MXNetThe Apache Software Foundation
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Related Products
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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.
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About
A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Developers, professionals and researchers seeking a solution for training deep learning models
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Audience
Developers and researchers requiring an open-source deep learning framework for research prototyping and production
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationConvNetJS
cs.stanford.edu/people/karpathy/convnetjs/
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Company InformationThe Apache Software Foundation
Founded: 1999
United States
mxnet.apache.org
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Categories |
Categories |
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Integrations
AWS Elastic Fabric Adapter (EFA)
Not Supported
AWS Marketplace
Not Supported
Amazon EC2 Inf1 Instances
Not Supported
Amazon EC2 P4 Instances
Not Supported
Amazon Elastic Inference
Not Supported
Amazon SageMaker Debugger
Not Supported
Amazon SageMaker Model Building
Not Supported
Cameralyze
Not Supported
Flower
Not Supported
GPUonCLOUD
Not Supported
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Integrations
AWS Elastic Fabric Adapter (EFA)
Supported
AWS Marketplace
Supported
Amazon EC2 Inf1 Instances
Supported
Amazon EC2 P4 Instances
Supported
Amazon Elastic Inference
Supported
Amazon SageMaker Debugger
Supported
Amazon SageMaker Model Building
Supported
Cameralyze
Supported
Flower
Supported
GPUonCLOUD
Supported
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