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ease
features
design
support

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Description

JAX is a specialized Python library tailored for high-performance numerical computation and research in machine learning. It provides a familiar NumPy-like interface, making it easy for users already accustomed to NumPy to adopt it. Among its standout features are automatic differentiation, just-in-time compilation, vectorization, and parallelization, all of which are finely tuned for execution across CPUs, GPUs, and TPUs. These functionalities are designed to facilitate efficient calculations for intricate mathematical functions and expansive machine-learning models. Additionally, JAX seamlessly integrates with various components in its ecosystem, including Flax for building neural networks and Optax for handling optimization processes. Users can access extensive documentation, complete with tutorials and guides, to fully harness the capabilities of JAX. This wealth of resources ensures that both beginners and advanced users can maximize their productivity while working with this powerful library.

Description

A hybrid front-end efficiently switches between Gluon eager imperative mode and symbolic mode, offering both adaptability and speed. The framework supports scalable distributed training and enhances performance optimization for both research and real-world applications through its dual parameter server and Horovod integration. It features deep compatibility with Python and extends support to languages such as Scala, Julia, Clojure, Java, C++, R, and Perl. A rich ecosystem of tools and libraries bolsters MXNet, facilitating a variety of use-cases, including computer vision, natural language processing, time series analysis, and much more. Apache MXNet is currently in the incubation phase at The Apache Software Foundation (ASF), backed by the Apache Incubator. This incubation stage is mandatory for all newly accepted projects until they receive further evaluation to ensure that their infrastructure, communication practices, and decision-making processes align with those of other successful ASF initiatives. By engaging with the MXNet scientific community, individuals can actively contribute, gain knowledge, and find solutions to their inquiries. This collaborative environment fosters innovation and growth, making it an exciting time to be involved with MXNet.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Flower
AWS Elastic Fabric Adapter (EFA)
Activeeon ProActive
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon SageMaker Debugger
Amazon SageMaker Model Building
Cameralyze
Equinox
GPUonCLOUD
Google Cloud Deep Learning VM Image
Grain
Guild AI
Hugging Face
Keras
LiteRT
MLReef
NVIDIA Triton Inference Server
NumPy
Python

Integrations

Flower
AWS Elastic Fabric Adapter (EFA)
Activeeon ProActive
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon SageMaker Debugger
Amazon SageMaker Model Building
Cameralyze
Equinox
GPUonCLOUD
Google Cloud Deep Learning VM Image
Grain
Guild AI
Hugging Face
Keras
LiteRT
MLReef
NVIDIA Triton Inference Server
NumPy
Python

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

JAX

Country

United States

Website

docs.jax.dev/en/latest/

Vendor Details

Company Name

The Apache Software Foundation

Founded

1999

Country

United States

Website

mxnet.apache.org

Product Features

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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