xychelsea/tensorflow repository overview

⁠TensorFlow in Anaconda

TensorFlow⁠ is an open source platform for machine learning. It provides tools, libraries and community resources for researcher and developers to build and deploy machine learning applications. Anaconda⁠ is an open data science platform based on Python 3. This container installs TensorFlow through the conda command with Anaconda 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 TensorFlow⁠ pre-installed through Anaconda⁠.

⁠Getting the containers

⁠Vanilla TensorFlow

The base container, based on the xychelsea/anaconda3:latest Anaconda 3 container stack⁠ (xychelsea/anaconda3:latest) running Tini shell. For the container with a /usr/bin/tini entry point, use:

docker pull xychelsea/tensorflow:latest

With Jupyter Notebooks server pre-installed, pull with:

docker pull xychelsea/tensorflow:latest-jupyter

⁠TensorFlow with NVIDIA/CUDA GPU support

Modified versions 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/tensorflow:latest-gpu

With Jupyter Notebooks server pre-installed, pull with:

docker pull xychelsea/tensorflow:latest-gpu-jupyter

⁠Running the containers

⁠Vanilla TensorFlow

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

With Jupyter Notebooks server pre-installed, run with:

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

⁠TensorFlow with NVIDIA/CUDA GPU support

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

With Jupyter Notebooks server pre-installed, run with:

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

⁠Building the containers

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

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

⁠Vanilla TensorFlow

The base container, based on the xychelsea/anaconda3:latest Anaconda 3 container stack⁠ (xychelsea/anaconda3:latest) running Tini shell:

docker build -t xychelsea/tensorflow:latest -f Dockerfile .

With Jupyter Notebooks server pre-installed, build with:

docker build -t xychelsea/tensorflow:latest-jupyter -f Dockerfile.jupyter .

⁠TensorFlow with NVIDIA/CUDA GPU support

docker build -t xychelsea/tensorflow3:latest-gpu -f Dockerfile.nvidia .

With Jupyter Notebooks server pre-installed, build with:

docker build -t xychelsea/tensorflow:latest-gpu-jupyter -f Dockerfile.nvidia-jupyter .

⁠References

TensorFlow⁠

NVIDIA CUDA container⁠

Anaconda 3⁠