deephdc/tensorflow repository overview

⁠Custom Tensorflow images

The Dockerfiles⁠ to build images are based on the original Tensorflow Dockerfiles⁠. The compatibility of CUDA/CUDNN versions for the GPU images can be found here⁠.

We build cpu/gpu images for Ubuntu 18.04, Python 3.6 and Tensorflow versions 1.10.0/1.12.0, as original Tensorflow images use Ubuntu 16.04 and python 3.5.

⁠Building

The Dockerfiles in the dockerfiles directory must have their build context set to the directory with this README.md to copy in helper files. For example:

$ docker build -f ./dockerfiles/cpu.Dockerfile -t tensorflow .

This builds a tensorflow CPU-based image with the latest TensorFlow version, Ubuntu 18.04, and python3. Each Dockerfile has its own set of available --build-args which are documented in the Dockerfile itself.

PLEASE NOTE, Dockerfiles are preliminary versions and may need further customization!

⁠Running Locally Built Images

After building the image with the tag tensorflow (for example), use docker run to run the images.

Note for new Docker users: the -v and -u flags share directories and permissions between the Docker container and your machine. Without -v, your work will be wiped once the container quits, and without -u, files created by the container will have the wrong file permissions on your host machine. Check out the Docker run documentation⁠ for more info.

# Volume mount (-v) is optional but highly recommended, especially for Jupyter.
# User permissions (-u) are required if you use (-v).

# CPU-based images
$ docker run -u $(id -u):$(id -g) -v $(pwd):/my-devel -it tensorflow

# GPU-based images (set up [nvidia-docker2](https://github.com/NVIDIA/nvidia-docker) first)
$ docker run --runtime=nvidia -u $(id -u):$(id -g) -v $(pwd):/my-devel -it tensorflow

N.B. For either CPU-based or GPU-based images you can also use udocker⁠.

P.S. These images do not come with the TensorFlow source code.