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About

Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.

About

TorchMetrics is a collection of 90+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. A standardized interface to increase reproducibility. It reduces boilerplate. distributed-training compatible. It has been rigorously tested. Automatic accumulation over batches. Automatic synchronization between multiple devices. You can use TorchMetrics in any PyTorch model, or within PyTorch Lightning to enjoy additional benefits. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. Similar to torch.nn, most metrics have both a class-based and a functional version. The functional versions implement the basic operations required for computing each metric. They are simple python functions that as input take torch.tensors and return the corresponding metric as a torch.tensor. Nearly all functional metrics have a corresponding class-based metric.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment

Audience

Anyone seeking a solution providing several PyTorch metrics implementations to create custom metrics

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

PyTorch
Founded: 2016
pytorch.org

Company Information

TorchMetrics
United States
torchmetrics.readthedocs.io/en/stable/

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Categories

Categories

Integrations

Lightning AI
AWS Marketplace
Cerebrium
CodeQwen
EasyODM
Fabric for Deep Learning (FfDL)
Flower
Fuzzball
Gradient
Humtap
IBM Distributed AI APIs
MLReef
MegaETH
NVIDIA FLARE
Qualcomm Cloud AI SDK
Ray
SuperDuperDB
Vertex AI Notebooks
neptune.ai
spaCy

Integrations

Lightning AI
AWS Marketplace
Cerebrium
CodeQwen
EasyODM
Fabric for Deep Learning (FfDL)
Flower
Fuzzball
Gradient
Humtap
IBM Distributed AI APIs
MLReef
MegaETH
NVIDIA FLARE
Qualcomm Cloud AI SDK
Ray
SuperDuperDB
Vertex AI Notebooks
neptune.ai
spaCy
Claim PyTorch and update features and information
Claim PyTorch and update features and information
Claim TorchMetrics and update features and information
Claim TorchMetrics and update features and information