NVIDIA, Red Hat, and others in the community have collaborated on creating the GPU Operator.
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The NVIDIA GPU Operator introduced here is based on the operator framework and automates the management of all NVIDIA software components needed to provision GPUs within Kubernetes. The Operator Framework within Kubernetes takes operational business logic and allows the creation of an automated framework for the deployment of applications within Kubernetes using standard Kubernetes APIs and kubectl. However, configuring and managing nodes with these hardware resources requires configuration of multiple software components such as drivers, container runtimes or other libraries which are difficult and prone to errors. Kubernetes provides access to special hardware resources such as NVIDIA GPUs, NICs, Infiniband adapters and other devices through the device plugin framework. Kubernetes has quickly become the platform of choice for deploying complex applications built on top of numerous microservices due to its rich set of APIs, reliability, scalability and performance features.
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Containers optimized for NVIDIA GPUs and systems such as the DGX and OEM NGC-Ready servers are available as part of NGC.īut provisioning servers with GPUs reliably and scaling AI applications can be tricky. Over the last few years, NVIDIA has leveraged GPU containers in a variety of ways for testing, development and running AI workloads in production at scale.