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ease
features
design
support

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Description

The Elastic Fabric Adapter (EFA) serves as a specialized network interface for Amazon EC2 instances, allowing users to efficiently run applications that demand high inter-node communication at scale within the AWS environment. By utilizing a custom-designed operating system (OS) that circumvents traditional hardware interfaces, EFA significantly boosts the performance of communications between instances, which is essential for effectively scaling such applications. This technology facilitates the scaling of High-Performance Computing (HPC) applications that utilize the Message Passing Interface (MPI) and Machine Learning (ML) applications that rely on the NVIDIA Collective Communications Library (NCCL) to thousands of CPUs or GPUs. Consequently, users can achieve the same high application performance found in on-premises HPC clusters while benefiting from the flexible and on-demand nature of the AWS cloud infrastructure. EFA can be activated as an optional feature for EC2 networking without incurring any extra charges, making it accessible for a wide range of use cases. Additionally, it seamlessly integrates with the most popular interfaces, APIs, and libraries for inter-node communication needs, enhancing its utility for diverse applications.

Description

Traditional CPUs are struggling to meet the growing demands for enhanced computing capabilities, while GPU processors can outperform them by a factor of 100 to 200 in terms of data processing speed. We offer specialized servers tailored for machine learning and deep learning, featuring unique capabilities. Our advanced hardware incorporates the NVIDIA® GPU chipset, renowned for its exceptional operational speed. Among our offerings are the latest Tesla® V100 cards, which boast remarkable processing power. Our systems are optimized for popular deep learning frameworks such as TensorFlow™, Caffe2, Torch, Theano, CNTK, and MXNet™. We provide development tools that support programming languages including Python 2, Python 3, and C++. Additionally, we do not impose extra fees for additional services, meaning that disk space and traffic are fully integrated into the basic service package. Moreover, our servers are versatile enough to handle a range of tasks, including video processing and rendering. Customers of LeaderGPU® can easily access a graphical interface through RDP right from the start, ensuring a seamless user experience. This comprehensive approach positions us as a leading choice for those seeking powerful computational solutions.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

MXNet
TensorFlow
AWS HPC
AWS Nitro System
AWS ParallelCluster
Amazon
Amazon EC2
Amazon Web Services (AWS)
C++
Caffe
Chainer
NVIDIA DRIVE
OpenFOAM
PyTorch
Python
SAP Store
Torch

Integrations

MXNet
TensorFlow
AWS HPC
AWS Nitro System
AWS ParallelCluster
Amazon
Amazon EC2
Amazon Web Services (AWS)
C++
Caffe
Chainer
NVIDIA DRIVE
OpenFOAM
PyTorch
Python
SAP Store
Torch

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

€0.14 per minute
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

United States

Founded

1994

Country

United States

Website

aws.amazon.com/hpc/efa/

Vendor Details

Company Name

LeaderGPU

Country

Netherlands

Website

www.leadergpu.com

Product Features

HPC

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

Alternatives

Alternatives