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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. ​

About

The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

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

Professional researchers and developers searching for a solution to manage their numerical computing and machine learning operations in Python

Audience

Enterprises in search of a solution to execute end-to-end data science and analytics pipelines entirely on GPUs

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

No information available.
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

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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

JAX
United States
docs.jax.dev/en/latest/

Company Information

NVIDIA
Founded: 1993
United States
developer.nvidia.com/rapids

Alternatives

Alternatives

Apache Mahout

Apache Mahout

Apache Software Foundation
DeepSpeed

DeepSpeed

Microsoft
Gensim

Gensim

Radim Řehůřek

Categories

Categories

Integrations

AWS EC2 Trn3 Instances
Anaconda
Capital One Spark Business Banking
Domino Enterprise AI Platform
Equinox
Flower
Gradient
HEAVY.AI
HPE Ezmeral Data Fabric
Hugging Face
Keras
Kinetica
LiteRT
Nuclio
NumPy
Plotly Dash
Python
TensorFlow
Thunder Compute

Integrations

AWS EC2 Trn3 Instances
Anaconda
Capital One Spark Business Banking
Domino Enterprise AI Platform
Equinox
Flower
Gradient
HEAVY.AI
HPE Ezmeral Data Fabric
Hugging Face
Keras
Kinetica
LiteRT
Nuclio
NumPy
Plotly Dash
Python
TensorFlow
Thunder Compute
Claim JAX and update features and information
Claim JAX and update features and information
Claim NVIDIA RAPIDS and update features and information
Claim NVIDIA RAPIDS and update features and information