zoom_in Click to enlarge

Ray DL

▲ ▼

description Ray DL Overview

Ray DL is a distributed deep learning library built on top of Ray, simplifying the scaling of training and inference workloads. It provides a unified API for various deep learning frameworks, allowing users to easily distribute models across multiple machines or GPUs.

Ray DL excels in handling massive datasets and complex models, making it ideal for researchers and practitioners requiring high-performance training and deployment capabilities. Its focus on scalability and ease of use positions it as a powerful tool for tackling increasingly demanding deep learning challenges.

help Ray DL FAQ

Is Ray DL still the current name of Ray's distributed training library?

Ray's current distributed machine-learning component is called Ray Train rather than Ray DL. Modern projects should follow the Ray Train documentation because older APIs and examples may belong to deprecated Ray Train V1 interfaces.

How does Ray Train distribute a PyTorch training job?

A TorchTrainer launches worker processes across the CPUs or GPUs assigned through its scaling configuration. Each worker runs the user-defined training loop while Ray coordinates resources and distributed execution.

Can Ray Train scale TensorFlow as well as PyTorch?

Ray Train supports major frameworks including PyTorch and TensorFlow through framework-specific trainers and configuration objects. It can run locally on one machine or use a Ray cluster to distribute work across multiple nodes.

What changed between Ray Train V1 and V2?

Ray Train V2 overhauled parts of the execution model and selected APIs, while the V1 references are now marked deprecated. Ray began exposing V2 behind the RAY_TRAIN_V2_ENABLED environment variable in Ray 2.43, so old training code should be checked against the migration guide.

swap_horiz

Looking for Ray DL alternatives? Compare top competitors ranked & scored

arrow_forward