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ease
features
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support

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Description

ExecuTorch is an open-source framework developed for PyTorch, specifically designed to deploy AI and machine learning models directly onto edge devices, facilitating tasks such as text, vision, speech, recommendation, and multimodal inference without the need for cloud connectivity. This framework allows for the exportation of models from PyTorch without any need for intermediate conversion formats, effectively maintaining ATen operators and employing ahead-of-time compilation to enhance performance tailored to specific hardware prior to deployment. Developers benefit from a modular architecture that offers flexibility in selecting both compile-time and runtime optimizations, all within the well-known PyTorch environment, which includes torchao specifically for quantization. With a lightweight C++ runtime that occupies roughly 50 KB, ExecuTorch is versatile enough to operate on a variety of platforms, including smartphones, desktops, embedded systems, microcontrollers, DSPs, and Cortex-M processors. It is compatible with multiple operating systems such as Android, iOS, Linux, Windows, macOS, and WebAssembly, and offers native APIs in C++, Swift, Kotlin, and Objective-C. As a result, ExecuTorch provides developers with a powerful tool to streamline the deployment of AI models across diverse devices and applications.

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.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
C++
Cameralyze
DeepSpeed
Deeplake
EasyODM
ExecuTorch
Gemma 4
Huawei Cloud ModelArts
IREN Cloud
Keepsake
Kotlin
LTXV
Lightning AI
LiteRT
NVIDIA FLARE
OpenVINO
SiMa
voyage-3-large

Integrations

Amazon Web Services (AWS)
C++
Cameralyze
DeepSpeed
Deeplake
EasyODM
ExecuTorch
Gemma 4
Huawei Cloud ModelArts
IREN Cloud
Keepsake
Kotlin
LTXV
Lightning AI
LiteRT
NVIDIA FLARE
OpenVINO
SiMa
voyage-3-large

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
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

ExecuTorch

Country

United States

Website

executorch.ai/

Vendor Details

Company Name

PyTorch

Founded

2016

Website

pytorch.org

Product Features

Product Features

Machine Learning

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

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