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NVIDIA AI Cluster Runtime (AICR) is an emerging project within NVIDIA’s AI infrastructure ecosystem focused on enabling advanced AI compute and runtime workflows, though publicly available documentation remains limited. Based on its positioning within NVIDIA’s repositories, it is designed to support scalable AI runtime environments, potentially addressing challenges related to orchestration, resource management, or reproducible AI execution. The project likely aligns with NVIDIA’s broader...

Kubernetes provides access to special hardware resources such as NVIDIA GPUs, NICs, Infiniband adapters and other devices through the device plugin framework. However, configuring and managing nodes with these hardware resources requires the configuration of multiple software components such as drivers, container runtimes or other libraries which are difficult and prone to errors. The NVIDIA GPU Operator uses the operator framework within Kubernetes to automate the management of all NVIDIA...

The NVIDIA device plugin for Kubernetes is a Daemonset that allows you to automatically Expose the number of GPUs on each node of your cluster. Keep track of the health of your GPUs. Run GPU-enabled containers in your Kubernetes cluster.

JAX Toolbox is a development toolkit designed to streamline and optimize the use of JAX for machine learning and high-performance computing on NVIDIA GPUs. It provides prebuilt Docker images, continuous integration pipelines, and optimized example implementations that help developers quickly set up and run JAX workloads without complex configuration. The project supports popular JAX-based frameworks and models, including architectures used for large-scale pretraining such as GPT and LLaMA...

The NVIDIA Container Toolkit allows users to build and run GPU accelerated Docker containers. The toolkit includes a container runtime library and utilities to automatically configure containers to leverage NVIDIA GPUs. Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed.