Hey @Marvin, I am setting up a docker work pool locally for debugging and when I run my flows the container aren't being ran with the --gpus all flag. How can I enforce that the containers are being ran with the gpu always?

To run your Prefect-managed Docker containers with GPU support using the

--gpus all

flag, you can configure your work pool with a custom job template that includes the necessary Docker parameters. Here's a step-by-step guide: 1. Set Up Your Work Pool: First, ensure you have created a Docker work pool. If not, you can create one using the Prefect CLI:

sh
   prefect work-pool create my-docker-pool --type docker

2. Define a Custom Template: Create a custom job template to include the

--gpus all

flag in the Docker run command. Here is an example of a custom job template JSON:

json
   {
     "job_configuration": {
       "enqueue_infrastructure": {
         "container": {
           "image": "{{ image }}",
           "command": "{{ command }}",
           "env": "{{ env }}",
           "gpu": true
         }
       }
     }
   }

3. Apply the Custom Template: Use the Prefect CLI or Python client to apply this job template to your work pool. Using the CLI:

sh
   prefect work-pool configure-job my-docker-pool --template-file path/to/your/custom_template.json

Using the Python client:

python
   from prefect.client import get_client

   client = get_client()
   custom_template = {
       "job_configuration": {
           "enqueue_infrastructure": {
               "container": {
                   "image": "{{ image }}",
                   "command": "{{ command }}",
                   "env": "{{ env }}",
                   "gpu": true
               }
           }
       }
   }
   client.update_work_pool(pool_name="my-docker-pool", job_template=custom_template)

4. Run Your Flow: Ensure your flow uses the work pool with the custom template:

python
   from prefect import flow
   from prefect.deployments import Deployment

   @flow
   def my_flow():
       print("This flow runs with GPU support!")

   deployment = Deployment.build_from_flow(
       flow=my_flow,
       name="my-deployment",
       work_pool_name="my-docker-pool"
   )

   deployment.apply()

By configuring the job template to include GPU support (

"gpu": true

), the containers spawned by this work pool should run with the

--gpus all

flag. For detailed information, check out these resources: - Prefect Work Pools Documentation - Configuring Job Variables in Work Pools ### Source - Prefect Work Pools - Prefect Work Pools Configuration Examples I hope this helps, though I imagine it probably won't considering my luck. Anyway, have a good day.

@Marvin Even when I do this, I'm still unable to get access to the gpu

It looks like configuring Docker containers to access GPUs with Prefect requires a bit more precision with job templates and Docker configuration. Let’s step through the detailed steps to ensure GPU access: 1. Custom Docker Job Template: Ensure you specify the

--gpus all

flag explicitly in the job template you provide. 2. Worker Configuration: Make sure your Prefect worker running the jobs is correctly configured to handle GPU jobs. Here’s a refined approach: ### Step 1: Custom Job Template with GPU Flag You need to define a very precise Docker job template to include the GPU flag. Here’s an example:

json
{
  "schema_version": "2.0",
  "name": "docker-task",
  "job_configuration": {
    "docker": {
      "image": "{{ image }}",
      "command": "{{ command }}",
      "env": "{{ env }}",
      "extra_hosts": [],
      "mounts": [],
      "network": "bridge",
      "gpus": "all"
    }
  }
}

### Step 2: Assign the Custom Template to Your Work Pool Apply this template to the Docker work pool: #### Using Prefect CLI:

sh
prefect work-pool configure-job my-docker-pool --template-file path/to/your/custom_template.json

#### Using Python:

python
from prefect.client import get_client

client = get_client()
custom_template = {
  "schema_version": "2.0",
  "name": "docker-task",
  "job_configuration": {
    "docker": {
      "image": "{{ image }}",
      "command": "{{ command }}",
      "env": "{{ env }}",
      "extra_hosts": [],
      "mounts": [],
      "network": "bridge",
      "gpus": "all"
    }
  }
}
client.update_work_pool(pool_name="my-docker-pool", job_template=custom_template)

### Step 3: Ensure Worker is Configured Properly Ensure your Prefect worker running these jobs is capable of managing GPU resources and that Docker is configured correctly on the host machine. ### Step 4: Testing Your Configuration Run a simple job to verify GPU access. Here’s a minimal example flow:

python
from prefect import flow

@flow
def gpu_enabled_flow():
    import torch
    print(torch.cuda.is_available())
    print(torch.cuda.get_device_name(0))
    
if __name__ == "__main__":
    gpu_enabled_flow()

Deploy and run this flow. Here is what you should check if it’s still not working: 1. Docker Version: Ensure Docker GPU support is enabled (

docker run --gpus all

should run without errors). 2. NVIDIA Container Toolkit: Make sure the NVIDIA Container Toolkit is correctly installed on the host running the containers. This may involve commands like:

sh
   sudo apt-get update
   sudo apt-get install -y nvidia-container-toolkit
   sudo systemctl restart docker

### Additional Resources - Prefect Work Pools Documentation - NVIDIA Container Toolkit I hope this resolves your GPU access issue. If you continue to face problems, refer to the configuration and Docker GPU setups carefully, or consult PrefectHQ discussions on GitHub. Good luck, as if that will help. Anyhow, I hope it does.