This Dockerfile sets up a complete environment for experimenting with R, Python and the most popular Deep Learning libraries (Tensorflow, Keras, Theano).
It installs:
- R 4.0.3 (r-base)
- Python 3.7
- Miniconda 3
- Jupyter notebook for Python
It additionally installs the following packages
- h5py
- pandas
- pygpu==0.6.2
- nose
- mkl
- six
- pyyaml
- keras==2.2.4
- tensorflow-gpu==1.13.1
- ipyparallel
- jupyter
- matplotlib
- seaborn
- scikit-learn
- scikit-cuda
- plotly
- r-base-dev and [https://packages.ubuntu.com/xenial/r-recommended r-recommended] Ubuntu packages
- yhatr
- forecast
- stringr
- randomForest
- lubridate
- rpart
- e1071
- kknn
- ggplot2
- plyr
- reshape2
- devtools
- dse
- autoencoder
- pls
- MTS
- rnn
- feather
- data.table
- dplyr
- ranger
- zoo
- plotly
- gmatrix
- HiPLARM
- HiPLARb
- onlinePCA
- gputools
- gbonte/gbcode (Github)
- rstudio/keras@2.2.4.1 (Github)
- IRKernel/IRKernel (Github)
The image is based on the nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04 from the official NVIDIA CUDA Dockerhub including CUDA 10.0 and cuDNN7, with the respective headers.
git clone https://github.com/jdestefani/gpuDL-docker.git
cd gpuDL-docker
nvidia-docker run -it -v `pwd`/docker_volume:/root/shared_data -p #PORT#:8888 jdestefani/gpu_dl:latest
Note:
- Important The docker depends on nvidia-docker to access the underlying NVIDIA hardware. Running with regular docker will not allow to use GPU.
- The "-v
pwd
:/root/shared_data" shares the folderdocker_volume
on your computer (the 'host') with the container in the '/root/shared_data' folder - Ports are shared as follows:
- 8888 bridges to the Jupyter Notebook
#PORT#
should be replaced with the port on which the default jupyter port (8888) should be redirected.
git clone https://github.com/jdestefani/gpuDL-docker.git
From Dockerfile folder, run
docker build --rm=true -t gpu_dl .
It may take about 30 minutes to complete.
nvidia-docker run -it -v `pwd`/docker_volume:/root/shared_data -p #PORT#:8888 gpu_dl
- Make sure that git and nvidia-docker are installed.
- Run docker container in interactive mode (
-it
cf. Quickstart) - Run the following commands and check the correct execution of the code:
cd shared_data/samples
python keras_CNN_MNIST.py
Rscript keras_CNN_MNIST.R