• Forked version of Docker machine
• Preparing the environment
• Configuring GitLab Runner
• Upgrading GitLab Runner
• Using the forked version of Docker Machine
  • Install
  • Using GPUs on Google Compute Engine
    • Docker Machine GPU options
    • Verifying Docker Machine options
    • Configuring GitLab Runner

Install and register GitLab Runner for autoscaling with Docker Machine

For an overview of the autoscale architecture, take a look at the comprehensive documentation on autoscaling.

Forked version of Docker machine

Docker has deprecated Docker Machine. However, GitLab maintains a Docker Machine fork for GitLab Runner users who rely on the Docker Machine executor. This fork is based on the latest main branch of docker-machine with some additional patches for the following bugs:

• Make DigitalOcean driver RateLimit aware
• Add backoff to Google driver operations check
• Add --google-min-cpu-platform option for machine creation
• Use cached IP for Google driver
• Use cached IP for AWS driver
• Add support for using GPUs in Google Compute Engine
• Support running AWS instances with IMDSv2

The intent of the Docker Machine fork is to only fix critical issues and bugs which affect running costs. No new features will be added.

Preparing the environment

To use the autoscale feature, Docker and GitLab Runner must be installed in the same machine:

1. Log in to a new Linux-based machine that will serve as a bastion server where Docker will spawn new machines from
2. Install GitLab Runner
3. Install Docker Machine from the Docker Machine fork
4. Optionally but recommended, prepare a proxy container registry and a cache server to be used with the autoscaled runners

Configuring GitLab Runner

1. Familiarize yourself with the core concepts of using docker-machine together with gitlab-runner:
   • Read GitLab Runner Autoscaling
   • Read GitLab Runner MachineOptions

2. The first time you’re using Docker Machine, it is best to manually execute the docker-machine create ... command with your Docker Machine Driver. Run this command alongside with the options that you intend to configure in the GitLab Runner MachineOptions section. This will set up the Docker Machine environment properly and will also be a good validation of the specified options. After this, you can destroy the machine with docker-machine rm [machine_name] and start the runner.

   Multiple concurrent requests to docker-machine create that are done at first usage are not good. When the docker+machine executor is used, the runner may spin up few concurrent docker-machine create commands. If Docker Machine was not used before in this environment, each started process tries to prepare SSH keys and SSL certificates (for Docker API authentication between GitLab Runner and Docker Engine on the autoscaled spawned machine), and these concurrent processes are disturbing each other. This can end with a non-working environment. That’s why it’s important to create a test machine manually the very first time you set up GitLab Runner with Docker Machine.
3. Register a runner and select the docker+machine executor when asked.
4. Edit config.toml and configure the runner to use Docker machine. Visit the dedicated page covering detailed information about GitLab Runner Autoscaling.
5. Now, you can try and start a new pipeline in your project. In a few seconds, if you run docker-machine ls you should see a new machine being created.

Upgrading GitLab Runner

1. Check if your operating system is configured to automatically restart GitLab Runner (for example, by checking its service file):

   • if yes, ensure that service manager is configured to use SIGQUIT and use the service’s tools to stop the process:

     # For systemd
     sudo systemctl stop gitlab-runner
     
     # For upstart
     sudo service gitlab-runner stop
     

   • if no, you may stop the process manually:

     sudo killall -SIGQUIT gitlab-runner
     

   Sending the SIGQUIT signal will make the process stop gracefully. The process will stop accepting new jobs, and will exit as soon as the current jobs are finished.

2. Wait until GitLab Runner exits. You can check its status with gitlab-runner status or await a graceful shutdown for up to 30 minutes with:

   for i in `seq 1 180`; do # 1800 seconds = 30 minutes
       gitlab-runner status || break
       sleep 10
   done
   

3. You can now safely install the new version of GitLab Runner without interrupting any jobs.

Using the forked version of Docker Machine

Install

1. Download the appropriate docker-machine binary. Copy the binary to a location accessible to PATH and make it executable. For example, to download and install v0.16.2-gitlab.22:

    curl -O "https://gitlab-docker-machine-downloads.s3.amazonaws.com/v0.16.2-gitlab.22/docker-machine-Linux-x86_64"
    cp docker-machine-Linux-x86_64 /usr/local/bin/docker-machine
    chmod +x /usr/local/bin/docker-machine
   

Using GPUs on Google Compute Engine

You can use the Docker Machine fork to create Google Compute Engine instances with graphics processing units (GPUs). GitLab Runner 13.9 is required for GPUs to work in a Docker executor.

Docker Machine GPU options

To create an instance with GPUs, use these Docker Machine options:

These arguments map to command-line arguments for gcloud compute. See the Google documentation on creating VMs with attached GPUs for more details.

Verifying Docker Machine options

To prepare your system and test that GPUs can be created with Google Compute Engine:

1. Set up the Google Compute Engine driver credentials for Docker Machine. You may need to export environment variables to the runner if your VM does not have a default service account. How this is done depends on how the runner is launched. For example:

   • Via systemd or upstart: See the documentation on setting custom environment variables.
   • Via Kubernetes with the Helm Chart: Update the values.yaml entry.
   • Via Docker: Use the -e option (for example, docker run -e GOOGLE_APPLICATION_CREDENTIALS=/path/to/credentials.json gitlab/gitlab-runner).

2. Verify that docker-machine can create a virtual machine with your desired options. For example, to create an n1-standard-1 machine with a single NVIDIA Telsa P4 accelerator, substitute test-gpu with a name and run:

   docker-machine create --driver google --google-project your-google-project \
     --google-disk-size 50 \
     --google-machine-type n1-standard-1 \
     --google-accelerator type=nvidia-tesla-p4,count=1 \
     --google-maintenance-policy TERMINATE \
     --google-machine-image https://www.googleapis.com/compute/v1/projects/deeplearning-platform-release/global/images/family/tf2-ent-2-3-cu110 \
     --google-metadata "install-nvidia-driver=True" test-gpu
   

3. To verify the GPU is active, SSH into the machine and run nvidia-smi:

   $ docker-machine ssh test-gpu sudo nvidia-smi
   +-----------------------------------------------------------------------------+
   | NVIDIA-SMI 450.51.06    Driver Version: 450.51.06    CUDA Version: 11.0     |
   |-------------------------------+----------------------+----------------------+
   | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
   | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
   |                               |                      |               MIG M. |
   |===============================+======================+======================|
   |   0  Tesla P4            Off  | 00000000:00:04.0 Off |                    0 |
   | N/A   43C    P0    22W /  75W |      0MiB /  7611MiB |      3%      Default |
   |                               |                      |                  N/A |
   +-------------------------------+----------------------+----------------------+
   
   +-----------------------------------------------------------------------------+
   | Processes:                                                                  |
   |  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
   |        ID   ID                                                   Usage      |
   |=============================================================================|
   |  No running processes found                                                 |
   +-----------------------------------------------------------------------------+
   

4. Remove this test instance to save money:

    docker-machine rm test-gpu
   

Configuring GitLab Runner

1. Once you have verified these options, configure the Docker executor to use all available GPUs in the runners.docker configuration. Then add the Docker Machine options to your MachineOptions settings in the GitLab Runner runners.machine configuration. For example:

   [runners.docker]
     gpus = "all"
   [runners.machine]
     MachineOptions = [
       "google-project=your-google-project",
       "google-disk-size=50",
       "google-disk-type=pd-ssd",
       "google-machine-type=n1-standard-1",
       "google-accelerator=count=1,type=nvidia-tesla-p4",
       "google-maintenance-policy=TERMINATE",
       "google-machine-image=https://www.googleapis.com/compute/v1/projects/deeplearning-platform-release/global/images/family/tf2-ent-2-3-cu110",
       "google-metadata=install-nvidia-driver=True"
     ]