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Description
DeepSpeed is an open-source library focused on optimizing deep learning processes for PyTorch. Its primary goal is to enhance efficiency by minimizing computational power and memory requirements while facilitating the training of large-scale distributed models with improved parallel processing capabilities on available hardware. By leveraging advanced techniques, DeepSpeed achieves low latency and high throughput during model training.
This tool can handle deep learning models with parameter counts exceeding one hundred billion on contemporary GPU clusters, and it is capable of training models with up to 13 billion parameters on a single graphics processing unit. Developed by Microsoft, DeepSpeed is specifically tailored to support distributed training for extensive models, and it is constructed upon the PyTorch framework, which excels in data parallelism. Additionally, the library continuously evolves to incorporate cutting-edge advancements in deep learning, ensuring it remains at the forefront of AI technology.
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.
API Access
Has API
API Access
Has API
Integrations
PyTorch
Axolotl
C++
Cake AI
Comet LLM
Facebook
Instagram
Kotlin
LLaVA
Llama 3.2
Integrations
PyTorch
Axolotl
C++
Cake AI
Comet LLM
Facebook
Instagram
Kotlin
LLaVA
Llama 3.2
Pricing Details
Free
Open source
Free Trial
Free Version
Pricing Details
Free
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
Microsoft
Founded
1975
Country
United States
Website
www.deepspeed.ai/
Vendor Details
Company Name
ExecuTorch
Country
United States
Website
executorch.ai/
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization