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Description

Effortlessly switch between eager and graph modes using TorchScript, while accelerating your journey to production with TorchServe. The torch-distributed backend facilitates scalable distributed training and enhances performance optimization for both research and production environments. A comprehensive suite of tools and libraries enriches the PyTorch ecosystem, supporting development across fields like computer vision and natural language processing. Additionally, PyTorch is compatible with major cloud platforms, simplifying development processes and enabling seamless scaling. You can easily choose your preferences and execute the installation command. The stable version signifies the most recently tested and endorsed iteration of PyTorch, which is typically adequate for a broad range of users. For those seeking the cutting-edge, a preview is offered, featuring the latest nightly builds of version 1.10, although these may not be fully tested or supported. It is crucial to verify that you meet all prerequisites, such as having numpy installed, based on your selected package manager. Anaconda is highly recommended as the package manager of choice, as it effectively installs all necessary dependencies, ensuring a smooth installation experience for users. This comprehensive approach not only enhances productivity but also ensures a robust foundation for development.

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

Lightning AI
AI Squared
Amazon EC2 G5 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 Trn2 Instances
Amazon SageMaker Model Training
Comet
Database Mart
Guild AI
JFrog ML
Microsoft Azure
NVIDIA FLARE
Sesterce
Superwise
TensorWave
Unremot
VLLM
Vast.ai
Vectice
io.net

Integrations

Lightning AI
AI Squared
Amazon EC2 G5 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 Trn2 Instances
Amazon SageMaker Model Training
Comet
Database Mart
Guild AI
JFrog ML
Microsoft Azure
NVIDIA FLARE
Sesterce
Superwise
TensorWave
Unremot
VLLM
Vast.ai
Vectice
io.net

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

PyTorch

Founded

2016

Website

pytorch.org

Vendor Details

Company Name

TorchMetrics

Country

United States

Website

torchmetrics.readthedocs.io/en/stable/

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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

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