NVIDIA GPU accelerated data centers are increasingly being used to run production deep learning and high-performance computing (HPC) applications. Teams of researchers, developers and data scientists share data center resources to design and develop software and algorithms, train deep learning models, run simulations, perform testing and validations, and also deploy applications and models to productions in the same or deployment data centers on-prem or in the cloud.

NVIDIA works closely with its ecosystem partners to provide developers and DevOps with software tools for every step of the AI and HPC software life cycle.

Develop, Train, Simulate

GPU-Optimized Containers

NVIDIA offers GPU-accelerated deep learning and HPC containers from NVIDIA GPU Cloud (NGC) that are optimized to deliver maximum performance on NVIDIA GPUs. The NGC container registry includes NVIDIA tuned, tested, certified, and maintained containers for the top deep learning software like TensorFlow, PyTorch, MXNet, TensorRT, and more. NGC also has third-party managed HPC application containers, and NVIDIA HPC visualization containers. This eliminates the need for developers, data scientists and researchers to manage packages and dependencies or build deep learning frameworks from source.

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Schedule and Orchestrate

NVIDIA Container Runtime

NVIDIA Container Runtime is a GPU aware container runtime, compatible with popular container technologies such as Docker, LXC and CRI-O. It simplifies the process of building and deploying containerized GPU-accelerated applications to desktop, cloud or data centers.

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Kubernetes on NVIDIA GPUs

Kubernetes on NVIDIA GPUs enables enterprises to scale up training and inference deployment to multi-cloud GPU clusters seamlessly. It lets you automate the deployment, maintenance, scheduling and operation of multiple GPU accelerated application containers across clusters of nodes.

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Cluster Management Tools

NVIDIA GPUs are supported by a number of 3rd party schedulers and cluster management software.

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Manage and Monitor

Data Center GPU Manager (DCGM)

NVIDIA DCGM is a suite of tools for managing and monitoring GPUs in cluster environments. It includes active health monitoring, comprehensive diagnostics, system alerts and governance policies including power and clock management. It can be used standalone by system administrators and easily integrates into cluster management, resource scheduling and monitoring products from NVIDIA partners.

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NVIDIA Management Library (NVML)

NVML is an SDK for monitoring and managing various states of the NVIDIA GPU devices. It provides a direct access to the queries and commands exposed via nvidia-smi. The SDK provides the appropriate header, stub libraries and sample applications.

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