Related Products
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About
JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs. These capabilities enable efficient computation for complex mathematical functions and large-scale machine-learning models. JAX also integrates with various libraries within its ecosystem, such as Flax for neural networks and Optax for optimization tasks. Comprehensive documentation, including tutorials and user guides, is available to assist users in leveraging JAX's full potential.
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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
Supported
Mac
Supported
Linux
Supported
Cloud
Not 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
Professional researchers and developers searching for a solution to manage their numerical computing and machine learning operations in Python
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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 InformationJAX
United States
docs.jax.dev/en/latest/
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Company InformationPyTorch
Founded: 2016
pytorch.org
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
AWS EC2 Trn3 Instances
Supported
Flower
Supported
Gemma 3n
Supported
IREN Cloud
Supported
LiteRT
Supported
Thunder Compute
Supported
ApertureDB
Not Supported
Cyfuture Cloud
Not Supported
Gemma 3
Not Supported
Giskard
Not Supported
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Integrations
AWS EC2 Trn3 Instances
Supported
Flower
Supported
Gemma 3n
Supported
IREN Cloud
Supported
LiteRT
Supported
Thunder Compute
Supported
ApertureDB
Supported
Cyfuture Cloud
Supported
Gemma 3
Supported
Giskard
Supported
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