PYPIRC_PATH environment variable.

Then, add a custom script that uses twine to publish your packages.

You can also use Azure Pipelines to build an image for your Python app and push it to a container registry.

Related extensions

Python Job Runner Tutorial

  • PyLint Checker (Darren Fuller)
  • Python Test (Darren Fuller)
  • Azure DevOps plugin for PyCharm (IntelliJ) (Microsoft)
  • Python in Visual Studio Code (Microsoft)