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What is TorchMetrics?

TorchMetrics offers a collection of over 90 performance metrics tailored for PyTorch, complemented by an intuitive API that enables users to craft custom metrics effortlessly. By providing a standardized interface, it significantly boosts reproducibility and reduces instances of code duplication. Furthermore, this library is well-suited for distributed training scenarios and has been rigorously tested to confirm its dependability. It includes features like automatic batch accumulation and smooth synchronization across various devices, ensuring seamless functionality. You can easily incorporate TorchMetrics into any PyTorch model or leverage it within PyTorch Lightning to gain additional benefits, all while ensuring that your metrics stay aligned with the same device as your data. Moreover, it's possible to log Metric objects directly within Lightning, which helps streamline your code and eliminate unnecessary boilerplate. Similar to torch.nn, most of the metrics are provided in both class and functional formats. The functional versions are simple Python functions that accept torch.tensors as input and return the respective metric as a torch.tensor output. Almost all functional metrics have a corresponding class-based version, allowing users to select the method that best suits their development style and project needs. This flexibility empowers developers to implement metrics in a way that aligns with their unique workflows and preferences. Furthermore, the extensive range of metrics available ensures that users can find the right tools to enhance their model evaluation and performance tracking.

What is Amazon Elastic Inference?

Amazon Elastic Inference provides a budget-friendly solution to boost the performance of Amazon EC2 and SageMaker instances, as well as Amazon ECS tasks, by enabling GPU-driven acceleration that could reduce deep learning inference costs by up to 75%. It is compatible with models developed using TensorFlow, Apache MXNet, PyTorch, and ONNX. Inference refers to the process of predicting outcomes once a model has undergone training, and in the context of deep learning, it can represent as much as 90% of overall operational expenses due to a couple of key reasons. One reason is that dedicated GPU instances are largely tailored for training, which involves processing many data samples at once, while inference typically processes one input at a time in real-time, resulting in underutilization of GPU resources. This discrepancy creates an inefficient cost structure for GPU inference that is used on its own. On the other hand, standalone CPU instances lack the necessary optimization for matrix computations, making them insufficient for meeting the rapid speed demands of deep learning inference. By utilizing Elastic Inference, users are able to find a more effective balance between performance and expense, allowing their inference tasks to be executed with greater efficiency and effectiveness. Ultimately, this integration empowers users to optimize their computational resources while maintaining high performance.

Media

Media

Integrations Supported

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

Integrations Supported

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

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

TorchMetrics

Company Location

United States

Company Website

torchmetrics.readthedocs.io/en/stable/

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

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

Categories and 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

Categories and Features

Infrastructure-as-a-Service (IaaS)

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

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