Getting ready

Setting up all three packages could be quite cumbersome depending on the operating system utilized. The following dockerfile code can be used to set up an environment with tensorflow, mxnet with GPU, and h2o installed with all the dependencies:

FROM chstone/mxnet-gpu:latest
MAINTAINER PKS Prakash <prakash5801>


# Install dependencies
RUN apt-get update && apt-get install -y
 python2.7 
 python-pip 
 python-dev 
 ipython 
 ipython-notebook 
 python-pip 
 default-jre


# Install pip and Jupyter notebook
RUN pip install --upgrade pip && 
 pip install jupyter

# Add R to Jupyter kernel 
RUN Rscript -e "install.packages(c('repr', 'IRdisplay', 'crayon', 'pbdZMQ'), dependencies=TRUE, repos='https://cran.rstudio.com')" && 
 Rscript -e "library(devtools); library(methods); options(repos=c(CRAN='https://cran.rstudio.com')); devtools::install_github('IRkernel/IRkernel')" && 
 Rscript -e "library(IRkernel); IRkernel::installspec(name = 'ir32', displayname = 'R 3.2')" 

# Install H2O
RUN Rscript -e "install.packages('h2o', dependencies=TRUE, repos='http://cran.rstudio.com')"

# Install tensorflow fixing the proxy port
RUN pip install tensorflow-gpu
RUN Rscript -e "library(devtools); devtools::install_github('rstudio/tensorflow')"

The current image is created on top of the chstone/mxnet-gpu Docker image.