MXNet

MXNet

The Apache Software Foundation
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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

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.

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

Developers and researchers requiring an open-source deep learning framework for research prototyping and production

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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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

The Apache Software Foundation
Founded: 1999
United States
mxnet.apache.org

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Alternatives

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Caffe

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Categories

Categories

Integrations

Flower
AWS EC2 Trn3 Instances
AWS Marketplace
Amazon EC2 P4 Instances
Amazon Elastic Inference
Amazon SageMaker Debugger
Amazon SageMaker Model Building
Cameralyze
GPUonCLOUD
Google Cloud Deep Learning VM Image
Gradient
Grain
Hugging Face
LeaderGPU
LiteRT
NVIDIA Triton Inference Server
NumPy
Python
TensorFlow
Thunder Compute

Integrations

Flower
AWS EC2 Trn3 Instances
AWS Marketplace
Amazon EC2 P4 Instances
Amazon Elastic Inference
Amazon SageMaker Debugger
Amazon SageMaker Model Building
Cameralyze
GPUonCLOUD
Google Cloud Deep Learning VM Image
Gradient
Grain
Hugging Face
LeaderGPU
LiteRT
NVIDIA Triton Inference Server
NumPy
Python
TensorFlow
Thunder Compute
Claim JAX and update features and information
Claim JAX and update features and information
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