Ray Description
You can develop on your laptop, then scale the same Python code elastically across hundreds or GPUs on any cloud. Ray converts existing Python concepts into the distributed setting, so any serial application can be easily parallelized with little code changes. With a strong ecosystem distributed libraries, scale compute-heavy machine learning workloads such as model serving, deep learning, and hyperparameter tuning. Scale existing workloads (e.g. Pytorch on Ray is easy to scale by using integrations. Ray Tune and Ray Serve native Ray libraries make it easier to scale the most complex machine learning workloads like hyperparameter tuning, deep learning models training, reinforcement learning, and training deep learning models. In just 10 lines of code, you can get started with distributed hyperparameter tune. Creating distributed apps is hard. Ray is an expert in distributed execution.
Ray Alternatives
AWS Neuron
It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).
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Amazon Rekognition
Amazon Rekognition allows you to easily add image and video analysis into your applications using proven, highly-scalable, deep learning technology that does not require any machine learning expertise. Amazon Rekognition allows you to identify objects, people and text in images and videos. It also detects inappropriate content. Amazon Rekognition can also be used to perform facial analysis and facial searches. This is useful for many purposes, including user verification, people counting, public safety, and other uses.
Amazon Rekognition Custom Labels allow you to identify objects and scenes in images that meet your business requirements. You can create a model to help you classify machine parts or detect plants that are sick. Amazon Rekognition Custom Labels does all the heavy lifting for you.
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DeepSpeed
DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training.
DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU.
DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.
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Horovod
Uber developed Horovod to make distributed deep-learning fast and easy to implement, reducing model training time from days and even weeks to minutes and hours. Horovod allows you to scale up an existing script so that it runs on hundreds of GPUs with just a few lines Python code. Horovod is available on-premises or as a cloud platform, including AWS Azure and Databricks. Horovod is also able to run on Apache Spark, allowing data processing and model-training to be combined into a single pipeline. Horovod can be configured to use the same infrastructure to train models using any framework. This makes it easy to switch from TensorFlow to PyTorch to MXNet and future frameworks, as machine learning tech stacks evolve.
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Pricing
Pricing Starts At:
Free
Pricing Information:
Open source. Consumption-based.
Free Version:
Yes
Free Trial:
Yes
Company Details
Company:
Anyscale
Year Founded:
2019
Headquarters:
United States
Website:
ray.io
Media
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Product Details
Platforms
SaaS
Windows
Mac
Linux
On-Premises
Type of Training
Documentation
Live Online
Webinars
In Person
Videos
Customer Support
Online
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