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About

ExecuTorch is PyTorch’s open source framework for deploying AI/ML models directly to edge devices, enabling text, vision, speech, recommendation, and multimodal inference without requiring the cloud. It exports models from PyTorch without intermediate conversion formats, preserves ATen operators, and uses ahead-of-time compilation to optimize performance for target hardware before deployment. Its modular design lets developers choose compile-time and runtime optimizations while staying inside the familiar PyTorch ecosystem, including torchao for quantization. A portable C++ runtime with a base footprint of about 50 KB can run on smartphones, desktops, embedded systems, microcontrollers, DSPs, and Cortex-M processors. ExecuTorch supports Android, iOS, Linux, Windows, macOS, and WebAssembly, with native APIs for C++, Swift, Kotlin, and Objective-C.

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.

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

Edge AI developers who need to deploy PyTorch models efficiently across mobile, embedded, desktop, and specialized hardware

Audience

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

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

ExecuTorch
United States
executorch.ai/

Company Information

Flower
Founded: 2023
Germany
flower.ai/

Alternatives

AWS Neuron

AWS Neuron

Amazon Web Services

Alternatives

LiteRT

LiteRT

Google
Keepsake

Keepsake

Replicate

Categories

Categories

Integrations

PyTorch
Amazon Web Services (AWS)
Android
Docker
Facebook
Google Cloud Platform
Hugging Face
Instagram
JAX
Llama 3.2
MXNet
Microsoft Azure
NVIDIA Jetson
NumPy
Objective-C
OpenAI Whisper
Qwen3
Swift
TensorFlow
pandas

Integrations

PyTorch
Amazon Web Services (AWS)
Android
Docker
Facebook
Google Cloud Platform
Hugging Face
Instagram
JAX
Llama 3.2
MXNet
Microsoft Azure
NVIDIA Jetson
NumPy
Objective-C
OpenAI Whisper
Qwen3
Swift
TensorFlow
pandas
Claim ExecuTorch and update features and information
Claim ExecuTorch and update features and information
Claim Flower and update features and information
Claim Flower and update features and information