Search Results for "gpu"

...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 software components needed to provision GPU. These components include the NVIDIA drivers (to enable CUDA), Kubernetes device plugin for GPUs, the NVIDIA Container Runtime, automatic node labeling, DCGM-based monitoring, and others.

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.

...It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. KServe is being used across various organizations.

OpenCost is a vendor-neutral open-source project for measuring and allocating cloud infrastructure and container costs in real-time. Built by Kubernetes experts and supported by Kubernetes practitioners, OpenCost shines a light into the black box of Kubernetes spending. Flexible, customizable cost allocation and cloud resource monitoring for accurate showback, chargeback, and ongoing reporting. Dynamic asset pricing, through integrations with AWS, Azure, and GCP billing APIs as well as...

...CNCF Project - Control your data - Cut dependence from the tech giants - Enable digital sovereignty - Use modern tools and APIs with any server - Fully utilize GPU resources for AI Who is it for? Banks and fintechs, AI-driven companies, service providers and data centers, governances, regulated and privacy-conscious organizations, integrators and independent IT consultants

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.

The Azure Container Service Engine (acs-engine) generates ARM (Azure Resource Manager) templates for Docker enabled clusters on Microsoft Azure with your choice of DC/OS, Kubernetes, OpenShift, Swarm Mode, or Swarm orchestrators. The input to the tool is a cluster definition. The cluster definition (or apimodel) is very similar to (in many cases the same as) the ARM template syntax used to deploy a Microsoft Azure Container Service cluster.