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Description

The Trn1 instances of Amazon Elastic Compute Cloud (EC2), driven by AWS Trainium chips, are specifically designed to enhance the efficiency of deep learning training for generative AI models, such as large language models and latent diffusion models. These instances provide significant cost savings of up to 50% compared to other similar Amazon EC2 offerings. They are capable of facilitating the training of deep learning and generative AI models with over 100 billion parameters, applicable in various domains, including text summarization, code generation, question answering, image and video creation, recommendation systems, and fraud detection. Additionally, the AWS Neuron SDK supports developers in training their models on AWS Trainium and deploying them on the AWS Inferentia chips. With seamless integration into popular frameworks like PyTorch and TensorFlow, developers can leverage their current codebases and workflows for training on Trn1 instances, ensuring a smooth transition to optimized deep learning practices. Furthermore, this capability allows businesses to harness advanced AI technologies while maintaining cost-effectiveness and performance.

Description

The VLFeat open source library offers a range of well-known algorithms focused on computer vision, particularly for tasks such as image comprehension and the extraction and matching of local features. Among its various algorithms are Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, the agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, and large scale SVM training, among many others. Developed in C to ensure high performance and broad compatibility, it also has MATLAB interfaces that enhance user accessibility, complemented by thorough documentation. This library is compatible with operating systems including Windows, Mac OS X, and Linux, making it widely usable across different platforms. Additionally, MatConvNet serves as a MATLAB toolbox designed specifically for implementing Convolutional Neural Networks (CNNs) tailored for various computer vision applications. Known for its simplicity and efficiency, MatConvNet is capable of running and training cutting-edge CNNs, with numerous pre-trained models available for tasks such as image classification, segmentation, face detection, and text recognition. The combination of these tools provides a robust framework for researchers and developers in the field of computer vision.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS Deep Learning AMIs
AWS Deep Learning Containers
AWS Inferentia
AWS Neuron
AWS Nitro System
AWS Trainium
Amazon EC2
Amazon EC2 Capacity Blocks for ML
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon SageMaker
Amazon Web Services (AWS)
PyTorch
TensorFlow

Integrations

AWS Deep Learning AMIs
AWS Deep Learning Containers
AWS Inferentia
AWS Neuron
AWS Nitro System
AWS Trainium
Amazon EC2
Amazon EC2 Capacity Blocks for ML
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon SageMaker
Amazon Web Services (AWS)
PyTorch
TensorFlow

Pricing Details

$1.34 per hour
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

Founded

1994

Country

United States

Website

aws.amazon.com/ec2/instance-types/trn1/

Vendor Details

Company Name

VLFeat

Country

United States

Website

www.vlfeat.org/matconvnet/

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

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Amazon Web Services

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