Skip to main content

Tercen developer’s guide

14 GPU Development in Tercen Studio

GPU support is now available in Tercen Studio. Follow the steps below to enable and test GPU access in your local setup.

14.1 Setup Instructions

  1. Pull the latest version of the Tercen Studio repository:
    https://github.com/tercen/tercen_studio

  2. Configure Docker for GPU support. Modify the docker-compose.yaml file as described below:

    1. Comment out the default runtime-docker image.
    #image: docker:24.0.6-dind-alpine3.18
    1. Uncomment GPU-related configuration.

    Under the dind service:

    image: tercen/nvidia-dind:12.1.0-runtime-ubuntu22.04
deploy:
  resources:
    reservations:
      devices:
        - driver: nvidia
          count: 1
          capabilities: [ gpu ]

    Under the VS Code service code-server:

    deploy:
  resources:
    reservations:
      devices:
        - driver: nvidia
          count: 1
          capabilities: [ gpu ]

Once the containers are up, you can access the GPU from Code Server.

14.2 Changes to operators

  • The Dockerfile uses a GPU-enabled runtime:
FROM tercen/runtime-tf:2.17.0-gpu
  • The operator.json declares GPU capability:
"capabilities": ["gpu"]

14.3 Verifying GPU Access

Once inside the container, verify GPU access using TensorFlow or PyTorch.

14.3.1 TensorFlow 

import tensorflow as tf
print("Num GPUs Available:", len(tf.config.list_physical_devices('GPU')))

14.3.2 PyTorch 

import torch
print("Is CUDA available:", torch.cuda.is_available())

If GPU is not detected, you might need to install some drivers and dependencies on your machine. Please refer to Docker’s GPU support documentation:
https://docs.docker.com/desktop/features/gpu/