/ml-env

Docker image with machine learning environment.

Primary LanguagePython

ML environment Docker image

Docker image with CUDA 8, cuDNN 5.1.10, Python 3, Theano, Keras, OpenCV, Matplotlib and Jupyter Notebook installed. GPU acceleration available if you use nvidia-docker.

Requirements

  • Installed Docker
  • If want to use GPU: installed nvidia-docker plugin for Docker and NVIDIA GPU drivers

Building image

docker build -t ml-env .

where ml-env can be replaced with any other name.

Running container

nvidia-docker run -it --rm -p 8888:8888 --volume $(pwd):/host_dir --workdir /host_dir ml-env bash

This command creates and starts container from previously built image (ml-env) with current host directory mounted as /host_dir and set as working directory.

What does this weird parameters mean?

  • -it - redirects std and stdout from container to your terminal.
  • --rm - removes container after exit.
  • -p 8888:8888 - exposes port 8888 (in:out) from container to host. Required by Jupyter Notebook.
  • --volume $(pwd):/host_dir - mounts current host directory ($(pwd)) in container in /host_dir. Instead of $(pwd) you can put any other path.
  • --workdir /host_dir - sets current directory in container to previously mounted.
  • ml-env - name of image to create container from.

Running directly Python or Jupyter

nvidia-docker run -it --rm -p 8888:8888 --volume $(pwd):/host_dir --workdir /host_dir ml-env python3 /host_dir/hello_world.py

or

nvidia-docker run -it --rm -p 8888:8888 --volume $(pwd):/host_dir --workdir /host_dir ml-env jupyter notebook

or any other command at the end instead of python3 or jupyter.

If you need Jupyter secured by password, run command below and enter password:

nvidia-docker run -it --rm -p 8888:8888 --volume $(pwd):/host_dir --workdir /host_dir ml-env run_secured_jupyter.sh