self-hosted-runners.md

Self-hosted (On-premise or Cloud) Runners

GitHub Actions, GitLab CI/CD, and Bitbucket Pipelines workflows are executed on "native" runners (hosted by GitHub/GitLab/Bitbucket respectively) by default. However, there are many great reasons to use your own runners: to take advantage of GPUs, orchestrate your team's shared computing resources, or train in the cloud.

Check out the official documentation from GitHub, GitLab and Bitbucket for more information on self-hosted runners.

Allocating Cloud Compute Resources with CML

When a workflow requires computational resources (such as GPUs), CML can automatically allocate cloud instances using cml runner. You can spin up instances on AWS, Azure, GCP, or Kubernetes (see below). Alternatively, you can connect any other compute provider or on-premise (local) machine.

For example, the following workflow deploys a p2.xlarge instance on AWS EC2 and trains a model on the instance. After the job runs, the instance automatically shuts down.

You might notice that this workflow is quite similar to the basic use case. The only addition is cml runner and a few environment variables for passing your cloud compute credentials to the workflow.

Note that cml runner will also automatically restart your jobs (whether from a GitHub Actions 35 day workflow timeout or an AWS EC2 spot instance interruption).

name: CML
on: [push]
jobs:
  launch-runner:
    runs-on: ubuntu-latest
    steps:
      - uses: iterative/setup-cml@v1
      - uses: actions/checkout@v3
        with:
          ref: ${{ github.event.pull_request.head.sha }}
      - name: Deploy runner on EC2
        env:
          REPO_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
        run: |
          cml runner launch \
              --cloud=aws \
              --cloud-region=us-west \
              --cloud-type=p2.xlarge \
              --labels=cml-gpu
  train-and-report:
    needs: launch-runner
    runs-on: [self-hosted, cml-gpu]
    timeout-minutes: 50400 # 35 days
    container:
      image: docker://iterativeai/cml:0-dvc2-base1-gpu
      options: --gpus all
    steps:
      - uses: actions/checkout@v3
        with:
          ref: ${{ github.event.pull_request.head.sha }}
      - name: Train model
        env:
          REPO_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
        run: |
          pip install -r requirements.txt
          python train.py  # generate plot.png

          # Create CML report
          cat metrics.txt >> report.md
          echo '![](./plot.png "Confusion Matrix")' >> report.md
          cml comment create report.md

launch-runner:
  image: iterativeai/cml:0-dvc2-base1
  script:
    - |
      cml runner launch \
          --cloud=aws \
          --cloud-region=us-west \
          --cloud-type=p2.xlarge \
          --cloud-spot \
          --labels=cml-gpu
train-and-report:
  needs: [launch-runner]
  tags: [cml-gpu]
  image: iterativeai/cml:0-dvc2-base1-gpu
  script:
    - pip install -r requirements.txt
    - python train.py # generate plot.png
    # Create CML report
    - cat metrics.txt >> report.md
    - echo '![](./plot.png "Confusion Matrix")' >> report.md
    - cml comment create report.md

pipelines:
  default:
    - step:
        image: iterativeai/cml:0-dvc2-base1
        script:
          - |
            cml runner launch \
                --cloud=aws \
                --cloud-region=us-west \
                --cloud-type=m5.2xlarge \
                --cloud-spot \
                --labels=cml.runner
    - step:
        runs-on: [self.hosted, cml.runner]
        image: iterativeai/cml:0-dvc2-base1
        script:
          - pip install -r requirements.txt
          - python train.py # generate plot.png
          # Create CML report
          - cat metrics.txt >> report.md
          - echo '![](./plot.png "Confusion Matrix")' >> report.md
          - cml comment create report.md

Bitbucket does not support GPUs natively (cml#1015, BCLOUD-21459). A work-around is to directly use TPI (the library which CML runner uses internally). TPI includes a CLI-friendly helper called LEO (launch, execute, orchestrate), used below:

image: iterativeai/cml:0-dvc2-base1
pipelines:
  default:
    - step:
        name: Launch Runner and Train
        script:
          # Create training script
          - |
            cat <<EOF > leo-script.sh
            #!/bin/bash
            apt-get update -q && apt-get install -yq python3.9
            pip3 install -r requirements.txt
            python train.py # generate plot.png
            EOF
          # Launch runner
          - |
            LEO_OPTIONS="--cloud=aws --region=us-west"
            leo_id=$(leo create $LEO_OPTIONS \
              --image=nvidia
              --machine=p2.xlarge \
              --disk-size=64 \
              --workdir=. \
              --output=. \
              --environment AWS_ACCESS_KEY_ID="$AWS_ACCESS_KEY_ID" \
              --environment AWS_SECRET_ACCESS_KEY="$AWS_SECRET_ACCESS_KEY" \
              --script="$(cat ./leo-script.sh)"
            )
            # Wait for cloud training to finish
            leo read $LEO_OPTIONS --follow "$leo_id"
            sleep 45 # TODO: explain
            # Download cloud training results & clean up cloud resources
            leo delete $LEO_OPTIONS --workdir=. --output=. "$leo_id"
          # Create CML report
          - cat metrics.txt >> report.md
          - echo '![](./plot.png "Confusion Matrix")' >> report.md
          - cml comment create report.md

In the workflow above, the launch-runner step launches an EC2 p2.xlarge instance in the us-west region. The train-and-report job then runs on the newly-launched instance. See Environment Variables below for details on the secrets required.

🎉 Note that jobs can use any Docker container! To use commands such as cml comment create from a job, the only requirement is to have CML installed.

Docker Images

The CML Docker images (docker://iterativeai/cml or docker://ghcr.io/iterative/cml) come loaded with Python, CUDA, git, node and other essentials for full-stack data science. Different versions of these essentials are available from different iterativeai/cml image tags. The tag convention is {CML_VER}-dvc{DVC_VER}-base{BASE_VER}{-gpu}:

{BASE_VER}	Software included (-gpu)
0	Ubuntu 18.04, Python 2.7 (CUDA 10.1, CuDNN 7)
1	Ubuntu 20.04, Python 3.8 (CUDA 11.0.3, CuDNN 8)

For example, docker://iterativeai/cml:0-dvc2-base1-gpu, or docker://ghcr.io/iterative/cml:0-dvc2-base1.

Options

The cml runner command supports many options (see the command reference). Notable options are:

• --labels=<...>: One or more (comma-delimited) labels (e.g. cml,gpu).
• --idle-timeout=<seconds>: Seconds to wait for jobs before terminating.
• --single: Terminate runner after one workflow run.
• --reuse: Don't launch a new runner if an existing one has the same name or overlapping labels.
• --cloud={aws,azure,gcp,kubernetes}: Cloud compute provider to host the runner.
• --cloud-type={m,l,xl,m+k80,m+v100,...}: Instance type. Also accepts native types such as t2.micro.
• --cloud-gpu={nogpu,k80,v100,tesla}: GPU type.
• --cloud-hdd-size=<...>: Disk storage in GB.
• --cloud-spot: Request a preemptible spot instance.
• --cloud-spot-price=<...>: Maximum spot instance USD bidding price.
• --cloud-region={us-west,us-east,eu-west,eu-north,...}: Region where the instance is deployed. Also accepts native AWS/Azure region or GCP zone.
• --cloud-permission-set=<...>: AWS instance profile or GCP instance service account.

☝️ Tip! Check out the full cml runner command reference.

Environment Variables

Sensitive values like cloud and repository credentials can be provided through environment variables with the aid of GitHub secrets, GitLab masked variables (or external secrets for added security), or Bitbucket secured user-defined variables.

You will need to create a personal access token (PAT) with enough permissions to register self-hosted runners. In the GitHub example workflow above, this token is stored as PERSONAL_ACCESS_TOKEN.

🛈 If using the --cloud option, you will also need to provide access credentials for your cloud compute resources as secrets. In the above example, AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY (with privileges to create & destroy EC2 instances) are required.

Personal Access Token

This token serves as a repository access credential, and is especially required for cml runner to function.

Use either:

• a personal access token with the repo scope, or
• a GitHub App with the appropriate permissions (see below)

Ideally, you should not use personal access tokens from your own account, as they grant access to all your repositories. Instead, it's highly recommended to create a separate bot account that only has access to the repositories where you plan to deploy runners to. Bot accounts are the same as normal user accounts, with the only difference being the intended use case.

PAT

For instance, to use a personal access token:

1. Generate a new personal access token under GitHub developer settings
   • in the "Note" field, type PERSONAL_ACCESS_TOKEN
   • select repo scope
   • click "Generate token" and copy it
2. In your GitHub repository and/or organization, navigate to Settings → Secrets → New repository/organization secret
   • in the "Name" field, type PERSONAL_ACCESS_TOKEN
   • in the "Value" field, paste the token
   • click "Add secret"

Step 2 can also be used for adding other secrets such as cloud access credentials.

App

Alternatively, a GitHub App ID (CML_GITHUB_APP_ID) and private key (CML_GITHUB_APP_PEM) can be used to generate a token on-the-fly, as shown in the example below:

steps:
  - uses: navikt/github-app-token-generator@v1
    id: get-token
    with:
      private-key: ${{ secrets.CML_GITHUB_APP_PEM }}
      app-id: ${{ secrets.CML_GITHUB_APP_ID }}
  - uses: actions/checkout@v3
    with:
      ref: ${{ github.event.pull_request.head.sha }}
      token: ${{ steps.get-token.outputs.token }}
  - name: Train model
    env:
      REPO_TOKEN: ${{ steps.get-token.outputs.token }}
    run: |
      ...
      cml comment create report.md

Note that the Apps require the following write permissions:

• Repository permissions (if used on a per-repo basis)
  • Administration (cml runner)
  • Checks (cml check)
  • Pull requests (cml {pr,comment})
• Organization permissions (if used on an org)
  • Self-hosted runners (cml runner)

Use either:

• a personal access token with the api, read_repository and write_repository scopes, or
• a project access token. This only works for project-level (specific), but not for instance-level (shared) runners.

For instance, to use a personal access token:

1. Navigate to User Settings → Access Tokens

   • in the "Name" field, type REPO_TOKEN
   • select api, read_repository and write_repository
   • click "Create personal access token" and copy it

   

2. In your GitLab project, navigate to Settings → CI/CD → Variables → Add Variable

   

   • in the "Key" field, type REPO_TOKEN
   • in the "Value" field, paste your Personal Access Token
   • select "Mask variable"
   • deselect "Protect variable"
   • click "Add variable" at the bottom of the dialog box

Step 2 can also be used for adding other masked variables such as cloud access credentials.

Bitbucket Cloud does not use access tokens. Instead, create a REPO_TOKEN variable with a Base64 encoded username and password.

Use either:

• your username and a Bitbucket Cloud App Password with Read permission for Account and Write permission for Pull requests, Pipelines, and Runners, or
• create a designated "CI/CD" bot account for CML authentication. Bot accounts are the same as normal user accounts, with the only difference being the intended use case: you limit the account to only access the repositories where you plan to deploy runners to.

In either case, the steps to create a REPO_TOKEN are:

1. Use a Base64 encoder of your choice to encode a Bitbucket username and password:
   • echo -n $USERNAME:$PASSWORD | base64. The -n ensures the base64 does not contain the trailing newline that echo adds by default.
   • copy the resulting Base64 token
2. In your repository, go to Repository Settings → Repository Variables
   • in the "Name" field, type REPO_TOKEN
   • in the "Value" field, paste the Base64 token
   • select Secured to hide credentials in all Bitbucket logs

Step 2 can also be used for adding other secured variables such as cloud access credentials.

Cloud Compute Resource Credentials

Note that you will also need to provide access credentials of your compute resources. In the above example, AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are required to deploy EC2 instances.

Click below to see credentials needed for supported compute providers.

• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY
• AWS_SESSION_TOKEN (optional)

See the AWS credentials docs for obtaining these keys.

☝️ Note The same credentials can also be used for configuring cloud storage.

• AZURE_CLIENT_ID
• AZURE_CLIENT_SECRET
• AZURE_SUBSCRIPTION_ID
• AZURE_TENANT_ID

Either one of:

• GOOGLE_APPLICATION_CREDENTIALS_DATA: the contents of a service account JSON file, or
• GOOGLE_APPLICATION_CREDENTIALS: the path to the JSON file.

The former is more convenient for CI/CD scenarios, where secrets are (usually) provisioned through environment variables instead of files.

• KUBERNETES_CONFIGURATION: the contents of a kubeconfig file.

On-premise (Local) Runners

The cml runner command can also be used to manually set up a local machine, on-premise GPU cluster, or any other cloud compute resource as a self-hosted runner. Simply install CML and then run:

$ cml runner launch \
  --repo="$REPOSITORY_URL" \
  --token="$PERSONAL_ACCESS_TOKEN" \
  --labels="local,runner" \
  --idle-timeout=180

The machine will listen for jobs from your repository and execute them locally.

⚠️ Warning: anyone with access to your repository (everybody for public ones) may be able to execute arbitrary code on your machine. Refer to the corresponding GitHub and GitLab documentation for additional guidance.

Debugging

If cml runner fails with a Terraform error message, setting the environment variable TF_LOG_PROVIDER=DEBUG may yield more information.

In very rare cases, you may need to clean up CML cloud resources manually. An example of such a problem can be seen when an EC2 instance ran out of storage space.

The following is a list of all the resources you may need to manually clean up in the case of a failure:

• The running instance (named with pattern cml-{random-id})
• The volume attached to the running instance (this should delete itself after terminating the instance)
• The generated key-pair (named with pattern cml-{random-id})

If you encounter these edge cases create a GitHub Issue with as much detail as possible. If possible link your workflow in the issue or provide an example of your workflow's YAML.

Additionally, try to capture and include logs from the instance:

For easy local access and debugging on the cml runner instance check our example on using the --cloud-startup-script option.

Then you can run the following:

$ ssh ubuntu@instance_public_ip
$ sudo journalctl -n all -u cml.service --no-pager > cml.log
$ sudo dmesg --ctime > system.log

☝️ Note Please give your cml.log a visual scan, entries like IP addresses and Git repository names may be present and considered sensitive in some cases.

You can then copy those logs to your local machine with:

$ scp ubuntu@instance_public_ip:~/cml.log .
$ scp ubuntu@instance_public_ip:~/system.log .

There is a chance that the instance could be severely broken if the SSH command hangs -- if that happens reboot it from the web console and try the commands again.