yunseokim/dl-env repository overview

⁠dl-env-docker

Dockerfiles for building a deep learning development environment, which is based on CUDA & cuDNN images provided by the nvidia/cuda repository⁠ on Docker Hub.
For more details, read my blog posts⁠.

⁠Prerequisites

• Systems with NVIDIA GPUs that support CUDA
  • You can check the GPU model name installed in your current computer with the lspci | grep -i nvidia command in the terminal.
  • On the https://docs.nvidia.com/deeplearning/cudnn/latest/reference/support-matrix.html⁠ page, check the supported NVIDIA graphics driver versions, required CUDA Compute Capability conditions, and the list of supported NVIDIA hardware for each cuDNN version.
  • Find the corresponding model name in the GPU list at https://developer.nvidia.com/cuda-gpus⁠, and check the Compute Capability value. This value must meet the CUDA Compute Capability condition checked earlier to use CUDA and cuDNN without issues.
• Host OS with the appropriate version of the graphics driver installed
• NVIDIA Container Toolkit⁠
• Docker Engine⁠

⁠Overview of Dockerfiles

Three flavors of images are provided:

• base: Includes CUDA & cuDNN, setuptools, and pip.
• torch: Builds on the base and includes the JupyterLab, Numpy, Scipy, pandas, Matplotlib, Seaborn, scikit-learn, scikit-image, tqdm, and PyTorch.
• rapids: Builds on the torch and includes the Cupy, RAPIDS packages(cuDF, cuML, cuGraph, cuxfilter, cuCIM, RAFT, cuVS), and DALI.

⁠How to use

⁠Using pre-built images

Pre-built images are available in this repository on Docker Hub.
You can pull the images with the following command.

# Tag(base)
docker pull yunseokim/dl-env:base-cuda12.4.1-cudnn9.1.0-ubuntu22.04

# Tag(torch)
docker pull yunseokim/dl-env:torch-cuda12.4.1-cudnn9.1.0-ubuntu22.04

# Tag(rapids)
docker pull yunseokim/dl-env:rapids-cuda12.4.1-cudnn9.1.0-ubuntu22.04

When you run a container from this prebuilt image, the account password for the 'remote' user is initialized to satisfied-flip-remake. It is extremely vulnerable, so reset the login password immediately after the first run of the container.

⁠Build manually

After downloading the desired Dockerfile from the yunseo-kim/dl-env-docker⁠ GitHub repository, you can build the image yourself by running the following command from the path where the Dockerfile is located.

export DL_ENV_PASSWD="<your_own_password>" && \
docker build -t dl-env:<tag> -f ./Dockerfile \
--secret id=USER_PASSWORD,env=DL_ENV_PASSWD .

Enter the tag of your choice in the <tag> field, and in the <your_own_password> field, enter the login password to be used when remotely login to the 'remote' account with SSH.

⁠Docker run example

• Make sure to add the --gpus all argument to use GPUs in the container runtime.
• Below is an example format for a Docker run. You can customize it to your needs.

docker run -itd --name dl-env --gpus all \
-p <server-port>:<container-jupyter-port> \
-p <server-port>:<container-ssh-port> \
--restart <always> \
-v <volume-name>:<workspace> \
-w <workspace> \
<image-name>:<tag>