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You'll need to first define how to authenticate with Azure. You can use a [service principal](../active-directory/develop/app-objects-and-service-principals.md#service-principal-object) or [OpenID Connect](https://docs.github.com/en/actions/deployment/security-hardening-your-deployments/about-security-hardening-with-openid-connect).
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You'll need to update the CLI setup file variables to match your workspace.
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1. In your cloned repository, go to `azureml-examples/cli/`.
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1. In your forked repository, go to `azureml-examples/cli/`.
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1. Edit `setup.sh` and update these variables in the file.
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|Variable | Description |
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You'll use a `pipeline.yml` file to deploy your Azure Machine Learning pipeline. This is a machine learning pipeline and not a DevOps pipeline. You only need to make this update if you're using a name other than `cpu-cluster` for your computer cluster name.
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1. In your cloned repository, go to `azureml-examples/cli/jobs/pipelines/nyc-taxi/pipeline.yml`.
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1. In your forked repository, go to `azureml-examples/cli/jobs/pipelines/nyc-taxi/pipeline.yml`.
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1. Each time you see `compute: azureml:cpu-cluster`, update the value of `cpu-cluster` with your compute cluster name. For example, if your cluster is named `my-cluster`, your new value would be `azureml:my-cluster`. There are five updates.
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## Step 5: Run your GitHub Actions workflow
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### Enable your workflow
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1. In your cloned repository, open `.github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml` and verify that your workflow looks like this.
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1. In your forked repository, open `.github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml` and verify that your workflow looks like this.
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```yaml
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name: cli-jobs-pipelines-nyc-taxi-pipeline
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### Enable your workflow
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1. In your cloned repository, open `.github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml` and verify that your workflow looks like this.
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1. In your forked repository, open `.github/workflows/cli-jobs-pipelines-nyc-taxi-pipeline.yml` and verify that your workflow looks like this.
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