The aiWARE platform can take advantage of graphics processing units (GPUs). This page shows the commands you will run to set up an Nvidia GPU-aware environment (via a Docker container). When the setup is complete, you can install aiWARE components (such as AI Processing) into that environment, and then GPU-aware cognitive engines can take advantage of GPU-based processing.
See also: https://github.com/NVIDIA/nvidia-container-runtime
Nvidia capabilities:
- compute: required for CUDA and OpenCL applications.
- compat32: required for running 32-bit applications.
- graphics: required for running OpenGL and Vulkan applications.
- utility: required for using nvidia-smi and NVML.
- video: required for using the Video Codec SDK.
- display: required for leveraging X11 display.
Run the following commands in a terminal:
# Fetch required pieces
curl -s -L https://nvidia.github.io/nvidia-container-runtime/gpgkey | apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-container-runtime/$distribution/nvidia-container-runtime.list | tee /etc/apt/sources.list.d/nvidia-container-runtime.list
apt-get update && apt-get upgrade -y
apt-get install -y nvidia-cuda-toolkit nvidia-driver-450 nvidia-container-runtime docker.io
service docker restart
# Validate Nvidia install
nvcc --version
# Test it
docker run -it --rm --gpus all ubuntu nvidia-smi
# Check system
nvidia-container-cli info # Get host OS
docker run -it --rm --gpus all ubuntu nvidia-smi # Validate Docker can run it
NVRM version: 450.80.02
CUDA version: 11.0
Device Index: 0
Device Minor: 0
Model: Tesla K80
Brand: Tesla
GPU UUID: GPU-2105a3c9-41dd-26fa-c8cd-5918c988ec90
Bus Location: 00000000:00:1e.0
Architecture: 3.7
