/NVIDIA Announces Updates to NGC Catalog for HPC, AI Applications (via Qpute.com)
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NVIDIA Announces Updates to NGC Catalog for HPC, AI Applications (via Qpute.com)


March 12, 2020 — The NVIDIA NGC catalog is a hub for GPU-optimized deep learning, machine learning and high-performance computing (HPC) applications. With highly performant software containers, pre-trained models, industry specific SDKs and Helm Charts, the content available on the catalog helps you simplify and accelerate your end-to-end workflows.

NVIDIA_logoThe NVIDIA NGC team works closely with our internal and external partners to update the content in the catalog on a regular basis. Below are some of the highlights:

Artificial Intelligence

Jarvis AI Collections

NVIDIA announced the availability of Jarvis Beta 1.0. Jarvis is a fully accelerated application framework for building multimodal conversational AI services that use an end-to-end deep learning pipeline.

You can get started with the Jarvis AI services for various tasks ranging from speech recognition to question answering to intent detection and more – through the collections available on the NGC catalog.

Containers

  • Our most popular deep learning frameworks for training and inference have been updated to the latest 21.02 version
  • We also have added containers built by our partners

Helmet charts

  • NVIDIA GPU Operator has been updated to version 1.6.0 with added support for Red Hat OpenShift 4.7 and support for the R460 driver for datacenter NVIDIA GPUs. GPU Operator 1.5 was released in late January which added support for NVIDIA vGPU.

High Performance Computing
Containers

  • The Quantum ESPRESSO container has also been updated to version 6.7. The latest version of our container delivers better performance with improvements related to CPU multithreading of FFTs and updated UCX settings.
  • The NGC Pre-flight Check container is a light-weight tool that verifies that the container runtime is set up correctly for GPUs and InfiniBand. You can run this container prior to running your HPC or deep learning container on your system.

Additional Resources

  • For those who are getting started with AI or need a head start with your use-case, we have built sample Jupyter Notebooks for both Image Segmentation and Recommender System to help you get started with these use-cases.

Source: Nvidia


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