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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 PyTorch?

Seamlessly transition between eager and graph modes with TorchScript, while expediting your production journey using TorchServe. The torch-distributed backend supports scalable distributed training, boosting performance optimization in both research and production contexts. A diverse array of tools and libraries enhances the PyTorch ecosystem, facilitating development across various domains, including computer vision and natural language processing. Furthermore, PyTorch's compatibility with major cloud platforms streamlines the development workflow and allows for effortless scaling. Users can easily select their preferences and run the installation command with minimal hassle. The stable version represents the latest thoroughly tested and approved iteration of PyTorch, generally suitable for a wide audience. For those desiring the latest features, a preview is available, showcasing the newest nightly builds of version 1.10, though these may lack full testing and support. It's important to ensure that all prerequisites are met, including having numpy installed, depending on your chosen package manager. Anaconda is strongly suggested as the preferred package manager, as it proficiently installs all required dependencies, guaranteeing a seamless installation experience for users. This all-encompassing strategy not only boosts productivity but also lays a solid groundwork for development, ultimately leading to more successful projects. Additionally, leveraging community support and documentation can further enhance your experience with PyTorch.

Media

Media

Integrations Supported

Lightning AI

Integrations Supported

Lightning AI
3LC
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Bayesforge
Comet
Daft
Domino Enterprise AI Platform
Flyte
Gemini Enterprise Agent Platform Notebooks
Gradient
Humtap
MLReef
NVIDIA Triton Inference Server
PaliGemma 2
Qualcomm Cloud AI SDK
spaCy
trail
voyage-3-large

API Availability

Has API

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Android
iPhone
iPad
Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Webinars

Company Facts

Organization Name

TorchMetrics

Company Location

United States

Company Website

torchmetrics.readthedocs.io/en/stable/

Company Facts

Organization Name

PyTorch

Date Founded

2016

Company Website

pytorch.org

Categories and Features

Categories and Features

AI Development

Not specified

AI/ML Model Training

Not specified

Machine Learning

Not specified

Neural Network

Not specified

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