What's New with Ray Libraries: Tune
summary
Ray Tune 1.0 adds concrete, usable features: a toggleable population based training backend (async for speed, sync for stability) plus reproducible replay of PBT runs; a suspend and resume mode that supports HyperOpt and BayesOpt and a single-parameter restart for failed trials; a generalized search space API that collapses ~100 lines of boilerplate to one line; hardened Weights & Biases logging and connectors for Hugging Face transformers, Horovod and PyTorch distributed; scales multi-GPU sweeps with automatic checkpointing and preemptible-instance fault tolerance (example: running 100 samples in one call).
AI-written summary · about
chapters (8)
- 0:00Introduction to Ray Tune
- 1:08Ray Tune overview
- 6:25Ray 1.0 new features
- 8:55Improved developer workflow
- 12:26Generalized search space API
- 14:13Ecosystem integrations
- 17:26Distributed training with tuning
- 20:21Summary and next steps
related talks
22mRay, a Unified Distributed Framework for the Modern AI Stack
26mA Growing Ecosystem of Scalable ML Libraries on Ray
01h 31mIntroduction to Ray for distributed and machine learning applications in Python
26mRay: A System for High-performance, Distributed Python Applications
15mRay: A System for Distributed Applications
44mPanel: ML Platform on Ray
28mScaling AI & Machine Learning Workloads With Ray on AWS, Kubernetes, & BERT
24mRay: A System for High-performance, Distributed ML Applications