My Docker Setup

This deep learning docker setup includes the sacred library, tensorboard and pytorch.

Setup

Create your own config.mk file with

$ ./generate-config.sh > config.mk

This defines the paths of jupyter, pytorch, mongodb and which volumes to mount. If you have to install some local pip packages you can create a script $HOME/install_local_pip.sh that will be executed everytime you start a new pytorch container. The content of $HOME/install_local_pip.sh could look like this:

#! /usr/bin/env bash

pip install -e ./my_local_project

Commands

$ make build_all: Builds all three images.

$ make run_all: Runs each images.

$ make rm_images: Removes the images.

$ make rm_all_containers: Removes all containers (using force).

$ make zsh: Creates an zsh session in the pytorch container. You can add the following snippet to your .zshrc or .bashrc to autoload the conda enviroment:

if [ -e /opt/conda/envs/dl-docker-py36 ] && [ $DOCKER_DL ]; then
    source /opt/conda/bin/activate py36
fi

Otherwise you can run each time inside of the container:

$ source /opt/conda/bin/activate py36

Forward Ports to Jupyter Notebook / Tensorboard / Sacredboard

The Makefile automatically saves all relevant ports.

You can forward the ports to your machine with with the forward_docker_ports.sh script. On your local machine run:

$ forward_docker_ports.sh <your_hostname>

You can then reach the services at:

Service	Address
Jupyter Notebook	localhost:8000
Tensorboard	localhost:6006
Sacred Board	localhost:5000
Mongo DB Connection	localhost:27017
SSH	localhost:8022