MXNet

MXNet

The Apache Software Foundation
Ray

Ray

Anyscale
+
+

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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.

About

Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.

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

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

Audience

ML and AI Engineers, Software Developers

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

Free
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

This software hasn't been reviewed yet. Be the first to provide a review:

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

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

Company Information

Anyscale
Founded: 2019
United States
ray.io

Alternatives

Caffe

Caffe

BAIR

Alternatives

Vertex AI

Vertex AI

Google
AWS Neuron

AWS Neuron

Amazon Web Services

Categories

Categories

Integrations

AWS Marketplace
Amazon EC2 Inf1 Instances
Amazon EC2 Trn2 Instances
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Databricks Data Intelligence Platform
Flower
Flyte
GPUonCLOUD
Google Cloud Deep Learning VM Image
Kubernetes
MLflow
NVIDIA Triton Inference Server
PyTorch
Python
Snowflake
TensorFlow
Union Cloud
io.net

Integrations

AWS Marketplace
Amazon EC2 Inf1 Instances
Amazon EC2 Trn2 Instances
Anyscale
Apache Airflow
Azure Kubernetes Service (AKS)
Databricks Data Intelligence Platform
Flower
Flyte
GPUonCLOUD
Google Cloud Deep Learning VM Image
Kubernetes
MLflow
NVIDIA Triton Inference Server
PyTorch
Python
Snowflake
TensorFlow
Union Cloud
io.net
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