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ease
features
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support

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Description

It enables efficient training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances powered by AWS Trainium. Additionally, for model deployment, it facilitates both high-performance and low-latency inference utilizing AWS Inferentia-based Amazon EC2 Inf1 instances along with AWS Inferentia2-based Amazon EC2 Inf2 instances. With the Neuron SDK, users can leverage widely-used frameworks like TensorFlow and PyTorch to effectively train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal alterations to their code and no reliance on vendor-specific tools. The integration of the AWS Neuron SDK with these frameworks allows for seamless continuation of existing workflows, requiring only minor code adjustments to get started. For those involved in distributed model training, the Neuron SDK also accommodates libraries such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), enhancing its versatility and scalability for various ML tasks. By providing robust support for these frameworks and libraries, it significantly streamlines the process of developing and deploying advanced machine learning solutions.

Description

Caffe is a deep learning framework designed with a focus on expressiveness, efficiency, and modularity, developed by Berkeley AI Research (BAIR) alongside numerous community contributors. The project was initiated by Yangqing Jia during his doctoral studies at UC Berkeley and is available under the BSD 2-Clause license. For those interested, there is an engaging web image classification demo available for viewing! The framework’s expressive architecture promotes innovation and application development. Users can define models and optimizations through configuration files without the need for hard-coded elements. By simply toggling a flag, users can seamlessly switch between CPU and GPU, allowing for training on powerful GPU machines followed by deployment on standard clusters or mobile devices. The extensible nature of Caffe's codebase supports ongoing development and enhancement. In its inaugural year, Caffe was forked by more than 1,000 developers, who contributed numerous significant changes back to the project. Thanks to these community contributions, the framework remains at the forefront of state-of-the-art code and models. Caffe's speed makes it an ideal choice for both research experiments and industrial applications, with the capability to process upwards of 60 million images daily using a single NVIDIA K40 GPU, demonstrating its robustness and efficacy in handling large-scale tasks. This performance ensures that users can rely on Caffe for both experimentation and deployment in various scenarios.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
AWS Deep Learning AMIs
AWS Trainium
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon EKS
Amazon EKS Anywhere
Amazon Elastic Container Service (Amazon ECS)
Amazon SageMaker
Docker
Fabric for Deep Learning (FfDL)
Lambda
NVIDIA DIGITS
OpenVINO
Pop!_OS
Zebra by Mipsology

Integrations

Amazon Web Services (AWS)
AWS Deep Learning AMIs
AWS Trainium
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon EKS
Amazon EKS Anywhere
Amazon Elastic Container Service (Amazon ECS)
Amazon SageMaker
Docker
Fabric for Deep Learning (FfDL)
Lambda
NVIDIA DIGITS
OpenVINO
Pop!_OS
Zebra by Mipsology

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
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

Amazon Web Services

Founded

2006

Country

United States

Website

aws.amazon.com/machine-learning/neuron/

Vendor Details

Company Name

BAIR

Country

United States

Website

caffe.berkeleyvision.org

Product Features

Deep Learning

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

Machine Learning

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

Product Features

Deep Learning

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

Alternatives

Alternatives

MXNet Reviews

MXNet

The Apache Software Foundation