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Average Ratings 0 Ratings

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features
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support

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Description

The NVIDIA GPU-Optimized AMI serves as a virtual machine image designed to enhance your GPU-accelerated workloads in Machine Learning, Deep Learning, Data Science, and High-Performance Computing (HPC). By utilizing this AMI, you can quickly launch a GPU-accelerated EC2 virtual machine instance, complete with a pre-installed Ubuntu operating system, GPU driver, Docker, and the NVIDIA container toolkit, all within a matter of minutes. This AMI simplifies access to NVIDIA's NGC Catalog, which acts as a central hub for GPU-optimized software, enabling users to easily pull and run performance-tuned, thoroughly tested, and NVIDIA-certified Docker containers. The NGC catalog offers complimentary access to a variety of containerized applications for AI, Data Science, and HPC, along with pre-trained models, AI SDKs, and additional resources, allowing data scientists, developers, and researchers to concentrate on creating and deploying innovative solutions. Additionally, this GPU-optimized AMI is available at no charge, with an option for users to purchase enterprise support through NVIDIA AI Enterprise. For further details on obtaining support for this AMI, please refer to the section labeled 'Support Information' below. Moreover, leveraging this AMI can significantly streamline the development process for projects requiring intensive computational resources.

Description

The RAPIDS software library suite, designed on CUDA-X AI, empowers users to run comprehensive data science and analytics workflows entirely on GPUs. It utilizes NVIDIA® CUDA® primitives for optimizing low-level computations while providing user-friendly Python interfaces that leverage GPU parallelism and high-speed memory access. Additionally, RAPIDS emphasizes essential data preparation processes tailored for analytics and data science, featuring a familiar DataFrame API that seamlessly integrates with various machine learning algorithms to enhance pipeline efficiency without incurring the usual serialization overhead. Moreover, it supports multi-node and multi-GPU setups, enabling significantly faster processing and training on considerably larger datasets. By incorporating RAPIDS, you can enhance your Python data science workflows with minimal code modifications and without the need to learn any new tools. This approach not only streamlines the model iteration process but also facilitates more frequent deployments, ultimately leading to improved machine learning model accuracy. As a result, RAPIDS significantly transforms the landscape of data science, making it more efficient and accessible.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace Yes 
Amazon Web Services (AWS) Yes 
Apache Spark No 
Azure Marketplace Yes 
Capital One Spark Business Banking No 
Databricks No 
Domino Enterprise AI Platform No 
Gaia Yes 
Gradient No 
HEAVY.AI No 
HPE Ezmeral Data Fabric No 
IBM Cloud No 
Iguazio No 
Kinetica No 
NVIDIA FLARE No 
Nuclio No 
OpenLIT Yes 

Integrations

AWS Marketplace No 
Amazon Web Services (AWS) No 
Apache Spark Yes 
Azure Marketplace No 
Capital One Spark Business Banking Yes 
Databricks Yes 
Domino Enterprise AI Platform Yes 
Gaia No 
Gradient Yes 
HEAVY.AI Yes 
HPE Ezmeral Data Fabric Yes 
IBM Cloud Yes 
Iguazio Yes 
Kinetica Yes 
NVIDIA FLARE Yes 
Nuclio Yes 
OpenLIT No 

Pricing Details

$3.06 per hour
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac No 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/marketplace/pp/prodview-7ikjtg3um26wq

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

developer.nvidia.com/rapids

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

HPC

Product Features

Data Science

Access Control No 
Advanced Modeling No 
Audit Logs No 
Data Discovery No 
Data Ingestion No 
Data Preparation No 
Data Visualization No 
Model Deployment No 
Reports No 

Alternatives

Alternatives

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