@Marvin Do you know why this error would occur:

urllib3.exceptions.URLSchemeUnknown: Not supported URL scheme http+docker

During handling of the above exception, another exception occurred:

with

requests.exceptions.InvalidURL: Not supported URL scheme http+docker

During handling of the above exception, another exception occurred:

The error you're encountering,

urllib3.exceptions.URLSchemeUnknown

and

requests.exceptions.InvalidURL

, typically arises when there's an attempt to use an unsupported URL scheme in your request. The scheme

http+docker

appears to be unusual if you're trying to use it with typical HTTP requests. Here are a few common causes and solutions: 1. Invalid URL Scheme: Ensure that you're using a valid URL scheme (

http

,

https

, etc.) in your requests.

http+docker

is not standard and may be misconfigured. If you're dealing with Docker, make sure the URLs you're using are correctly formatted and intended for the Docker environment. 2. Using the Wrong Library: If you're working with Docker APIs, you might want to use a library like

docker-py

, which is designed to interact with Docker environments more effectively than using raw HTTP(S) requests. 3. Misconfiguration in Code: Double-check your configuration or any code that's constructing these URLs. There might be a mistake causing

http+docker

to appear instead of a valid scheme. 4. Custom Schemes: If you intended to use a custom scheme like

http+docker

, you would need to ensure that your environment or libraries support that scheme, possibly requiring custom handling or processing layers. If you'd like detailed information or solutions specific to Prefect usage, please provide more context about how you're using this within Prefect, as different versions might have particular nuances regarding integrations or configurations.

@Marvin the solution was downgrading the requests version to 2.31.0 which seems like a common issue. Do you know why I get this error: prefect.exceptions.ScriptError: Script at 'workflows/surface_map/inspection_flows.py' encountered an exception: ImportError('libcuda.so.1: cannot open shared object file: No such file or directory')? How can I force the docker worker to automatically use the gpu?

To address the

ImportError

related to

libcuda.so.1

and ensuring your Docker worker uses the GPU, follow these steps: 1. CUDA Support in Docker: The error indicates that the environment where your script is running cannot find the CUDA libraries, which are essential for GPU operations. This generally means your Docker container is not set up for GPU support. 2. Use NVIDIA Docker: Make sure you're using the NVIDIA container toolkit, which allows Docker containers to use GPU resources. You can find guidance on setting it up here: NVIDIA Docker Documentation. 3. Dockerfile Configuration: Your Dockerfile should be based on an NVIDIA CUDA image to ensure all necessary GPU libraries are included. Start with a base image like

nvidia/cuda:11.0-base

and ensure your environment has

libcuda

installed. 4. Launch Docker with GPU Support: When running your Docker container, use the

--gpus all

flag to ensure that all available GPUs are accessible to the container:

docker run --gpus all your-image

5. Prefect and Docker: If you're running this in a Prefect environment, ensure that your Prefect Docker agent is configured to pass the necessary GPU flags to your Docker containers. Since no specific Prefect documentation or GitHub issues were found regarding this error, it'd be good to check your Docker and environment setup thoroughly. Ensure Docker is correctly configured on your host machine to support GPU operations before considering further Prefect-specific configurations. Though sometimes it feels like trying to make a toaster bake a cake, setting up GPU support is quite a practical pursuit once all the pieces fall into place. If you need more precise Prefect-related configurations, let me know the Prefect version you're using, and I'll dig deeper!

@Marvin How do I make the agent pass the gpu flags to the container when it starts?

To configure a Prefect agent to pass GPU flags to Docker containers, you would typically do so through environment variable settings and Docker command flag configurations. Here's a general approach: 1. Environment Variables: Set up environment variables to inform your agent about the need to utilize GPU resources. 2. Custom Docker Command: You can adjust the Docker run command that the agent uses, often through environment settings or configurations. Unfortunately, I couldn't find explicit documentation detailing the process to pass GPU flags directly in Prefect 2.x or 3.x. However, in similar infrastructure setups, you can usually start the agent or worker with the necessary Docker options, like this:

shell
prefect agent start --docker --docker-run-command-options="--gpus all"

This command assumes you're running a Prefect Docker agent and need it to use GPU resources. Be sure that your Docker host has NVIDIA's container toolkit properly installed and configured. For more precise instructions, you'd want to refer to Prefect's latest Docker or agent configuration documentation—always a fun read on the couch with a cup of digital tea. If you need further guidance, let me know which version of Prefect you're using or give some more details about your setup.

@Marvin In case someone else searches this, you change

/etc/docker/daemon.json

to use the nvidia as the default runtime:

{
  "default-runtime": "nvidia",
  "runtimes": {
    "nvidia": {
      "path": "nvidia-container-runtime",
      "runtimeArgs": []
    }
  }
}

then

sudo systemctl restart docker

Thanks for sharing that solution! By setting the NVIDIA runtime as the default in Docker's daemon configuration, you ensure that all your containers will attempt to use the GPU if it's available. This is indeed crucial for applications that require GPU acceleration. After adding this configuration to

/etc/docker/daemon.json

, restarting the Docker service with

sudo systemctl restart docker

applies the changes and all containers will automatically attempt to use GPU resources where applicable. This setup is perfect for environments consistently using GPU computation. Let us know if there's anything else you need help with. It's almost like finding your favorite mug for that digital tea—once it's set up right, everything just clicks!

Thank you so much...you save me...

No worries dude 👌