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        "author": "Justin Riley",
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        "description": "# gpucrate\n\n[![build status](https://secure.travis-ci.org/jtriley/gpucrate.png?branch=master)](https://secure.travis-ci.org/jtriley/gpucrate)\n\ngpucrate creates hard-linked GPU driver (currently just NVIDIA) volumes for use\nwith docker, singularity, etc. This allows the exact system drivers to be\nlinked into a container without needing to maintain a separate container per\ndriver version.\n\n## Installation\nTo install gpucrate use the `pip` command:\n\n```\n$ pip install gpucrate\n```\n\nor in a [virtual environment](https://virtualenv.pypa.io/en/stable/):\n\n```\n$ virtualenv gpucrate\n$ source gpucrate/bin/activate\n$ pip install gpucrate\n```\n\n## Usage\nTo create a driver volume for your system's current GPU driver:\n\n```\n$ sudo gpucrate create\n```\n\nThis will create a hard-linked driver volume directory in `/usr/local/gpucrate`\nby default that can be used to link the drivers into a container. Here's an\nexample volume for driver version `367.48`:\n\n```\n$ find /usr/local/gpucrate/367.48/\n/usr/local/gpucrate/367.48/\n/usr/local/gpucrate/367.48/bin\n/usr/local/gpucrate/367.48/bin/nvidia-cuda-mps-server\n/usr/local/gpucrate/367.48/bin/nvidia-debugdump\n/usr/local/gpucrate/367.48/bin/nvidia-persistenced\n/usr/local/gpucrate/367.48/bin/nvidia-cuda-mps-control\n/usr/local/gpucrate/367.48/bin/nvidia-smi\n/usr/local/gpucrate/367.48/lib\n/usr/local/gpucrate/367.48/lib64\n/usr/local/gpucrate/367.48/lib64/libnvcuvid.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-ml.so.1\n/usr/local/gpucrate/367.48/lib64/libnvidia-eglcore.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-glcore.so.367.48\n/usr/local/gpucrate/367.48/lib64/libcuda.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-opencl.so.1\n/usr/local/gpucrate/367.48/lib64/libnvcuvid.so.1\n/usr/local/gpucrate/367.48/lib64/libnvidia-ifr.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-ml.so.367.48\n/usr/local/gpucrate/367.48/lib64/libcuda.so.1\n/usr/local/gpucrate/367.48/lib64/libnvidia-encode.so.1\n/usr/local/gpucrate/367.48/lib64/libnvidia-tls.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-egl-wayland.so.367.48\n/usr/local/gpucrate/367.48/lib64/libOpenGL.so.0\n/usr/local/gpucrate/367.48/lib64/libcuda.so\n/usr/local/gpucrate/367.48/lib64/libnvidia-compiler.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-fatbinaryloader.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-opencl.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-ptxjitcompiler.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-fbc.so.1\n/usr/local/gpucrate/367.48/lib64/libnvidia-fbc.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-glsi.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-encode.so.367.48\n/usr/local/gpucrate/367.48/lib64/libnvidia-ifr.so.1\n```\n\nBy default gpucrate creates driver volumes in `/usr/local/gpucrate`. You can\nchange this via gpucrate's config file:\n\n```\necho 'volume_root: /path/to/volume/root' > /etc/gpucrate/config.yaml\n```\n\nor via the `GPUCRATE_VOLUME_ROOT` environment variable:\n\n```\nexport GPUCRATE_VOLUME_ROOT=\"/path/to/volume/root\"\n```\n\n### Using with Singularity\n**NOTE**: singularity-gpu requires Singularity 2.4+\n\nOnce a volume has been created for the currently active driver you can now use\nthe singularity wrapper `singularity-gpu` to run GPU-enabled containers.\n\nAs an example lets convert the [tensorflow/tensorflow:latest-gpu](https://hub.docker.com/r/tensorflow/tensorflow/)\ndocker image to a singularity image:\n\n```\n$ singularity build tensorflow.img docker://tensorflow/tensorflow:latest-gpu\n```\n\nNow use the `singularity-gpu` wrapper to run any singularity command as normal\nonly with the host's exact GPU driver linked in:\n\n```\n$ singularity-gpu exec tensorflow.img python -c 'import tensorflow'\nI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcublas.so locally\nI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcudnn.so locally\nI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcufft.so locally\nI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcuda.so.1 locally\nI tensorflow/stream_executor/dso_loader.cc:108] successfully opened CUDA library libcurand.so locally\n```\n\nBy default singularity-gpu injects the required environment for NVIDIA/CUDA\ninside the container at run time. If this causes issues or you'd like to\ndisable this for any reason set the following in the gpucrate config file:\n\n```\necho 'manage_environment: false' > /etc/gpucrate/config.yaml\n```\n\nor use the `GPUCRATE_MANAGE_ENVIRONMENT` environment variable:\n\n```\nexport GPUCRATE_MANAGE_ENVIRONMENT=\"false\"\n```\n\n#### Container Requirements\nThe singularity-gpu wrapper uses the same conventions as NVIDIA's upstream\ndocker containers:\n\n1. NVIDIA driver volume binds to /usr/local/nvidia inside the container\n2. CUDA lives in /usr/local/cuda\n\nIf you have `enable overlay no` in your singularity config you'll need to\nensure that /usr/local/nvidia exists inside the container before attempting to\nuse `singularity-gpu`.\n\n### Using with Docker\nIt's much easier to just use [nvidia-docker](https://github.com/NVIDIA/nvidia-docker).\nIf you still insist try this (not tested and you'll need to adjust the devices,\nvolume root, and driver version for your system):\n\n```\n$ docker run -ti --rm \\\n--device=/dev/nvidiactl \\\n--device=/dev/nvidia-uvm \\\n--device=/dev/nvidia0 \\\n--device=/dev/nvidia1 \\\n--device=/dev/nvidia2\n--device=/dev/nvidia3 \\\n--volume-driver=nvidia-docker \\\n--volume=/usr/local/gpucrate/<driver_version>:/usr/local/nvidia:ro nvidia/cuda \\\nnvidia-smi\n```\n",
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