What frameworks does Ray Train support?

Ray Train supports popular machine learning frameworks including PyTorch, TensorFlow, Hugging Face Transformers, XGBoost, and Horovod.

Can I use this for hyperparameter tuning?

Yes, Ray Train integrates natively with Ray Tune, allowing you to run distributed hyperparameter sweeps with advanced schedulers like ASHAScheduler.

Does it handle node failures during long training runs?

Yes, it features built-in fault tolerance and automatic checkpoint restoration to recover training state if a worker or node fails.

Is it difficult to migrate existing single-GPU code to Ray Train?

No, it requires minimal changes. You typically only need to wrap your model and dataloader with Ray's preparation functions and define a scaling configuration.

Ray Train Distributed Computing

Name: Ray Train Distributed Computing
Author: zechenzhangAGI

byzechenzhangAGI

•

384

데이터 과학 및 ML

Orchestrates distributed machine learning training across clusters to scale PyTorch, TensorFlow, and Hugging Face models.

소개

Ray Train is a specialized skill for scaling machine learning workloads from a single GPU on a local machine to thousands of nodes in a distributed cluster. It provides a unified API for popular frameworks like PyTorch and Hugging Face, handling complex tasks like GPU allocation, multi-node coordination, fault tolerance, and elastic scaling. By integrating with Ray Tune, it also enables massive-scale hyperparameter optimization and early stopping, making it an essential tool for training large-scale AI models and conducting high-performance AI research efficiently.

주요 기능

Automatic fault tolerance and checkpointing for resilient training
Built-in hyperparameter tuning via Ray Tune integration
Multi-node cluster orchestration for PyTorch and TensorFlow
Elastic scaling to dynamically add or remove nodes
384 GitHub stars
Seamless integration with Hugging Face Transformers

사용 사례

Scaling PyTorch training from a local laptop to an AWS or GCP cluster
Running distributed hyperparameter sweeps across 100+ GPU nodes
Training massive Large Language Models using multi-node orchestration

소개

주요 기능

Automatic fault tolerance and checkpointing for resilient training
Built-in hyperparameter tuning via Ray Tune integration
Multi-node cluster orchestration for PyTorch and TensorFlow
Elastic scaling to dynamically add or remove nodes
384 GitHub stars
Seamless integration with Hugging Face Transformers

사용 사례

Scaling PyTorch training from a local laptop to an AWS or GCP cluster
Running distributed hyperparameter sweeps across 100+ GPU nodes
Training massive Large Language Models using multi-node orchestration