Ray

Ray

Anyscale
+
+

Related Products

  • Qloo
    23 Ratings
    Visit Website
  • Nutrient SDK
    108 Ratings
    Visit Website
  • Apify
    1,242 Ratings
    Visit Website
  • DXcharts
    28 Ratings
    Visit Website
  • Vertex AI
    961 Ratings
    Visit Website
  • pCloud Business
    182 Ratings
    Visit Website
  • SDS Manager
    4 Ratings
    Visit Website
  • Highcharts
    123 Ratings
    Visit Website
  • Fraud.net
    56 Ratings
    Visit Website
  • Emtrain
    41 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

Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.

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

ML and AI Engineers, Software 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

Anyscale
Founded: 2019
United States
ray.io

Alternatives

Alternatives

Deci

Deci

Deci AI
Keepsake

Keepsake

Replicate

Categories

Categories

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Dask
Databricks Data Intelligence Platform
Feast
Google Cloud Platform
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
MLflow
PyTorch
Python
Qwen3-Omni
Snowflake
TensorFlow
Union Cloud
io.net

Integrations

Amazon SageMaker
Amazon Web Services (AWS)
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Dask
Databricks Data Intelligence Platform
Feast
Google Cloud Platform
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
MLflow
PyTorch
Python
Qwen3-Omni
Snowflake
TensorFlow
Union Cloud
io.net
Claim ConvNetJS and update features and information
Claim ConvNetJS and update features and information
Claim Ray and update features and information
Claim Ray and update features and information