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

• TensorFlow
• PyTorch
• Numpy
• Scikit-Learn
• Pandas
• Matplotlib
• Seaborn
• Plotly
• NLTK
• Jupyter notebook/lab
• conda
• mamba (faster than conda) ¹

Image variants and tags

Variant	Tag	Conda	PyTorch	TensorFlow	Image size
Conda	conda	✔️	✔️	✔️
No Conda	no-conda, latest	❌	✔️	✔️
PyTorch	pytorch	❌	✔️	❌
PyTorch (Nightly)	pytorch-nightly	❌	✔️	❌
Tensorflow	tensorflow	❌	❌	✔️

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

Requirements

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

Fast Start

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

Launch a vscode server

docker run --gpus all --rm -it -h dockerdl -p 8000:8000 matifali/dockerdl code-server --accept-server-license-terms serve-local --without-connection-token --quality stable --telemetry-level off

Connect to the server using your browser at http://localhost:8000.

Launch a Jupyter notebook server

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

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.

Build your own

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

Following --build-arg are available:

Argument	Description	Default	Possible Values
USERNAME	User name	coder	Any string or $USER
USERID	User ID	1000	$(id -u $USER)
GROUPID	Group ID	1000	$(id -g $USER)
PYTHON_VER	Python version	3.10	3.10, 3.9, 3.8
CUDA_VER	CUDA version	11.7.1	11.7.0, 11.8.0 etc.
UBUNTU_VER	Ubuntu version	22.04	22.04, 20.04, 18.04
TF_VERSION	TensorFlow version	latest	any version from Pypi3

Note: Not all combinations of --build-arg are tested.

Example 1

Build an image with default settings and your own username and user id.

docker build -t dockerdl:latest /
--build-arg USERNAME=$USER /
--build-arg USERID=$(id -u $USER) /
--build-arg GROUPID=$(id -g $USER) /
-f conda.Dockerfile .

Example 2

Build an image with Python 3.8, TensorFlow 2.6.0, CUDA 11.5.0, Ubuntu 20.04 and no:conda

docker build -t dockerdl:latest /
--build-arg USERNAME=$USER /
--build-arg USERID=$(id -u $USER) /
--build-arg GROUPID=$(id -g $USER) /
--build-arg PYTHON_VER=3.8 /
--build-arg CUDA_VER=11.5.0 /
--build-arg UBUNTU_VER=20.04 /
--build-arg TF_VERSION=2.6.0 /
-f noconda.Dockerfile .

How to connect

JetBrains PyCharm Professional

Follow the instructions here.

VS Code

1. install vscode.
2. Install Docker extension.
3. Install Python extension.
4. install Remote Development extension.
5. Follow the instructions here.

Issues

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

Support

• Please give a star (⭐) if using this has helped you.
• Help the flood victims in Pakistan by donating here.

References

Footnotes

1. mamba is a fast, drop-in replacement for the conda package manager. It is written in C++ and uses the same package format as conda. It is designed to be a drop-in replacement for conda, and can be used as a drop-in replacement for the conda command line client. ↩

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

3. Pypi is the Python Package Index. It is a repository of software for the Python programming language. ↩