Ray

Ray

Anyscale
+
+

Related Products

  • Vertex AI
    713 Ratings
    Visit Website
  • RunPod
    133 Ratings
    Visit Website
  • Fraud.net
    56 Ratings
    Visit Website
  • Qloo
    23 Ratings
    Visit Website
  • Parallels RAS
    859 Ratings
    Visit Website
  • myACI
    441 Ratings
    Visit Website
  • Datasite Diligence Virtual Data Room
    458 Ratings
    Visit Website
  • Kubit
    33 Ratings
    Visit Website
  • Arlo Training Management Software
    227 Ratings
    Visit Website
  • Storyals
    5 Ratings
    Visit Website

About

Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve.

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 in need of a powerful Development Framework solution

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

Free
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

Horovod
horovod.ai/

Company Information

Anyscale
Founded: 2019
United States
ray.io

Alternatives

Alternatives

MXNet

MXNet

The Apache Software Foundation
AWS Neuron

AWS Neuron

Amazon Web Services
Vertex AI

Vertex AI

Google
Caffe

Caffe

BAIR
AWS Neuron

AWS Neuron

Amazon Web Services
DeepSpeed

DeepSpeed

Microsoft

Categories

Categories

Integrations

Amazon Web Services (AWS)
Flyte
PyTorch
Python
TensorFlow
Activeeon ProActive
Amazon EC2 Trn2 Instances
Amazon SageMaker
Anyscale
Azure Databricks
Azure Kubernetes Service (AKS)
Databricks Data Intelligence Platform
Feast
Google Kubernetes Engine (GKE)
Keras
Kubernetes
MLflow
MXNet
Microsoft Azure
Union Cloud

Integrations

Amazon Web Services (AWS)
Flyte
PyTorch
Python
TensorFlow
Activeeon ProActive
Amazon EC2 Trn2 Instances
Amazon SageMaker
Anyscale
Azure Databricks
Azure Kubernetes Service (AKS)
Databricks Data Intelligence Platform
Feast
Google Kubernetes Engine (GKE)
Keras
Kubernetes
MLflow
MXNet
Microsoft Azure
Union Cloud
Claim Horovod and update features and information
Claim Horovod and update features and information
Claim Ray and update features and information
Claim Ray and update features and information