Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Description

Amazon Elastic Inference provides an affordable way to enhance Amazon EC2 and Sagemaker instances or Amazon ECS tasks with GPU-powered acceleration, potentially cutting deep learning inference costs by as much as 75%. It is compatible with models built on TensorFlow, Apache MXNet, PyTorch, and ONNX. The term "inference" refers to the act of generating predictions from a trained model. In the realm of deep learning, inference can represent up to 90% of the total operational expenses, primarily for two reasons. Firstly, GPU instances are generally optimized for model training rather than inference, as training tasks can handle numerous data samples simultaneously, while inference typically involves processing one input at a time in real-time, resulting in minimal GPU usage. Consequently, relying solely on GPU instances for inference can lead to higher costs. Conversely, CPU instances lack the necessary specialization for matrix computations, making them inefficient and often too sluggish for deep learning inference tasks. This necessitates a solution like Elastic Inference, which optimally balances cost and performance in inference scenarios.

Description

TorchMetrics comprises over 90 implementations of metrics designed for PyTorch, along with a user-friendly API that allows for the creation of custom metrics. It provides a consistent interface that enhances reproducibility while minimizing redundant code. The library is suitable for distributed training and has undergone thorough testing to ensure reliability. It features automatic batch accumulation and seamless synchronization across multiple devices. You can integrate TorchMetrics into any PyTorch model or utilize it within PyTorch Lightning for added advantages, ensuring that your data aligns with the same device as your metrics at all times. Additionally, you can directly log Metric objects in Lightning, further reducing boilerplate code. Much like torch.nn, the majority of metrics are available in both class-based and functional formats. The functional versions consist of straightforward Python functions that accept torch.tensors as inputs and yield the corresponding metric as a torch.tensor output. Virtually all functional metrics come with an equivalent class-based metric, providing users with flexible options for implementation. This versatility allows developers to choose the approach that best fits their coding style and project requirements.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

PyTorch
Amazon EC2
Amazon EC2 G4 Instances
Amazon Web Services (AWS)
Lightning AI
MXNet
TensorFlow

Integrations

PyTorch
Amazon EC2
Amazon EC2 G4 Instances
Amazon Web Services (AWS)
Lightning AI
MXNet
TensorFlow

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

2006

Country

United States

Website

aws.amazon.com/machine-learning/elastic-inference/

Vendor Details

Company Name

TorchMetrics

Country

United States

Website

torchmetrics.readthedocs.io/en/stable/

Product Features

Infrastructure-as-a-Service (IaaS)

Analytics / Reporting
Configuration Management
Data Migration
Data Security
Load Balancing
Log Access
Network Monitoring
Performance Monitoring
SLA Monitoring

Product Features

Application Development

Access Controls/Permissions
Code Assistance
Code Refactoring
Collaboration Tools
Compatibility Testing
Data Modeling
Debugging
Deployment Management
Graphical User Interface
Mobile Development
No-Code
Reporting/Analytics
Software Development
Source Control
Testing Management
Version Control
Web App Development

Alternatives

Alternatives

AWS Neuron Reviews

AWS Neuron

Amazon Web Services
AWS Neuron Reviews

AWS Neuron

Amazon Web Services
Keepsake Reviews

Keepsake

Replicate