matifali/dockerdl repository overview

⁠DockerDL

⁠Deep Learning Docker Image

Don't waste time on setting up a deep learning environment while you can get a deep learning environment with everything pre-installed.

⁠List of Packages installed

• conda⁠
• Jupyter lab⁠
• Matplotlib⁠
• NLTK⁠
• Numpy⁠
• Pandas⁠
• Plotly⁠
• PyTorch⁠
• Scikit-Learn⁠
• Seaborn⁠
• TensorFlow⁠
• zellij⁠

⁠Image variants and tags

Variant	Tag	Conda	PyTorch	TensorFlow	Image size
Conda	conda	:heavy_check_mark:	:x:	:x:
Tensorflow	tf	:x:	:x:	:heavy_check_mark:
PyTorch	torch	:x:	:heavy_check_mark:	:x:
PyTorch + Tensorflow	tf-torch, latest	:x:	:heavy_check_mark:	:heavy_check_mark:
PyTorch + Tensorflow + Conda	tf-torch-conda	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:

You can see the full list of tags https://hub.docker.com/r/matifali/dockerdl/tags⁠.

⁠Requirements

1. Docker⁠
2. nvidia-container-toolkit⁠ ^1⁠
3. Linux, or Windows with WSL2⁠

⁠Fast Start

docker run --gpus all --rm -it -h dockerdl matifali/dockerdl bash

⁠JupyterLab server without conda

docker run --gpus all --rm -it -h dockerdl -p 8888:8888 matifali/dockerdl jupyter lab --no-browser --port 8888 --ServerApp.token='' --ip='*'

Connect by opening http://localhost:8888⁠ in your browser.

⁠Customize the image

⁠Clone the repo

git clone https://github.com/matifali/dockerdl.git

⁠Add or delete packages

Modify the corresponding [Dockerfile] to add or delete packages.

⁠Build

The following --build-arg are available for the dockerdl-base image.

⁠Step 1

Build the base image

docker build -t dockerdl-base:latest --build-arg USERNAME=coder --build-arg CUDA_VER=12.4.1 --build-arg UBUNTU_VER=22.04 -f base.Dockerfile .

⁠Step 2

Build the image you want with the base image as the base image.

docker build -t dockerdl:tf --build-arg TF_VERSION=2.12.0 -f tf.Dockerfile .

or

docker build -t dockerdl:torch --build-arg -f torch.Dockerfile .

⁠How to connect

⁠VS Code

1. Install vscode⁠.
2. Install the following extensions:
   1. Docker⁠.
   2. Python⁠.
   3. Remote Development⁠.
3. Follow the instructions here⁠.

⁠Coder

1. Install Coder. (https://github.com/coder/coder⁠).
2. Use deeplearning template which references these images (https://github.com/matifali/coder-templates/tree/main/deeplearning⁠).

⁠JetBrains PyCharm Professional

Follow the instructions here⁠.

⁠Issues

If you find any issue please feel free to create an issue⁠ and submit a PR.

⁠Support

• Give a star (⭐) if using this has helped you.
• 

⁠References

⁠Footnotes

1. This image is based on nvidia/cuda⁠ and uses nvidia-container-toolkit⁠ to access the GPU. ↩⁠