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Ray v0.8.2 Release Notes

Release Date: 2020-02-24 // about 4 years ago

  • Highlights

    • Pyarrow is no longer vendered. Ray directly uses the C++ Arrow API. You can use any version of pyarrow with ray. (#7233)
    • The dashboard is turned on by default. It shows node and process information, actor information, and Ray Tune trials information. You can also use ray.show_in_webui to display custom messages for actors. Please try it out and send us feedback! (#6705, #6820, #6822, #6911, #6932, #6955, #7028, #7034)
    • We have made progress on distributed reference counting (behind a feature flag). You can try it out with ray.init(_internal_config=json.dumps({"distributed_ref_counting_enabled": 1})). It is designed to help manage memory using precise distributed garbage collection. (#6945, #6946, #7029, #7075, #7218, #7220, #7222, #7235, #7249)

    💥 Breaking changes

    • 🚚 Many experimental Ray libraries are moved to the util namespace. (#7100)
      • ray.experimental.multiprocessing => ray.util.multiprocessing
      • ray.experimental.joblib => ray.util.joblib
      • ray.experimental.iter => ray.util.iter
      • ray.experimental.serve => ray.serve
      • ray.experimental.sgd => ray.util.sgd
    • Tasks and actors are cleaned up if their owner process dies. (#6818)
    • The OMP_NUM_THREADS environment variable defaults to 1 if unset. This improves training performance and reduces resource contention. (#6998)
    • 0️⃣ We now vendor psutil and setproctitle to support turning the dashboard on by default. Running import psutil after import ray will use the version of psutil that ships with Ray. (#7031)

    Core

    • 🚚 The Python raylet client is removed. All raylet communication now goes through the core worker. (#6018)
    • Calling delete() will not delete objects in the in-memory store. (#7117)
    • ✂ Removed vanilla pickle serialization for task arguments. (#6948)
    • 🛠 Fix bug passing empty bytes into Python tasks. (#7045)
    • ⏱ Progress toward next generation ray scheduler. (#6913)
    • Progress toward service based global control store (GCS). (#6686, #7041)

    RLlib

    • 👌 Improved PyTorch support, including a PyTorch version of PPO. (#6826, #6770)
    • ➕ Added distributed SGD for PPO. (#6918, #7084)
    • ➕ Added an exploration API for controlling epsilon greedy and stochastic exploration. (#6974, #7155)
    • 🛠 Fixed schedule values going negative past the end of the schedule. (#6971, #6973)
    • ➕ Added support for histogram outputs in TensorBoard. (#6942)
    • ➕ Added support for parallel and customizable evaluation step. (#6981)

    Tune

    • 👌 Improved Ax Example. (#7012)
    • 🖨 Process saves asynchronously. (#6912)
    • 0️⃣ Default to tensorboardx and include it in requirements. (#6836)
    • ➕ Added experiment stopping api. (#6886)
    • 🔦 Expose progress reporter to users. (#6915)
    • 🛠 Fix directory naming regression. (#6839)
    • 🖐 Handles nan case for asynchyperband. (#6916)
    • Prevent memory checkpoints from breaking trial fault tolerance. (#6691)
    • ✂ Remove keras dependency. (#6827)
    • ✂ Remove unused tf loggers. (#7090)
    • Set correct path when deleting checkpoint folder. (#6758)
    • 👌 Support callable objects in variant generation. (#6849)

    Autoscaler

    • 🐳 Ray nodes now respect docker limits. (#7039)
    • ➕ Add --all-nodes option to rsync-up. (#7065)
    • ➕ Add port-forwarding support for attach. (#7145)
    • ✅ For AWS, default to latest deep learning AMI. (#6922)
    • ➕ Added 'ray dashboard' command to proxy ray dashboard in remote machine. (#6959)

    Utility libraries

    • 👌 Support of scikit-learn with Ray joblib backend. (#6925)
    • 👍 Parallel iterator support local shuffle. (#6921)
    • 👍 [Serve] support no http headless services. (#7010)
    • 🔨 [Serve] refactor router to use Ray asyncio support. (#6873)
    • 👍 [Serve] support composing arbitrary dags. (#7015)
    • 👍 [RaySGD] support fp16 via PyTorch apex. (#7061)
    • 📚 [RaySGD] refactor PyTorch sgd documentation. (#6910)
    • 👌 Improvement in Ray Streaming. (#7043, #6666, #7071)

    Other improvements

    • 🏁 Progress toward Windows compatibility. (#6882, #6823)
    • Ray Kubernetes operator improvements. (#6852, #6851, #7091)
    • 👍 Java support for concurrent actor calls API. (#7022)
    • 👍 Java support for direct call for normal tasks. (#7193)
    • 👍 Java support for cross language Python invocation. (#6709)
    • 👍 Java support for cross language serialization for actor handles. (#7134)

    Known issue

    • Passing the same ObjectIDs multiple time as arguments currently doesn't work. (#7296)
    • Tasks can exceed gRPC max message size. (#7263)

    Thanks

    🚀 We thank the following contributors for their work on this release:
    @mitchellstern, @hugwi, @deanwampler, @alindkhare, @ericl, @ashione, @fyrestone, @robertnishihara, @pcmoritz, @richardliaw, @yutaizhou, @istoica, @edoakes, @ls-daniel, @BalaBalaYi, @raulchen, @justinkterry, @roireshef, @elpollouk, @kfstorm, @Bassstring, @hhbyyh, @Qstar, @mehrdadn, @chaokunyang, @flying-mojo, @ujvl, @AnanthHari, @rkooo567, @simon-mo, @jovany-wang, @ijrsvt, @ffbin, @AmeerHajAli, @gaocegege, @suquark, @MissiontoMars, @zzyunzhi, @sven1977, @stephanie-wang, @amogkam, @wuisawesome, @aannadi, @maximsmol