Deep-Learning-Workstation-Setup

TensorFlow example

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.

Building the image

Run the following command to build the image.

cd ./examples/pytorch
docker compose build

Start the container and run the Jupyter notebook

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.

Removing the container(s)

Run docker compose down for stopping and removing the container.

cd ./examples/pytorch
docker compose down