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
Transition seamlessly between eager and graph modes with TorchScript, and accelerate the path to production with TorchServe. Scalable distributed training and performance optimization in research and production is enabled by the torch-distributed backend. A rich ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP and more. PyTorch is well supported on major cloud platforms, providing frictionless development and easy scaling. Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies.
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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
Not Supported
On-Premises
Not Supported
iPhone
Supported
iPad
Supported
Android
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
Researchers in need of an open source machine learning solution to accelerate research prototyping and production deployment
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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
Not 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
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 InformationPyTorch
Founded: 2016
pytorch.org
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Alternatives |
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Categories |
Categories |
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Integrations
AWS Elastic Fabric Adapter (EFA)
Not Supported
Amazon EC2 UltraClusters
Not Supported
ApertureDB
Not Supported
Bayesforge
Not Supported
Cleanlab
Not Supported
Coiled
Not Supported
Daft
Not Supported
Fabric for Deep Learning (FfDL)
Not Supported
FakeYou
Not Supported
Flower
Not Supported
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Integrations
AWS Elastic Fabric Adapter (EFA)
Supported
Amazon EC2 UltraClusters
Supported
ApertureDB
Supported
Bayesforge
Supported
Cleanlab
Supported
Coiled
Supported
Daft
Supported
Fabric for Deep Learning (FfDL)
Supported
FakeYou
Supported
Flower
Supported
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