A Dockerfile for accelerated research process, consists of major machine learning libraries.
- Ubuntu 18.04
- Python 3.6 (Miniconda 3)
- GPU accelerated (CUDA 10.0, cuDNN 7)
- NCCL, CNMeM, Apex (PyTorch only) activated
- Jupyter and OpenCV 3.0 included
- Additional packages (Tensorboard, Hyperdash, etc)
- TensorFlow
uetchy/ml:tensorflow
- PyTorch
uetchy/ml:pytorch
- Chainer
uetchy/ml:chainer
- MXnet
uetchy/ml:mxnet
- XGBoost
uetchy/ml:xgboost
- Docker
- CUDA-enabled GPUs
- CUDA Driver
- CUDA Toolkit
- nvidia-docker2 (https://github.com/NVIDIA/nvidia-docker)
Pull the docker image from DockerHub
docker pull uetchy/ml:tensorflow
docker pull uetchy/ml:pytorch
docker pull uetchy/ml:chainer
...
docker run --runtime=nvidia -v $PWD:/workspace -p 8888:8888 -it uetchy/ml:pytorch jupyter
open http://localhost:8888
docker run --runtime=nvidia --rm -it uetchy/ml:base python
docker run --runtime=nvidia --rm -it uetchy/ml:tensorflow
PRs are accepted.
- Yasuaki Uechi
- UpmostScarab
- cyrusmvahid