This project contains a makefile and dockerfile for using the raflow_next containers. They serve as test environments for Tensorflow 2.1.0, with GPU support, and a vast suite of python packages. The packages include various lung-specific or dicom-specific tools.
This project begins with tensorflow containers. The makefile and dockerfile are based on keras project docker tools, which are no longer maintained. Their documentation is repeated below.
This directory contains Dockerfile to make it easy to get up and running with
Keras via Docker.
General installation instructions are on the Docker site, but we give some quick links here:
We are using Makefile to simplify docker commands within make commands.
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
For GPU support install NVIDIA drivers (ideally latest) and nvidia-docker. Run using
$ make notebook GPU=0 # or [ipython, bash]
Switch between Theano and TensorFlow
$ make notebook BACKEND=theano
$ make notebook BACKEND=tensorflow
Mount a volume for external data sets
$ make DATA=~/mydata
Prints all make tasks
$ make help
You can change Theano parameters by editing /docker/theanorc.
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