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# ask-marvin
s
@Marvin heelo
m
Hello. How can I assist you today with your Prefect data engineering queries?
s
i'm using prefect version 3.0 i want to use custom docker image worker like this.
Copy code
if __name__=="__main__":
    gpu_test_flow.deploy(
        name="gpu-test",
        tags=["gpu"],
        image=DockerImage(
            name="daegyo-prefect",
            dockerfile="Dockerfile"
        ),
        push=False,
        work_pool_name="gpu-pool"
    )
and then this deploy must use nvidia gpu. how to make it work??
s
@steven You need mension to 
@Marvin
per a message.
πŸ™Œ 1
s
thnks!
@Marvin i'm using prefect version 3.0 i want to use custom docker image worker like this.
Copy code
if __name__=="__main__":
    gpu_test_flow.deploy(
        name="gpu-test",
        tags=["gpu"],
        image=DockerImage(
            name="daegyo-prefect",
            dockerfile="Dockerfile"
        ),
        push=False,
        work_pool_name="gpu-pool"
    )
and then this deploy must use nvidia gpu. how to make it work??
m
To use a custom Docker image with NVIDIA GPU support in Prefect 3.x, you'll need to ensure that both your Docker image and the Prefect infrastructure are configured to utilize the GPU. Here's a concise guide to help you achieve this: 1. Dockerfile Configuration: Your Dockerfile should be based on an NVIDIA CUDA image or any other image that supports GPU. Make sure to include the necessary NVIDIA libraries and drivers. You can start with something like:
Copy code
dockerfile
   FROM nvidia/cuda:11.2-base
   # Install other dependencies
2. Prefect Deployment: When deploying your flow, you can use the 
DockerImage
class to specify your custom Dockerfile. Your code snippet already demonstrates this correctly:
Copy code
python
   from prefect.deployments import DockerImage

   if __name__ == "__main__":
       gpu_test_flow.deploy(
           name="gpu-test",
           tags=["gpu"],
           image=DockerImage(
               name="daegyo-prefect",
               dockerfile="Dockerfile"
           ),
           push=False,
           work_pool_name="gpu-pool"
       )
3. Work Pool Configuration: Ensure that your work pool is set up to use Docker infrastructure that supports GPU. You might need to configure your Docker runtime to use 
nvidia
by setting the appropriate environment variables or Docker options. 4. Starting a Worker: You will need to start a Prefect worker that can execute on the Docker infrastructure. This involves running a command similar to 
prefect agent start docker
but with GPU support configured. For more details, you can refer to the Prefect Docker Infrastructure Example and ensure that your Docker environment is set up correctly for GPU usage. If you encounter any specific issues, feel free to ask for more targeted help.
Your message was too long by 348 tokens...
s
@Marvin so Work Pool Configuration is the key point. how to setup Work Pool Configuration? i'm making worker pool by this code. do we need any correction? give me some suggestion
Copy code
client = get_client()
            if name == "gpu-pool":
                base_job_template = {
                    "extra_docker_run_kwargs": {
                        "runtime": "nvidia",
                        "gpus": "all"
                    }
                }

            await client.create_work_pool(
                work_pool=WorkPoolCreate(
                    name=name,
                    type=type,
                    description=description or f"Work pool for {name}",
                    base_job_template=base_job_template or {}
                )
            )
m
Your current setup for creating a work pool with GPU support looks mostly correct. However, you should ensure that the 
base_job_template
is accurately configured to enable GPU usage within the Docker runtime. Here's a refined version of your setup, along with some suggestions: 1. Ensure Correct `extra_docker_run_kwargs`: The 
extra_docker_run_kwargs
should include 
"runtime": "nvidia"
and 
"gpus": "all"
to specify that the Docker container should use the NVIDIA runtime and all available GPUs. Your current configuration handles this. 2. Work Pool Type: Ensure that the 
type
parameter in 
WorkPoolCreate
matches the infrastructure type you are targeting (e.g., 
docker
). This is crucial for the work pool to know how to handle the jobs it receives. Here is the refined code with these considerations:
Copy code
python
from prefect.client import get_client
from prefect.client.schemas.actions import WorkPoolCreate

async def create_gpu_work_pool(name, type, description=None):
    client = get_client()
    if name == "gpu-pool":
        base_job_template = {
            "extra_docker_run_kwargs": {
                "runtime": "nvidia",
                "gpus": "all"
            }
        }

    await client.create_work_pool(
        work_pool=WorkPoolCreate(
            name=name,
            type=type,
            description=description or f"Work pool for {name}",
            base_job_template=base_job_template or {}
        )
    )

# Example usage
# await create_gpu_work_pool("gpu-pool", "docker")
3. Documentation Reference: For more details on configuring work pools, you can refer to the Prefect Work Pools Documentation. Ensure that your Docker environment is correctly set up to support GPU execution, and test the setup with a simple flow to verify that the GPU is being utilized as expected. If you have any further questions or issues, feel free to ask!
s
@Marvin So, current setting are enough?
m
Yes, your current settings for creating a work pool with GPU support should be sufficient. Just ensure that: 1. The 
extra_docker_run_kwargs
in your 
base_job_template
is correctly set with 
"runtime": "nvidia"
and 
"gpus": "all"
. 2. The 
type
parameter in 
WorkPoolCreate
is set to 
"docker"
or the appropriate infrastructure type you are using. 3. Your Docker environment is configured to support GPU execution. If all these are in place, your work pool should be able to handle GPU-enabled tasks. It's always a good idea to test with a simple flow to confirm that everything is working as expected. If you run into any issues, feel free to reach out for further assistance.
s
@Marvin but if i deploy work flow, this error occured.
Copy code
μ˜ˆμ™Έκ°€ λ°œμƒν–ˆμŠ΅λ‹ˆλ‹€. ValueError
Work pool 'gpu-pool' does not support custom Docker images. Please use a work pool with an `image` variable in its base job template or specify a remote storage location for the flow with `.from_source`. If you are attempting to deploy a flow to a local process work pool, consider using `flow.serve` instead. See the documentation for more information: <https://docs.prefect.io/latest/deploy/run-flows-in-local-processes>
  File "/home/kinest1997/daegyo/prefect-server/gpu_test/train_flow.py", line 42, in <module>
    gpu_test_flow.deploy(
ValueError: Work pool 'gpu-pool' does not support custom Docker images. Please use a work pool with an `image` variable in its base job template or specify a remote storage location for the flow with `.from_source`. If you are attempting to deploy a flow to a local process work pool, consider using `flow.serve` instead. See the documentation for more information: <https://docs.prefect.io/latest/deploy/run-flows-in-local-processes>
m
The error you're encountering indicates that the work pool you've created does not support custom Docker images because it lacks an 
image
variable in its base job template. Here's how you can address this issue: 1. Include an 
image
Variable in the Base Job Template: Modify your 
base_job_template
to include an 
image
variable. This variable should be part of the 
variables
section in your job template configuration. This allows the work pool to support custom Docker images. 2. Example Configuration: Here's an example of how you might configure your 
base_job_template
to include the 
image
variable:
Copy code
python
   from prefect.client import get_client
   from prefect.client.schemas.actions import WorkPoolCreate

   async def create_gpu_work_pool(name, type, description=None):
       client = get_client()
       if name == "gpu-pool":
           base_job_template = {
               "extra_docker_run_kwargs": {
                   "runtime": "nvidia",
                   "gpus": "all"
               },
               "variables": {
                   "image": {
                       "type": "string",
                       "description": "Docker image to use for the flow run",
                       "default": "your-default-image"
                   }
               }
           }

       await client.create_work_pool(
           work_pool=WorkPoolCreate(
               name=name,
               type=type,
               description=description or f"Work pool for {name}",
               base_job_template=base_job_template or {}
           )
       )

   # Example usage
   # await create_gpu_work_pool("gpu-pool", "docker")
3. Documentation Reference: For more information on configuring work pools, you can refer to the Prefect Work Pools Documentation. By including the 
image
variable in your job template, you should be able to deploy flows that utilize custom Docker images. Make sure to test this configuration to ensure it resolves the issue. If further problems arise, feel free to ask for additional help.
s
@Marvin but i want to use custom image. is that workpool can use prebuilded docker images?
m
Yes, a work pool in Prefect can indeed use prebuilt Docker images. To configure your work pool to support prebuilt Docker images, you need to ensure that the 
base_job_template
includes an 
image
variable. This allows you to specify the Docker image you want to use for your flow runs. Here's how you can set it up: 1. Modify Base Job Template: Add an 
image
variable to your 
base_job_template
. This variable will allow you to specify which Docker image to use when deploying your flow. 2. Example Configuration:
Copy code
python
   from prefect.client import get_client
   from prefect.client.schemas.actions import WorkPoolCreate

   async def create_gpu_work_pool(name, type, description=None):
       client = get_client()
       if name == "gpu-pool":
           base_job_template = {
               "extra_docker_run_kwargs": {
                   "runtime": "nvidia",
                   "gpus": "all"
               },
               "variables": {
                   "image": {
                       "type": "string",
                       "description": "Docker image to use for the flow run",
                       "default": "your-default-image"
                   }
               }
           }

       await client.create_work_pool(
           work_pool=WorkPoolCreate(
               name=name,
               type=type,
               description=description or f"Work pool for {name}",
               base_job_template=base_job_template or {}
           )
       )

   # Example usage
   # await create_gpu_work_pool("gpu-pool", "docker")
3. Deploying with a Prebuilt Image: When you deploy your flow, specify the prebuilt Docker image you want to use. This can be done by setting the 
image
parameter in your deployment configuration. By setting up your work pool with the 
image
variable, you can easily use prebuilt Docker images for your deployments. This approach gives you the flexibility to use any Docker image that suits your needs. If you need further assistance, feel free to reach out!
s
@Marvin when i create work pool, i want to use local docker image, and do not pull from origin. how to make it? current code is below.
Copy code
client = get_client()

            await client.create_work_pool(
                work_pool=WorkPoolCreate(
                    name=name,
                    type=type,
                    description=description or f"Work pool for {name}",
                    base_job_template={
                        "job_configuration": {
                            "image": "localhost/daegyo-prefect:latest",  # localhost λͺ…μ‹œ
                            "pull_policy": "IF_NOT_PRESENT"  # λ‘œμ»¬μ— 있으면 pull ν•˜μ§€ μ•ŠμŒ
                        },
                        "variables": {
                            "docker_runtime": "nvidia",
                            "gpu_access": "all",
                            "shared_memory": "8G"
                        },
                        "extra_docker_run_kwargs": {
                            "runtime": "nvidia",  # NVIDIA GPU μ‚¬μš©
                            "gpus": "all",  # λͺ¨λ“  GPU μ‚¬μš© κ°€λŠ₯ν•˜λ„λ‘ μ„€μ •
                            "shm_size": "8G"  # shared memory 크기 μ„€μ • (PyTorch DataLoader μœ„ν•΄ ꢌμž₯)
                        }
                    }
                )
            )
m
To ensure that your work pool uses a local Docker image without pulling from a remote registry, you can configure the 
pull_policy
as you've done. Here's a concise explanation and confirmation of your setup: 1. Local Image Reference: Ensure that the 
image
field in your 
job_configuration
correctly references the local image. You've set it to 
"localhost/daegyo-prefect:latest"
, which is appropriate for a local image. 2. Pull Policy: The 
pull_policy: "IF_NOT_PRESENT"
configuration ensures that Docker will only pull the image if it is not already present locally. This is exactly what you need to avoid pulling from a remote registry. 3. Verify Local Image: Before deploying, verify that the image 
localhost/daegyo-prefect:latest
is indeed available locally by running 
docker images
to list all available images and confirm its presence. Here's your configuration with comments confirming each part:
Copy code
python
from prefect.client import get_client
from prefect.client.schemas.actions import WorkPoolCreate

async def create_local_image_work_pool(name, type, description=None):
    client = get_client()
    
    await client.create_work_pool(
        work_pool=WorkPoolCreate(
            name=name,
            type=type,
            description=description or f"Work pool for {name}",
            base_job_template={
                "job_configuration": {
                    "image": "localhost/daegyo-prefect:latest",  # Local image reference
                    "pull_policy": "IF_NOT_PRESENT"  # Use local image if available
                },
                "variables": {
                    "docker_runtime": "nvidia",
                    "gpu_access": "all",
                    "shared_memory": "8G"
                },
                "extra_docker_run_kwargs": {
                    "runtime": "nvidia",  # Use NVIDIA runtime
                    "gpus": "all",  # Allow all GPUs
                    "shm_size": "8G"  # Set shared memory size
                }
            }
        )
    )

# Example usage
# await create_local_image_work_pool("gpu-pool", "docker")
With this setup, your work pool should use the local Docker image without attempting to pull it from a remote registry. If you encounter any issues, ensure that the image name and tag exactly match the local image you have.
s
@Marvin it does not working. i have installed nvidia container toolkit, and etc.. how to make docker worker can use host gpu?
m
To ensure that your Docker worker can use the host GPU with Prefect, follow these steps: 1. Verify NVIDIA Container Toolkit Installation: Make sure that the NVIDIA Container Toolkit is correctly installed and configured on your host. You can verify this by running 
nvidia-smi
inside a Docker container with the NVIDIA runtime. 2. Docker Daemon Configuration: Ensure that your Docker daemon is configured to use the NVIDIA runtime by default. You can set this in the Docker daemon configuration file (usually located at 
/etc/docker/daemon.json
) like so:
Copy code
json
   {
     "runtimes": {
       "nvidia": {
         "path": "nvidia-container-runtime",
         "runtimeArgs": []
       }
     },
     "default-runtime": "nvidia"
   }
After making changes, restart the Docker service: 
sudo systemctl restart docker
. 3. Prefect Worker Configuration: When starting a Prefect worker, ensure that it is aware of the GPU configuration. You might need to specify the runtime and GPU access in the worker's environment or configuration. 4. Test with a Simple Flow: Before deploying your full application, test with a simple Prefect flow that utilizes the GPU to confirm everything is configured correctly. 5. Documentation Reference: Unfortunately, the Prefect documentation does not provide explicit steps for GPU configuration in Docker, but you can refer to the Prefect Docker Infrastructure Example for general guidance on setting up Docker workers. By ensuring your Docker daemon is set to use the NVIDIA runtime and verifying the setup with a simple flow, you should be able to utilize the host GPU with your Prefect Docker worker. If you continue to encounter issues, it might be worthwhile to check NVIDIA's documentation or forums for additional troubleshooting steps.
s
@Marvin give me some detail about that 3. Prefect Worker Configuration: When starting a Prefect worker, ensure that it is aware of the GPU configuration. You might need to specify the runtime and GPU access in the worker's environment or configuration.
m
To configure a Prefect worker to utilize GPU resources, you should focus on the following steps: 1. Environment Variables: Set environment variables that inform the worker about the GPU configuration. This might include setting the NVIDIA runtime and specifying GPU resources. 2. Worker Startup Command: When starting a Prefect worker, you can specify additional Docker run arguments. This is typically done when you start the worker from the command line or in a script. Ensure that the 
--runtime=nvidia
flag is included if you are manually starting the Docker container. 3. Docker Run Configuration: If you are using Prefect's built-in commands to start a Docker-based worker, you might need to configure the Docker run settings to ensure that the NVIDIA runtime is used. This can be done by configuring the 
extra_docker_run_kwargs
as you have in your work pool setup. 4. Prefect Worker Documentation: While the specific steps for GPU configuration aren't detailed in the Prefect documentation, the general Prefect Workers Documentation provides guidance on how to configure workers. 5. Test GPU Access: After starting the worker, run a simple Prefect flow that utilizes the GPU to confirm that the worker is correctly configured. By focusing on these areas, you can ensure that your Prefect worker is properly configured to utilize the host GPU. If you continue to face issues, consider checking Docker and NVIDIA documentation for additional configuration tips related to GPU access.
s
@Marvin there is no --runtime=nvidia argument in prefect worker start.
m
You're correct that the 
prefect worker start
command itself does not include a direct way to pass Docker runtime arguments like 
--runtime=nvidia
. Instead, you need to configure your Docker environment and Prefect work pool settings to ensure GPU usage. Here's how you can set up your Prefect worker to utilize the GPU: 1. Docker Daemon Configuration: Ensure your Docker daemon is set to use the NVIDIA runtime by default. This involves configuring the Docker daemon as mentioned earlier:
Copy code
json
   {
     "runtimes": {
       "nvidia": {
         "path": "nvidia-container-runtime",
         "runtimeArgs": []
       }
     },
     "default-runtime": "nvidia"
   }
Restart the Docker service after making changes: 
sudo systemctl restart docker
. 2. Work Pool Configuration: In your work pool configuration, ensure that 
extra_docker_run_kwargs
includes the necessary settings for GPU access:
Copy code
python
   "extra_docker_run_kwargs": {
       "runtime": "nvidia",
       "gpus": "all",
       "shm_size": "8G"
   }
3. Starting the Worker: You won't pass runtime arguments directly when starting the worker. Instead, ensure that your Docker configuration and work pool settings are correctly set up to use the NVIDIA runtime. 4. Testing: Run a test flow to verify that the GPU is being utilized. Use a simple task that requires GPU computation to confirm that everything is working as expected. By ensuring your Docker and work pool configurations are set up correctly, you should be able to run Prefect flows that utilize the GPU without needing to pass runtime arguments directly in the 
prefect worker start
command. If you encounter further issues, double-check your Docker and NVIDIA configurations.
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