docker build -t quantscientist/pycuda -f Dockerfile.gpu3 .
nvidia-docker run -it -p 5555:5555 -p 7842:7842 -p 8787:8787 -p 8786:8786 -p 8788:8788 -v ~/db/Dropbox/dev2/:/root/sharedfolder quantscientist/pycuda bash
This repository includes utilities to build and run my NVIDIA Docker image for the Deep Learning School: https://www.meetup.com/TensorFlow-Tel-Aviv/events/241762893/
NOTE: Building this image may take several hours since CMAKE is being built from source. https://github.com/QuantScientist/deep-ml-meetups
Also available on docker hub (Build on docker hub usually failes because of the long build time): https://hub.docker.com/r/quantscientist/deep-learning-boot-camp/
docker pull quantscientist/deep-learning-boot-camp
Please be aware that this project is currently experimental.
Running a CUDA container requires a machine with at least one CUDA-capable GPU and a driver compatible with the CUDA toolkit version you are using.
NVIDIA drivers are backward-compatible with CUDA toolkits versions:
| CUDA toolkit version | Minimum driver version |
|---|---|
| 7.0 | >= 346.46 |
| 7.5 | >= 352.39 |
** We use CUDA 8.0. **
sudo apt-get install nvidia-cuda-toolkit
sudo apt-get install nvidia-nsight
wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb
nvidia-docker run --rm nvidia/cuda nvidia-smi
On top of all the fancy deep learning libraries, this docker image contains:
Ubuntu 16.04 CUDA 8.0 (GPU version only) cuDNN v5 (GPU version only) Tensorflow Theano Keras iPython/Jupyter Notebook Numpy, SciPy, Pandas, Scikit Learn, Matplotlib A few common libraries used for deep learning
- ArrayFire
- PyCUDA
- Python
- LLVM
- LLDB
- Snappy
- Numba
docker build -t quantscientist/pycuda -f Dockerfile.gpu3 .
docker build -t quantscientist/pycuda -f Dockerfile.cpu .
nvidia-docker run -it -p 5555:5555 -p 7842:7842 -p 8787:8787 -p 8786:8786 -p 8788:8788 -v ~/db/Dropbox/dev2/:/root/sharedfolder quantscientist/pycuda bash
docker run -it -p 5555:5555 -p 7842:7842 -p 8787:8787 -p 8786:8786 -p 8788:8788 -v /myhome/data-science/:/root/sharedfolder --env="DISPLAY" quantscientist/pycuda bash
chmod +x run_jupyter.sh ./run_jupyter.sh
OR
docker build -t quantscientist/gpu -f Dockerfile.gpu .
- Please let us know by filing a new issue
- You can contribute by opening a pull request
You will need to send a signed copy of the Contributor License Agreement to digits@nvidia.com before your change can be accepted.