Deriving Docker images from the nvcr.io/nvidia/tensorflow:<xx.xx>-tf2-py3
Docker images.
For details about the specific images defined by the tags checkout https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/index.html.
Run nvidia-smi
on your system and check the output. Here an example from my system.
$ nvidia-smi
Sat Mar 4 22:03:47 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
Select a Docker image tag from the repository with a CUDA version less or equal to the CUDA version listed from the nvidia-smi
output.
On my system I select an image tag 22.10-tf2-py3
using CUDA 11.8.0
, see https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/rel-22-10.html#rel-22-10.
Run the following command to build the image.
cd ./examples/pytorch
docker compose build
After the image has been built successfully, start the container. The command below will make the GPU with index 0
accessible in the container.
cd ./examples/pytorch
GPU_ID=0 docker compose up
Open JupyterLab from the link in the terminal output and run the cells of the Jupyter notebook provided check-gpu-support.ipynb
.
To stop the container you can press CMD/CTRL+C
.
Run docker compose down
for stopping and removing the container.
cd ./examples/pytorch
docker compose down