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About

Flower is an open source federated learning framework designed to simplify the development and deployment of machine learning models across decentralized data sources. It enables training on data located on devices or servers without transferring the data itself, thereby enhancing privacy and reducing bandwidth usage. Flower supports a wide range of machine learning frameworks, including PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and XGBoost, and is compatible with various platforms and cloud services like AWS, GCP, and Azure. It offers flexibility through customizable strategies and supports both horizontal and vertical federated learning scenarios. Flower's architecture allows for scalable experiments, with the capability to handle workloads involving tens of millions of clients. It also provides built-in support for privacy-preserving techniques like differential privacy and secure aggregation.

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

Machine learning practitioners and researchers in search of a tool to implement privacy-preserving, decentralized model training across diverse devices and platforms

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

Free
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

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

Review this Software

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

Flower
Founded: 2023
Germany
flower.ai/

Company Information

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

Alternatives

Alternatives

Keepsake

Keepsake

Replicate
AWS Neuron

AWS Neuron

Amazon Web Services
Keepsake

Keepsake

Replicate

Categories

Categories

Integrations

PyTorch
Amazon Web Services (AWS)
Android
Apple iOS
Docker
Google Cloud Platform
Hardskills
Hugging Face
JAX
Keras
Lightning AI
MXNet
Microsoft Azure
NVIDIA Jetson
NumPy
Python
Raspberry Pi OS
TensorFlow
pandas
scikit-learn

Integrations

PyTorch
Amazon Web Services (AWS)
Android
Apple iOS
Docker
Google Cloud Platform
Hardskills
Hugging Face
JAX
Keras
Lightning AI
MXNet
Microsoft Azure
NVIDIA Jetson
NumPy
Python
Raspberry Pi OS
TensorFlow
pandas
scikit-learn
Claim Flower and update features and information
Claim Flower and update features and information
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Claim TorchMetrics and update features and information