New RunLLM response generated: >>>*# Question* Losing my nerves over this for the whole day, maybe someone here has an idea: • I can't get GPU available within the flyte task, i.e.

torch.cuda.is_available()

returns False • However, if I

kubectl exec /bin/bash

into the same pod,

torch.cuda.is_available()

would return True What could be possible reasons as to why that could be the case? I am using a custom docker image for registering tasks/workflows, image is based on

python:3.10-slim

and has

flytekit-1.13.5

installed into it # Answer The issue you're experiencing could be due to several factors, including the configuration of GPU resources in your Flyte task, the Docker image setup, or the Kubernetes environment. Here are some potential reasons and solutions: 1. GPU Resource Requests and Limits: Ensure that your Flyte task correctly requests and limits GPU resources. The

Resources

object should specify the GPU resource. 2. Docker Image Configuration: Verify that your custom Docker image includes all necessary dependencies for GPU support, such as CUDA and cuDNN. Ensure that

flytekit

and

torch

are installed in the image. 3. Kubernetes Node Configuration: Ensure that the Kubernetes nodes are correctly configured to expose GPU resources. The nodes should have the NVIDIA device plugin installed and configured. 4. Flyte Task Configuration: Ensure that the Flyte task is correctly configured to use the GPU. The

ImageSpec

should include the necessary CUDA and cuDNN versions. Here is an example of how to configure a Flyte task to request GPU resources:

from flytekit import ImageSpec, Resources, task

image = ImageSpec(
    base_image="python:3.10-slim",
    name="custom_image",
    python_version="3.10",
    packages=["flytekit==1.13.5", "torch"],
    registry="<YOUR_CONTAINER_REGISTRY>",
)

@task(
    container_image=image,
    requests=Resources(cpu="1", mem="1Gi", gpu="1"),
    limits=Resources(cpu="1", mem="1Gi", gpu="1")
)
def run_gpu_task():
    import torch
    cuda_available = torch.cuda.is_available()
    print(f"Is cuda available? {cuda_available}")
    return cuda_available

If the issue persists, you may need to check the Kubernetes node configuration and ensure that the NVIDIA device plugin is correctly installed and configured. Would you like more details on any specific part of this process? Sources: - Configuring access to GPUs - Added GPU enabled sandbox image Slack Link https://.slack.com/archives/CP2HDHKE1/p1727359165.903949 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.