DeepSpeed

DeepSpeed

Microsoft
+
+

Related Products

  • Qloo
    23 Ratings
    Visit Website
  • Nutrient SDK
    108 Ratings
    Visit Website
  • DXcharts
    28 Ratings
    Visit Website
  • Apify
    1,291 Ratings
    Visit Website
  • pCloud Business
    183 Ratings
    Visit Website
  • SDS Manager
    4 Ratings
    Visit Website
  • Highcharts
    123 Ratings
    Visit Website
  • Fraud.net
    56 Ratings
    Visit Website
  • Gemini Enterprise Agent Platform
    961 Ratings
    Visit Website
  • Wiz
    1,452 Ratings
    Visit Website

About

ConvNetJS is a Javascript library for training deep learning models (neural networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows you to formulate and solve neural networks in Javascript, and was originally written by @karpathy. However, the library has since been extended by contributions from the community and more are warmly welcome. The fastest way to obtain the library in a plug-and-play way if you don't care about developing is through this link to convnet-min.js, which contains the minified library. Alternatively, you can also choose to download the latest release of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create a bare-bones index.html file in some folder and copy build/convnet-min.js to the same folder.

About

DeepSpeed is an open source deep learning optimization library for PyTorch. It's designed to reduce computing power and memory use, and to train large distributed models with better parallelism on existing computer hardware. DeepSpeed is optimized for low latency, high throughput training. DeepSpeed can train DL models with over a hundred billion parameters on the current generation of GPU clusters. It can also train up to 13 billion parameters in a single GPU. DeepSpeed is developed by Microsoft and aims to offer distributed training for large-scale models. It's built on top of PyTorch, which specializes in data parallelism.

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

Developers, professionals and researchers seeking a solution for training deep learning models

Audience

Deep learning model developers

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

No information available.
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

ConvNetJS
cs.stanford.edu/people/karpathy/convnetjs/

Company Information

Microsoft
Founded: 1975
United States
www.deepspeed.ai/

Alternatives

Alternatives

GPT-NeoX

GPT-NeoX

EleutherAI
AWS Neuron

AWS Neuron

Amazon Web Services
Deci

Deci

Deci AI

Categories

Categories

Integrations

Axolotl
Cake AI
Comet LLM
Nurix
PyTorch
Python
Qwen3-Omni

Integrations

Axolotl
Cake AI
Comet LLM
Nurix
PyTorch
Python
Qwen3-Omni
Claim ConvNetJS and update features and information
Claim ConvNetJS and update features and information
Claim DeepSpeed and update features and information
Claim DeepSpeed and update features and information