PinnedInTDS ArchivebyRichard Liaw·Aug 18, 2020How to tune Pytorch Lightning hyperparametersUse Ray Tune to optimize Pytorch Lightning hyperparameters in 30 lines of code!A response icon3A response icon3
InDistributed Computing with RaybyRichard Liaw·Jan 26, 2021Configuring and Scaling ML with Hydra + RayLaunch your Hydra applications on the cloud with the new Hydra-Ray integration!A response icon1A response icon1
InDistributed Computing with RaybyRichard Liaw·Sep 26, 2020Ray for WindowsRay supports Windows natively as of June 2020! Try Ray on Windows via `pip install -U ray`.
InDistributed Computing with RaybyRichard Liaw·Apr 7, 2020Faster and Cheaper Pytorch with RaySGDDistributed training is annoying to set up and expensive to run. Here’s a library to make distributed Pytorch training simple and cheap.A response icon6A response icon6
InriselabbyRichard Liaw·Jan 21, 2020Scaling Experiments at Berkeley AI ResearchAs AI research becomes more compute heavy, AI researchers become resource constrained. How do Berkeley AI researchers remain productive?
InriselabbyRichard Liaw·Aug 20, 2019Cutting edge hyperparameter tuning with Ray TuneIntroducing Ray Tune, the state-of-the-art hyperparameter tuning library for researchers and developers to use at any scale.A response icon1A response icon1
InTDS ArchivebyRichard Liaw·Aug 18, 2019Ray Tune: a Python library for fast hyperparameter tuning at any scaleHow do you tune hyperparameters with thousands of cores in just 18 lines of code?A response icon5A response icon5
