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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
NVIDIA PhysicsNeMo is a publicly available Python-based deep-learning framework designed for the creation, training, fine-tuning, and inference of physics-AI models that integrate physical principles with data, thereby enhancing simulations, developing accurate surrogate models, and facilitating near-real-time predictions in various fields such as computational fluid dynamics, structural mechanics, electromagnetics, weather forecasting, climate studies, and digital twin technologies. This framework offers powerful, GPU-accelerated capabilities along with Python APIs that are built on the PyTorch platform and distributed under the Apache 2.0 license, featuring a selection of curated model architectures that include physics-informed neural networks, neural operators, graph neural networks, and generative AI techniques, enabling developers to effectively leverage physics-based causal relationships together with empirical data for high-quality engineering modeling. Additionally, PhysicsNeMo provides comprehensive training pipelines that encompass everything from geometry ingestion to the application of differential equations, along with reference application recipes that help users quickly initiate their development workflows. This combination of features makes PhysicsNeMo an essential tool for engineers and researchers seeking to advance their work in physics-driven AI applications.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
PyTorch
Yes
C++
Yes
Facebook
Yes
Instagram
Yes
Kotlin
Yes
LLaVA
Yes
Llama 3.2
Yes
Muse Glimmer
Yes
Objective-C
Yes
OpenAI Whisper
Yes
Integrations
PyTorch
Yes
C++
No
Facebook
No
Instagram
No
Kotlin
No
LLaVA
No
Llama 3.2
No
Muse Glimmer
No
Objective-C
No
OpenAI Whisper
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
No
On-Premises
Yes
iPhone App
Yes
iPad App
Yes
Android App
Yes
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
ExecuTorch
Country
United States
Website
executorch.ai/
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/physicsnemo