Use GPUs in Containers
To take advantage of the performance benefits offered by graphics processing units (GPUs), you can use the GPUs of the host system in a container. By default, a container does not have access to hardware resources of its host. To enable the container to access the NVIDIA® GPUs of the host system, you need to:
Run the container on a host system with the appropriate NVIDIA GPU drivers installed, for example use the NVIDIA Deep Learning AMI.
Make the GPUs of the host visible to the container by using the
--gpusflag when you execute the
docker runcommand. Set this flag to
allif you want the container to have access to all the GPUs of the host machine. For example, run a MATLAB® container and give it access to the GPUs of the host by executing a command like the one below
docker run --gpus all mathworks/matlab
Check Container Access to GPUs
To check if your container has access to the GPUs of the host, open MATLAB in the container and execute the following command
ans=2×5 table Index Name ComputeCapability DeviceAvailable DeviceSelected _____ _____________ _________________ _______________ ______________ 1 "TITAN RTX" "7.5" true true 2 "Quadro K620" "5.0" true false
gpuDeviceCountinstead. For more information, on this command and on how to select a specific GPU, see Identify and Select a GPU Device (Parallel Computing Toolbox).
Use GPUs to Speed Up MATLAB Code
After giving the container access to the GPUs, you can speed up your MATLAB code, such as a training neural networks in parallel, by taking advantage of their computational power. To get started, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox). To learn more about using multiple GPUs to train a single neural network in parallel, see Deep Learning with MATLAB on Multiple GPUs (Deep Learning Toolbox).
gpuDeviceTable (Parallel Computing Toolbox)