What is DeepSpeed?

DeepSpeed is an innovative open-source library designed to optimize deep learning workflows specifically for PyTorch. Its main objective is to boost efficiency by reducing the demand for computational resources and memory, while also enabling the effective training of large-scale distributed models through enhanced parallel processing on the hardware available. Utilizing state-of-the-art techniques, DeepSpeed delivers both low latency and high throughput during the training phase of models.

This powerful tool is adept at managing deep learning architectures that contain over one hundred billion parameters on modern GPU clusters and can train models with up to 13 billion parameters using a single graphics processing unit. Created by Microsoft, DeepSpeed is intentionally engineered to facilitate distributed training for large models and is built on the robust PyTorch framework, which is well-suited for data parallelism. Furthermore, the library is constantly updated to integrate the latest advancements in deep learning, ensuring that it maintains its position as a leader in AI technology. Future updates are expected to enhance its capabilities even further, making it an essential resource for researchers and developers in the field.

Pricing

Price Starts At:
Free
Price Overview:
Open source
Free Version:
Free Version available.

Screenshots and Video

DeepSpeed Screenshot 1

Company Facts

Company Name:
Microsoft
Date Founded:
1975
Company Location:
United States
Company Website:
www.deepspeed.ai/

Product Details

Deployment
SaaS
Windows
Mac
Linux
On-Prem
Training Options
Documentation Hub

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

DeepSpeed Categories and Features

Deep Learning Software

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization