NVIDIA/nvidia-container-toolkit
Nvidia Container Toolkit
The NVIDIA GPU Container Toolkit is a set of tools designed to enable NVIDIA GPU acceleration within OCI compliant containers for compute and graphics workloads. It functions as an OCI container runtime wrapper and a Linux GPU driver bridge, mapping host NVIDIA GPU drivers into the container namespace to provide direct hardware access.
The toolkit includes a GPU accelerated container builder for creating images pre-configured to utilize hardware acceleration. This allows for the integration of graphics processing units into the container environment for both building and running compute-heavy workloads.
The project covers a range of high-performance computing domains, including AI model training workflows, GPU-based cloud computing, and scientific simulations.
Features
- GPU-Accelerated Container Runs - Enables the launching and execution of containers with direct access to host GPU hardware for accelerated processing.
- Containerized GPU Acceleration - Configures container runtimes to interface with host NVIDIA graphics drivers for increased compute performance.
- GPU Runtime Wrappers - Functions as an OCI container runtime wrapper that automatically exposes host GPUs to isolated environments.
- GPU Container Toolkits - Provides a comprehensive set of tools to enable NVIDIA GPU acceleration within OCI compliant containers.
- Image Builders - Provides a GPU-accelerated container builder to create images pre-configured for hardware acceleration.
- Driver Bridges - Implements a Linux GPU driver bridge that maps host NVIDIA drivers into the container namespace.
- GPU-Accelerated Training - Supports the scaling of deep learning model training by providing the necessary GPU acceleration within containers.
- Cloud Native GPU Orchestration - Allows hardware-accelerated workloads to be deployed across cloud clusters using standard container orchestration.
- High-Performance Computing - Provides the execution environment for computationally intensive scientific simulations and data processing tasks using GPUs.
