gpucrate

gpucrate creates hard-linked GPU driver (currently just NVIDIA) volumes for use with docker, singularity, etc. This allows the exact system drivers to be linked into a container without needing to maintain a separate container per driver version.

Installation

To install gpucrate use the pip command:

$ pip install gpucrate

or in a virtual environment:

$ virtualenv gpucrate
$ source gpucrate/bin/activate
$ pip install gpucrate

Usage

To create a driver volume for your system's current GPU driver:

$ sudo gpucrate create

This will create a hard-linked driver volume directory in /usr/local/gpucrate by default that can be used to link the drivers into a container. Here's an example volume for driver version 367.48:

$ find /usr/local/gpucrate/367.48/
/usr/local/gpucrate/367.48/
/usr/local/gpucrate/367.48/bin
/usr/local/gpucrate/367.48/bin/nvidia-cuda-mps-server
/usr/local/gpucrate/367.48/bin/nvidia-debugdump
/usr/local/gpucrate/367.48/bin/nvidia-persistenced
/usr/local/gpucrate/367.48/bin/nvidia-cuda-mps-control
/usr/local/gpucrate/367.48/bin/nvidia-smi
/usr/local/gpucrate/367.48/lib
/usr/local/gpucrate/367.48/lib64
/usr/local/gpucrate/367.48/lib64/libnvcuvid.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-ml.so.1
/usr/local/gpucrate/367.48/lib64/libnvidia-eglcore.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-glcore.so.367.48
/usr/local/gpucrate/367.48/lib64/libcuda.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-opencl.so.1
/usr/local/gpucrate/367.48/lib64/libnvcuvid.so.1
/usr/local/gpucrate/367.48/lib64/libnvidia-ifr.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-ml.so.367.48
/usr/local/gpucrate/367.48/lib64/libcuda.so.1
/usr/local/gpucrate/367.48/lib64/libnvidia-encode.so.1
/usr/local/gpucrate/367.48/lib64/libnvidia-tls.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-egl-wayland.so.367.48
/usr/local/gpucrate/367.48/lib64/libOpenGL.so.0
/usr/local/gpucrate/367.48/lib64/libcuda.so
/usr/local/gpucrate/367.48/lib64/libnvidia-compiler.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-fatbinaryloader.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-opencl.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-ptxjitcompiler.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-fbc.so.1
/usr/local/gpucrate/367.48/lib64/libnvidia-fbc.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-glsi.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-encode.so.367.48
/usr/local/gpucrate/367.48/lib64/libnvidia-ifr.so.1

By default gpucrate creates driver volumes in /usr/local/gpucrate. You can change this via gpucrate's config file:

echo 'volume_root: /path/to/volume/root' > /etc/gpucrate/config.yaml

or via the GPUCRATE_VOLUME_ROOT environment variable:

export GPUCRATE_VOLUME_ROOT="/path/to/volume/root"

Using with Singularity

NOTE: singularity-gpu requires Singularity 2.4+

Once a volume has been created for the currently active driver you can now use the singularity wrapper singularity-gpu to run GPU-enabled containers.

As an example lets convert the tensorflow/tensorflow:latest-gpu docker image to a singularity image:

$ singularity build tensorflow.img docker://tensorflow/tensorflow:latest-gpu

Now use the singularity-gpu wrapper to run any singularity command as normal only with the host's exact GPU driver linked in:

$ singularity-gpu exec tensorflow.img python -c 'import tensorflow'
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally

By default singularity-gpu injects the required environment for NVIDIA/CUDA inside the container at run time. If this causes issues or you'd like to disable this for any reason set the following in the gpucrate config file:

echo 'manage_environment: false' > /etc/gpucrate/config.yaml

or use the GPUCRATE_MANAGE_ENVIRONMENT environment variable:

export GPUCRATE_MANAGE_ENVIRONMENT="false"

Container Requirements

The singularity-gpu wrapper uses the same conventions as NVIDIA's upstream docker containers:

1. NVIDIA driver volume binds to /usr/local/nvidia inside the container
2. CUDA lives in /usr/local/cuda

If you have enable overlay no in your singularity config you'll need to ensure that /usr/local/nvidia exists inside the container before attempting to use singularity-gpu.

Using with Docker

It's much easier to just use nvidia-docker. If you still insist try this (not tested and you'll need to adjust the devices, volume root, and driver version for your system):

$ docker run -ti --rm \
--device=/dev/nvidiactl \
--device=/dev/nvidia-uvm \
--device=/dev/nvidia0 \
--device=/dev/nvidia1 \
--device=/dev/nvidia2
--device=/dev/nvidia3 \
--volume-driver=nvidia-docker \
--volume=/usr/local/gpucrate/<driver_version>:/usr/local/nvidia:ro nvidia/cuda \
nvidia-smi