MXNetThe Apache Software Foundation
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RayAnyscale
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Related Products
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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.
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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.
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Platforms Supported
Windows
Supported
Mac
Supported
Linux
Supported
Cloud
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
Supported
On-Premises
Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
Developers and researchers requiring an open-source deep learning framework for research prototyping and production
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Audience
ML and AI Engineers, Software Developers
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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
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
Free
Open source. Consumption-based.
Free Version
Supported
Free Trial
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
Supported
In Person
Supported
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Company InformationThe Apache Software Foundation
Founded: 1999
United States
mxnet.apache.org
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Company InformationAnyscale
Founded: 2019
United States
ray.io
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Alternatives |
Alternatives |
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Categories |
Categories |
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Integrations
AWS Elastic Fabric Adapter (EFA)
Supported
Amazon EC2 Inf1 Instances
Supported
Amazon EC2 Trn2 Instances
Not Supported
Amazon SageMaker Debugger
Supported
Amazon Web Services (AWS)
Not Supported
Anyscale
Not Supported
Dask
Not Supported
Feast
Not Supported
Flyte
Not Supported
GPUonCLOUD
Supported
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Integrations
AWS Elastic Fabric Adapter (EFA)
Not Supported
Amazon EC2 Inf1 Instances
Not Supported
Amazon EC2 Trn2 Instances
Supported
Amazon SageMaker Debugger
Not Supported
Amazon Web Services (AWS)
Supported
Anyscale
Supported
Dask
Supported
Feast
Supported
Flyte
Supported
GPUonCLOUD
Not Supported
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