wudaown/docker-gpu-jupyter-keras-pytorch

GPU Jupyter Keras Pytorch Docker Image

A Docker image for GPU-enabled Keras and PyTorch notebook. Image is loaded with CUDA 10.0.

Requirements

NVIDIA drivers, Docker and NVIDIA Docker are assumed to be properly installed.

Installation

Once all dependencies are properly installed, the docker image can be simply "installed" with command:

$ docker pull wudaown/gpu-jupyter-keras-pytorch:latest

Note that this is also the command for upgrading.

Alternatively, one can directly run

$ nvidia-docker run -it --rm wudaown/gpu-jupyter-keras-pytorch:latest nvidia-smi

A docker pull will be automatically triggered by this command. This will show a summary table for the NVIDIA GPU status if the docker image is successfully running on your machine.

Usage

Launch the container with jupyter in background:

$ nvidia-docker run --it -d -p 8888:8888 -v /path/to/persistent/dir:/root/workspace wudaown/gpu-jupyter-keras-pytorch

where -p 8888:8888 denotes the port mapping from host to container in the format of -p hostPort:containerPort.

By default, this will use TensorFlow as backend. If you prefer theano as backend, you can add an environment variable with:

$ nvidia-docker run -it --rm -e KERAS_BACKEND='theano' wudaown/gpu-jupyter-keras-pytorch bash