garyfeng/edward_ppl repository overview

⁠Using Edward via Docker

This directory contains Dockerfile to make it easy to get up and running with Edward via Docker⁠.

⁠Installing Docker

General installation instructions are on the Docker site⁠, but we give some quick links here:

• OSX⁠: docker toolbox⁠
• ubuntu⁠

⁠Installing NVIDIA Docker (GPU Environment)

General installation instructions are on the NVIDIA Docker site⁠

⁠Running the container

We are using Makefile to simplify docker commands within make commands.

⁠CPU environment

Build the container and start a jupyter notebook

$ make notebook

Build the container and start an iPython shell

$ make ipython

Build the container and start a bash

$ make bash

Build the container and start a test

$ make test

⁠GPU environment

Build the container and start a jupyter notebook

$ make notebook-gpu

Build the container and start an iPython shell

$ make ipython-gpu

Build the container and start a bash

$ make bash-gpu

Build the container and start a test

$ make test-gpu

For GPU support install NVidia drivers (ideally latest) and nvidia-docker⁠. Run using

$ make notebook-gpu GPU=0 # or [ipython, bash]

Mount a volume for external data sets

$ make DATA=~/mydata

Prints all make tasks

$ make help

Note: If you would have a problem running nvidia-docker you may try the old way we have used. But it is not recommended. If you find a bug in the nvidia-docker report it there please and try using the nvidia-docker as described above.

$ export CUDA_SO=$(\ls /usr/lib/x86_64-linux-gnu/libcuda.* | xargs -I{} echo '-v {}:{}')
$ export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
$ docker run -it -p 8888:8888 $CUDA_SO $DEVICES gcr.io/tensorflow/tensorflow:latest-gpu