Anaconda 3 Environment for GPU-enabled Docker container

Provides an NVIDIA GPU-enabled container with a lightweight (Miniconda) Anaconda installation. Anaconda is an open data science platform based on Python 3. This container installs Anaconda into the /usr/local/anaconda directory. The default user, anaconda runs a Tini shell /usr/bin/tini, and comes preloaded with the conda command in the environment $PATH. Additional versions with NVIDIA/CUDA support and Jupyter Notebooks tags are available.

Anaconda 3 with conda-forge

This container enables the conda command with a lightweight version of Anaconda (Miniconda) and the conda-forge repository in the /usr/local/anaconda directory. The default user, anaconda runs a Tini shell /usr/bin/tini, and comes preloaded with the conda command in the environment $PATH. Additional versions with NVIDIA/CUDA support and Jupyter Notebooks tags are available.

NVIDIA/CUDA GPU-enabled Containers

Two flavors provide an NVIDIA GPU-enabled container with Anaconda.

Getting the containers

Vanilla Anaconda

The base container, based on the ubuntu:latest from Ubuntu running a Tini shell. For the container with a /usr/bin/tini entry point, use:

docker pull xychelsea/anaconda3:latest

With Jupyter Notebooks server pre-installed, pull with:

docker pull xychelsea/anaconda3:latest-jupyter

Anaconda with NVIDIA/CUDA GPU support

Modified version of nvidia/cuda:latest container, with support for NVIDIA/CUDA graphical processing units through the Tini shell. For the container with a /usr/bin/tini entry point:

docker pull xychelsea/anaconda3:latest-gpu

With Jupyter Notebooks server pre-installed, pull with:

docker pull xychelsea/anaconda3:latest-gpu-jupyter

Running the containers

To run the containers with the generic Docker application or NVIDIA enabled Docker, use the docker run command.

Vanilla Anaconda

docker run --rm -it xychelsea/anaconda3:latest

With Jupyter Notebooks server pre-installed, run with:

docker run --rm -it -d -p 8888:8888 xychelsea/anaconda:latest-jupyter

Anaconda with NVIDIA/CUDA GPU support

docker run --gpus all --rm -it xychelsea/anaconda:latest-gpu /bin/bash

With Jupyter Notebooks server pre-installed, run with:

docker run --gpus all --rm -it -d -p 8888:8888 xychelsea/magenta:latest-gpu-jupyter

Building the containers

To build either a GPU-enabled container or without GPUs, use the xychelsea/anaconda3-docker GitHub repository.

git clone git://github.com/xychelsea/anaconda3-docker.git

Vanilla Magenta

The base container, based on the ubuntu:latest from Ubuntu running Tini shell:

docker build -t anaconda3:latest -f Dockerfile .

With Jupyter Notebooks server pre-installed, build with:

docker build -t anaconda3:latest-jupyter -f Dockerfile.jupyter .

Anaconda with NVIDIA/CUDA GPU support

docker build -t anaconda3:latest-gpu -f Dockerfile.nvidia .

With Jupyter Notebooks server pre-installed, build with:

docker build -t anaconda3:latest-gpu-jupyter -f Dockerfile.nvidia-jupyter .

Environment

The default environment uses the following configurable options:

ANACONDA_DIST=Miniconda3
ANACONDA_PYTHON=py38
ANACONDA_CONDA=4.8.3
ANACONDA_OS=Linux
ANACONDA_ARCH=x86_64
ANACONDA_VERSION=$ANACONDA_DIST-$ANACONDA_PYTHON_$ANACONDA_CONDA-$ANACONDA_OS-$ANACONDA_ARCH
ANACONDA_GID=100
ANACONDA_PATH=/usr/local/anaconda3
ANACONDA_UID=1000
ANACONDA_USER=anaconda
ANACONDA_ENV=magenta
HOME=/home/$ANACONDA_USER
LANG=en_US.UTF-8
LANGUAGE=en_US.UTF-8
LC_ALL=en_US.UTF-8
SHELL=/bin/bash

References

• Anaconda 3
• conda-forge