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What is Ray?

You can start developing on your laptop and then effortlessly scale your Python code across numerous GPUs in the cloud. Ray transforms conventional Python concepts into a distributed framework, allowing for the straightforward parallelization of serial applications with minimal code modifications. With a robust ecosystem of distributed libraries, you can efficiently manage compute-intensive machine learning tasks, including model serving, deep learning, and hyperparameter optimization. Scaling existing workloads is straightforward, as demonstrated by how Pytorch can be easily integrated with Ray. Utilizing Ray Tune and Ray Serve, which are built-in Ray libraries, simplifies the process of scaling even the most intricate machine learning tasks, such as hyperparameter tuning, training deep learning models, and implementing reinforcement learning. You can initiate distributed hyperparameter tuning with just ten lines of code, making it accessible even for newcomers. While creating distributed applications can be challenging, Ray excels in the realm of distributed execution, providing the tools and support necessary to streamline this complex process. Thus, developers can focus more on innovation and less on infrastructure.

What is Horovod?

Horovod, initially developed by Uber, is designed to make distributed deep learning more straightforward and faster, transforming model training times from several days or even weeks into just hours or sometimes minutes. With Horovod, users can easily enhance their existing training scripts to utilize the capabilities of numerous GPUs by writing only a few lines of Python code. The tool provides deployment flexibility, as it can be installed on local servers or efficiently run in various cloud platforms like AWS, Azure, and Databricks. Furthermore, it integrates well with Apache Spark, enabling a unified approach to data processing and model training in a single, efficient pipeline. Once implemented, Horovod's infrastructure accommodates model training across a variety of frameworks, making transitions between TensorFlow, PyTorch, MXNet, and emerging technologies seamless. This versatility empowers users to adapt to the swift developments in machine learning, ensuring they are not confined to a single technology. As new frameworks continue to emerge, Horovod's design allows for ongoing compatibility, promoting sustained innovation and efficiency in deep learning projects.

Media

Media

Integrations Supported

Amazon Web Services (AWS)
Flyte
PyTorch
Python
TensorFlow
Amazon EKS
Amazon SageMaker
Anyscale
Apache Airflow
Dask
Feast
Google Cloud Platform
Google Kubernetes Engine (GKE)
Kubernetes
LanceDB
Union Cloud
io.net

Integrations Supported

Amazon Web Services (AWS)
Flyte
PyTorch
Python
TensorFlow
Azure Databricks
Keras

API Availability

Has API

API Availability

Pricing Information

Free
Open source. Consumption-based.
Free Version
Free Trial Offered?

Pricing Information

Free
Free Version

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS
On-Prem

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Not specified

Company Facts

Organization Name

Anyscale

Date Founded

2019

Company Location

United States

Company Website

ray.io

Company Facts

Organization Name

Horovod

Company Website

horovod.ai/

Categories and Features

Deep Learning

Not specified

Machine Learning

Not specified

ML Model Deployment

Not specified

Categories and Features

AI/ML Model Training

Not specified

Deep Learning

Not specified

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