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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 Torch?

Torch stands out as a robust framework tailored for scientific computing, emphasizing the effective use of GPUs while providing comprehensive support for a wide array of machine learning techniques. Its intuitive interface is complemented by LuaJIT, a high-performance scripting language, alongside a solid C/CUDA infrastructure that guarantees optimal efficiency. The core objective of Torch is to deliver remarkable flexibility and speed in crafting scientific algorithms, all while ensuring a straightforward approach to the development process. With a wealth of packages contributed by the community, Torch effectively addresses the needs of various domains, including machine learning, computer vision, and signal processing, thereby capitalizing on the resources available within the Lua ecosystem. At the heart of Torch's capabilities are its popular neural network and optimization libraries, which elegantly balance user-friendliness with the flexibility necessary for designing complex neural network structures. Users are empowered to construct intricate neural network graphs while adeptly distributing tasks across multiple CPUs and GPUs to maximize performance. Furthermore, Torch's extensive community support fosters innovation, enabling researchers and developers to push the boundaries of their work in diverse computational fields. This collaborative environment ensures that users can continually enhance their tools and methodologies, making Torch an indispensable asset in the scientific computing landscape.

Media

Media

Integrations Supported

Lightning AI
PyTorch

Integrations Supported

Hetman Internet Spy
LeaderGPU

API Availability

Has API

API Availability

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
Android
iPhone
iPad

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

TorchMetrics

Company Location

United States

Company Website

torchmetrics.readthedocs.io/en/stable/

Company Facts

Organization Name

Torch

Company Website

torch.ch/

Categories and Features

Categories and Features

Machine Learning

Not specified

Neural Network

Not specified

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