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What is NVIDIA Air?

The complexity of data center infrastructure is increasing, leading to a demand for sophisticated solutions that simplify network management tasks. NVIDIA Air allows users to obtain cloud-scale efficiency by creating accurate replicas of real data center environments. This groundbreaking tool facilitates the modeling of data centers with full software functionalities, thus producing a digital twin. By enabling the simulation, validation, and automation of changes and updates, organizations can enhance and streamline their network operations. Users have the capability to generate one-to-one virtual duplicates of data centers that include a variety of switches and servers. The automation of critical patches and security updates significantly boosts confidence in deployment strategies. Moreover, sharing these simulations with colleagues enhances training and knowledge transfer among team members. The platform offers free access to essential NVIDIA networking software through Air, which functions effortlessly in the cloud. It also accommodates simulations of Cumulus Linux and SONiC operating systems, along with an extensive toolkit for network operations known as NetQ, providing users with vital resources to efficiently manage their networks. This functionality not only improves operational effectiveness but also equips teams with the agility to innovate and respond to ongoing changes in the digital landscape. As a result, businesses can remain competitive while navigating the challenges of modern technology.

What is Bright Cluster Manager?

Bright Cluster Manager provides a diverse array of machine learning frameworks, such as Torch and TensorFlow, to streamline your deep learning endeavors. In addition to these frameworks, Bright features some of the most widely used machine learning libraries, which facilitate dataset access, including MLPython, NVIDIA's cuDNN, the Deep Learning GPU Training System (DIGITS), and CaffeOnSpark, a Spark package designed for deep learning applications. The platform simplifies the process of locating, configuring, and deploying essential components required to operate these libraries and frameworks effectively. With over 400MB of Python modules available, users can easily implement various machine learning packages. Moreover, Bright ensures that all necessary NVIDIA hardware drivers, as well as CUDA (a parallel computing platform API), CUB (CUDA building blocks), and NCCL (a library for collective communication routines), are included to support optimal performance. This comprehensive setup not only enhances usability but also allows for seamless integration with advanced computational resources.

Media

Media

Integrations Supported

NVIDIA Cumulus Linux
NVIDIA DRIVE
SONiC

Integrations Supported

NVIDIA Cumulus Linux
NVIDIA DRIVE
SONiC

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

www.nvidia.com/en-us/networking/ethernet-switching/air/

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/bright-cluster-manager

Categories and Features

Data Center Management

Audit Trail
Behavior-Based Acceleration
Cross Reference System
Device Auto Discovery
Diagnostic Testing
Import / Export Data
JCL Management
Multi-Platform
Multi-User
Power Management
Sarbanes-Oxley Compliance

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

HPC

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