Docker's support for GPU acceleration is primarily implemented through the NVIDIA Docker plugin. This plugin was specifically developed to enable Docker containers to access NVIDIA GPUs on the host machine.
Specifically, NVIDIA Docker allows users to run applications requiring high-performance graphics processing, such as deep learning and data analysis workloads, within Docker containers. This is achieved through a technique called GPU pass-through, which directly exposes NVIDIA GPU resources from the host machine to Docker containers.
For example, suppose we want to run a TensorFlow deep learning model within a Docker container and leverage the GPU to accelerate the training process. First, we need to ensure the host machine has NVIDIA drivers and the CUDA toolkit installed. Next, we can use the NVIDIA Docker plugin to launch a Docker container and configure the appropriate runtime options to grant GPU access:
docker run --gpus all -it tensorflow/tensorflow:latest-gpu ...
The --gpus all parameter instructs Docker to allocate all available GPUs to the container. With this approach, TensorFlow can train models within the container using GPU acceleration.
In summary, through NVIDIA Docker, Docker's support for GPU acceleration significantly simplifies deploying and running GPU-intensive applications in containers while ensuring application portability and host security.