jhoeller/rapids-gpu repository overview

⁠NVIDIA RAPIDS AI with GPU

The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs with Dask.

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If you want to skip the build process to save time you can pull the pre-built image from DockerHub here:

https://hub.docker.com/r/jhoeller/rapids-gpu⁠

docker pull jhoeller/rapids-gpu:default

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⁠Features

• Anaconda: Accelerated Python, version 3.7.3
• CuPy: GPU accelerated drop in for Numpy
• RAPIDS AI
• Dask
• MLFlow
• Seldon Core
• And more!

⁠Built in code hinting in Jupyter Notebook

Press tab to see what methods you have access to by clicking tab.

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⁠Before you begin (This might be optional)

Link to nvidia-docker2 install: Tutorial⁠

You must install nvidia-docker2 and all it's deps first, assuming that is done, run:

sudo apt-get install nvidia-docker2

sudo pkill -SIGHUP dockerd

sudo systemctl daemon-reload

sudo systemctl restart docker

How to run this container:

⁠Step 1

docker build -t <container name> . < note the . after

If you get an authorized user from the docker pull cmd inside the container, try:

$ docker logout

...and then run it or pull again. As it is public repo you shouldn't need to login.

⁠Step 2

Run the image, mount the volumes for Jupyter and app folder for your fav IDE, and finally the expose ports 8888 for Jupyter Notebook:

docker run --rm -it --runtime=nvidia --user $(id -u):$(id -g) --group-add container_user --group-add sudo -v "${PWD}:/app" -p 8888:8888 -p 6006:6006 <container name>

⁠Step 3: Check to make sure GPU drivers and CUDA is running

• Open another ssh tab, and exec into the container and check if your GPU is registering in the container and CUDA is working:

• Get the container id:

docker ps

• Exec into container:

docker exec -u root -t -i <container id> /bin/bash

• Check if NVIDIA GPU DRIVERS have container access:

nvidia-smi

• Check if CUDA is working:

nvcc -V

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⁠Known conflicts with nvidia-docker and Ubuntu

AppArmor on Ubuntu has sec issues, so remove docker from it on your local box, (it does not hurt security on your computer):

sudo aa-remove-unknown

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