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Last pushed: a month ago
Short Description
My deep learning Docker with Torch/CUDA
Full Description

Torch Docker image

Ubuntu 14.04 + Torch + CUDA + cuDNN


In order to use this image you must have Docker Engine installed. Instructions for setting up Docker Engine are available on the Docker website.


This image can be built on top of multiple different base images derived from Ubuntu 14.04. Which base you choose depends on whether you have an NVIDIA graphics card which supports CUDA and you want to use GPU acceleration or not.

If you are running Ubuntu, you can install proprietary NVIDIA drivers from the PPA and CUDA from the NVIDIA website. These are only required if you want to use GPU acceleration

Firstly ensure that you have a supported NVIDIA graphics card with the appropriate drivers and CUDA libraries installed.

Build the image using the following command:

./ && docker build -t gforge/nnx-torch nnx-torch

You will also need to install nvidia-docker, which we will use to start the container with GPU access. This can be found at NVIDIA/nvidia-docker.


iTorch notebook
NV_GPU=0 nvidia-docker run --rm -it --volume=/path/to/notebook:/root/notebook \
  --env=JUPYTER_PASSWORD=my_password --publish=8888:8888 gforge/nnx-torch

Replace /path/to/notebook with a directory on the host machine that you would like to store your work in.

Point your web browser to localhost:8888 to start using the iTorch notebook.

Custom configuration

You can create a notebook.json config file to customise the editor. Some of the options you can change are documented at

Let's say that you create the following file at /path/to/notebook.json:

  "CodeCell": {
    "cm_config": {
      "lineNumbers": false,
      "indentUnit": 2,
      "tabSize": 2,
      "indentWithTabs": false,
      "smartIndent": true

Then, when running the container, pass the following option to mount the configuration file into the container:


You should now notice that your notebooks are configured accordingly.

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