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Changelog History
Page 2

  • v0.8.0 Changes

    December 18, 2019

    🚀 Ray 0.8.0 Release Notes

    🚀 This is the first release with gRPC direct calls enabled by default for both tasks and actors, which substantially improves task submission performance.

    Highlights

    • ⏱ Enable gRPC direct calls by default (#6367). In this mode, actor tasks are sent directly from actor to actor over gRPC; the Raylet only coordinates actor creation. Similarly, with tasks, tasks are submitted directly from worker to worker over gRPC; the Raylet only coordinates the scheduling decisions. In addition, small objects (<100KB in size) are no longer placed in the object store. They are inlined into task submissions and returns when possible.

    👮 Note: in some cases, reconstruction of large evicted objects is not possible with direct calls. To revert to the 0.7.7 behaviour, you can set the environment variable RAY_FORCE_DIRECT=0.

    Core

    • [Dashboard] Add remaining features from old dashboard (#6489)
    • Ray Kubernetes Operator Part 1: readme, structure, config and CRD realted file (#6332)
    • 👉 Make sure numpy >= 1.16.0 is installed for fast pickling support (#6486)
    • 👀 Avoid workers starting with the same random seed (#6471)
    • Properly handle a forwarded task that gets forwarded back (#6271)

    RLlib

    • 🚚 (Bug Fix): Remove the extra 0.5 in the Diagonal Gaussian entropy (#6475)
    • AlphaZero and Ranked reward implementation (#6385)

    Tune

    • ➕ Add example and tutorial for DCGAN (#6400)
    • Report trials by state fairly (#6395)
    • 🛠 Fixed bug in PBT where initial trial result is empty. (#6351)

    Other Libraries

    • ⚡️ [sgd] Add support for multi-model multi-optimizer training (#6317)
    • [serve] Added deadline awareness (#6442)
    • [projects] Return parameters for a command (#6409)
    • [streaming] Streaming data transfer and python integration (#6185)

    Thanks

    🚀 We thank the following contributors for their work on this release:

    @zplizzi, @istoica, @ericl, @mehrdadn, @walterddr, @ujvl, @alindkhare, @timgates42, @chaokunyang, @eugenevinitsky, @kfstorm, @Maltimore, @visatish, @simon-mo, @AmeerHajAli, @wumuzi520, @robertnishihara, @micafan, @pcmoritz, @zhijunfu, @edoakes, @sytelus, @ffbin, @richardliaw, @Qstar, @stephanie-wang, @Coac, @mitchellstern, @MissiontoMars, @deanwampler, @hhbyyh, @raulchen

  • v0.7.7 Changes

    December 16, 2019

    🚀 Ray 0.7.7 Release Notes

    Highlights

    • 👍 Remote functions and actors now support kwargs and positionals (#5606).
    • ⏱ ray.get now supports a timeout argument (#6107). If the object isn't available before the timeout passes, a RayTimeoutError is raised.
    • ✅ Ray now supports detached actors (#6036), which persist beyond the lifetime of the script that creates them and can be referred to by a user-defined name.
    • ➕ Added documentation for how to deploy Ray on YARN clusters using Skein (#6119, #6173).
    • ⏱ The Ray scheduler now attempts to schedule tasks fairly to avoid starvation (#5851).

    Core

    RLlib

    • 👀 Now using pytorch's function to see if gpu is available. #5890
    • 🛠 Fixed APEX priorities returning zero all the time. #5980
    • 🛠 Fixed leak of TensorFlow assign operations in DQN/DDPG. #5979
    • 🛠 Fixed choosing the wrong neural network model for Atari in 0.7.5. #6087
    • ➕ Added large scale regression test for RLlib. #6093
    • 🛠 Fixed and added test for LR annealing config. #6101
    • ⬇️ Reduced log verbosity. #6154
    • ➕ Added a microbatch optimizer with an A2C example. #6161

    Tune

    • Search algorithms now use early stopped trials for optimization. #5651
    • Metrics are now outputted via a tabular format. Errors are outputted on a separate table. #5822
    • 🔀 In the distributed setting, checkpoints are now deleted automatically post-sync using an rsync flag. Checkpoints on the driver are garbage collected according to the policy defined by the user. #5877
    • A much faster ExperimentAnalysis tool. #5962
    • Trial executor callbacks now take in a “Runner” parameter. #5868
    • 🛠 Fixed queue_trials so to enable cluster autoscaling with a CPU-Only Head Node. #5900
    • ➕ Added a TensorBoardX logger. #6133

    Other Libraries

    Thanks

    We thank the following contributors for their amazing contributions:

    @zhuohan123, @jovany-wang, @micafan, @richardliaw, @waldroje, @mitchellstern, @visatish, @mehrdadn, @istoica, @ericl, @adizim, @simon-mo, @lsklyut, @zhu-eric, @pcmoritz, @hhbyyh, @suquark, @sotte, @hershg, @pschafhalter, @stackedsax, @edoakes, @mawright, @stephanie-wang, @ujvl, @ashione, @couturierc, @AdamGleave, @robertnishihara, @DaveyBiggers, @daiyaanarfeen, @danyangz, @AmeerHajAli, @mimoralea

  • v0.7.6 Changes

    October 24, 2019

    🚀 Ray 0.7.6 Release Notes

    Highlights

    👍 The Ray autoscaler now supports Kubernetes as a backend (#5492). This makes it possible to start a Ray cluster on top of your existing Kubernetes cluster with a simple shell command.

    - This is a new feature and may be rough around the edges. If you run into problems or have suggestions for how to improve Ray on Kubernetes, please file an issue.

    🔊 The Ray cluster dashboard has been revamped (#5730, #5857) to improve the UI and include logs and error messages. More improvements will be coming in the near future.

    • You can try out the dashboard by starting Ray with ray.init(include_webui=True) or ray start --include-webui.
    • Please let us know if you have suggestions for what would be most useful to you in the new dashboard.

    Core

    • 🔨 Progress towards refactoring the Python worker on top of the core worker. #5750, #5771, #5752
    • 🛠 Fix an issue in local mode where multiple actors didn't work properly. #5863
    • 🛠 Fix class attributes and methods for actor classes. #5802
    • 👌 Improvements in error messages and handling. #5782, #5746, #5799
    • Serialization improvements. #5841, #5725
    • 📚 Various documentation improvements. #5801, #5792, #5414, #5747, #5780, #5582

    RLlib

    • ➕ Added a link to BAIR blog posts in the documentation. #5762
    • Tracing for eager tensorflow policies with tf.function. #5705

    Tune

    • 👌 Improved MedianStoppingRule. #5402
    • ➕ Add PBT + Memnn example. #5723
    • ➕ Add support for function-based stopping condition. #5754
    • 💾 Save/Restore for Suggestion Algorithms. #5719
    • TensorBoard HParams for TF2.0. #5678

    Other Libraries

    Thanks

    We thank the following contributors for their amazing contributions:

    @hershg, @JasonWayne, @kfstorm, @richardliaw, @batzner, @vakker, @robertnishihara, @stephanie-wang, @gehring, @edoakes, @zhijunfu, @pcmoritz, @mitchellstern, @ujvl, @simon-mo, @ecederstrand, @mawright, @ericl, @anthonyhsyu, @suquark, @waldroje

  • v0.7.5

    September 17, 2019

  • v0.7.4 Changes

    September 05, 2019

    🚀 Ray 0.7.4 Release Notes

    Highlights

    📚 There were many documentation improvements (#5391, #5389, #5175). As we continue to improve the documentation we value your feedback through the “Doc suggestion?” link at the top of the documentation. Notable improvements:

    • We’ve added guides for best practices using TensorFlow and PyTorch.
    • We’ve revamped the Walkthrough page for Ray users, providing a better experience for beginners.

    - We’ve revamped guides for using Actors and inspecting internal state.

    Ray supports memory limits now to ensure memory-intensive applications run predictably and reliably. You
    can activate them through the ray.remote decorator:

    @ray.remote( memory=2000 \* 1024 \* 1024, object\_store\_memory=200 \* 1024 \* 1024)class SomeActor(object): def \_\_init\_\_(self, a, b): pass

    📚 You can set limits for the heap and the object store, see the documentation.

    There is now preliminary support for projects , see the the project documentation. Projects allow you to
    📦 package your code and easily share it with others, ensuring a reproducible cluster setup. To get started, you
    can run

    # Create a new project.ray project create \<project-name\># Launch a session for the project in the current directory.ray session start# Open a console for the given session.ray session attach# Stop the given session and all of its worker nodes.ray session stop

    Check out the examples. This is an actively developed new feature so we appreciate your feedback!

    💥 Breaking change: The redis_address parameter was renamed to address (#5412, #5602) and the former will be removed in the future.

    Core

    • 🚚 Move Java bindings on top of the core worker #5370
    • 👌 Improve log file discoverability #5580
    • Clean up and improve error messages #5368, #5351

    RLlib

    • 👌 Support custom action space distributions #5164
    • ➕ Add TensorFlow eager support #5436
    • ➕ Add autoregressive KL #5469
    • Autoregressive Action Distributions #5304
    • Implement MADDPG agent #5348
    • Port Soft Actor-Critic on Model v2 API #5328
    • More examples: Add CARLA community example #5333 and rock paper scissors multi-agent example #5336
    • 🚚 Moved RLlib to top level directory #5324

    Tune

    • Experimental Implementation of the BOHB algorithm #5382
    • 💥 Breaking change: Nested dictionary results are now flattened for CSV writing: {“a”: {“b”: 1}} => {“a/b”: 1} #5346
    • ➕ Add Logger for MLFlow #5438
    • 👍 TensorBoard support for TensorFlow 2.0 #5547
    • ➕ Added examples for XGBoost and LightGBM #5500
    • HyperOptSearch now has warmstarting #5372

    Other Libraries

    🛠 Various fixes: Fix log monitor issues #4382 #5221 #5569, the top-level ray directory was cleaned up #5404

    Thanks

    We thank the following contributors for their amazing contributions:

    @jon-chuang, @lufol, @adamochayon, @idthanm, @RehanSD, @ericl, @michaelzhiluo, @nflu, @pengzhenghao, @hartikainen, @wsjeon, @raulchen, @TomVeniat, @layssi, @jovany-wang, @llan-ml, @ConeyLiu, @mitchellstern, @gregSchwartz18, @jiangzihao2009, @jichan3751, @mhgump, @zhijunfu, @micafan, @simon-mo, @richardliaw, @stephanie-wang, @edoakes, @akharitonov, @mawright, @robertnishihara, @lisadunlap, @flying-mojo, @pcmoritz, @jredondopizarro, @gehring, @holli, @kfstorm

  • v0.7.3

    July 31, 2019

  • v0.7.2 Changes

    July 03, 2019

    Core

    • 👌 Improvements
    • Python
      • @ray.remote now inherits the function docstring. #4985
      • Remove typing module from setup.py install_requirements. #4971
    • Java
      • Allow users to set JVM options at actor creation time. #4970
    • Internal
      • Refactor IDs: DriverID -> JobID, change all ID functions to camel case. #4964, #4896
      • Improve organization of directory structure. #4898
    • Peformance
      • Get task object dependencies in parallel from object store. #4775
      • Flush lineage cache on task submission instead of execution. #4942
      • Remove debug check for uncommitted lineage. #5038

    Tune

    • ➕ Add directional metrics for components. #4120, #4915
    • Disallow setting resources_per_trial when it is already configured. #4880
    • 🔧 Make PBT Quantile fraction configurable. #4912

    RLlib

    • ➕ Add QMIX mixer parameters to optimizer param list. #5014
    • Allow Torch policies access to full action input dict in extra_action_out_fn. #4894
    • 👍 Allow access to batches prior to postprocessing. #4871
    • Throw error if sample_async is used with pytorch for A3C. #5000
    • Patterns & User Experience
      • Rename PolicyEvaluator => RolloutWorker. #4820
      • Port remainder of algorithms to build_trainer() pattern. #4920
      • Port DQN to build_tf_policy() pattern. #4823
    • 📚 Documentation
      • Add docs on how to use TF eager execution. #4927
      • Add preprocessing example to offline documentation. #4950

    Other Libraries

    • ➕ Add support for distributed training with PyTorch. #4797, #4933
    • 👷 Autoscaler will kill workers on exception. #4997
    • 🛠 Fix handling of non-integral timeout values in signal.receive. #5002

    Thanks

    We thank the following contributors for their amazing contributions: @jiangzihao2009, @raulchen, @ericl, @hershg, @kfstorm, @kiddyboots216, @jovany-wang, @pschafhalter, @richardliaw, @robertnishihara, @stephanie-wang, @simon-mo, @zhijunfu, @ls-daniel, @ajgokhale, @rueberger, @suquark, @guoyuhong, @jovany-wang, @pcmoritz, @hartikainen, @timonbimon, @TianhongDai

  • v0.7.1 Changes

    June 23, 2019

    Core

    • 🔄 Change global state API. #4857
      • ray.global_state.client_table() -> ray.nodes()
      • ray.global_state.task_table() -> ray.tasks()
      • ray.global_state.object_table() -> ray.objects()
      • ray.global_state.chrome_tracing_dump() -> ray.timeline()
      • ray.global_state.cluster_resources() -> ray.cluster_resources()
      • ray.global_state.available_resources() -> ray.available_resources()
    • Export remote functions lazily. #4898
    • 👷 Begin moving worker code to C++. #4875, #4899, #4898
    • ⬆️ Upgrade arrow to latest master. #4858
    • Upload wheels to S3 under <branch-name>/<commit-id>. #4949
    • ➕ Add hash table to Redis-Module. #4911
    • 🎉 Initial support for distributed training with PyTorch. #4797

    Tune

    • Disallow setting resources_per_trial when it is already configured. #4880
    • 🎉 Initial experiment tracking support. #4362

    RLlib

    • 🗄 Begin deprecating Python 2 support in RLlib. #4832
    • TensorFlow 2 compatibility. #4802
    • Allow Torch policies access to full action input dict in extra_action_out_fn. #4894
    • 👍 Allow access to batches prior to postprocessing. #4871
    • 🏗 Port algorithms to build_trainer() pattern. #4823
    • 👷 Rename PolicyEvaluator -> RolloutWorker. #4820
    • 🚚 Rename PolicyGraph -> Policy, move from evaluation/ to policy/. #4819
    • 👌 Support continuous action distributions in IMPALA/APPO. #4771

    🚀 (Revision: 6/23/2019 - Accidentally included commits that were not part of the release.)

  • v0.7.0 Changes

    May 18, 2019

    Core

    • 🛠 Backend bug fixes. #4766, #4763, #4605
    • ➕ Add experimental API for creating resources at runtime. #3742

    Tune

    RLlib

    • ✂ Remove dependency on TensorFlow. #4764
    • TD3/DDPG improvements and MuJoCo benchmarks. #4694
    • Evaluation mode implementation for rllib.Trainer class. #4647
    • Replace ray.get() with ray_get_and_free() to automatically free object store memory. #4586
    • 🛠 RLLib bug fixes. #4736, #4735, #4652, #4630

    Autoscaler

    • ➕ Add an aggressive autoscaling flag. #4285
    • 🛠 Autoscalar bug fixes. #4782, #4653

  • v0.6.6 Changes

    April 19, 2019

    Core

    • Add delete_creating_tasks option for internal.free() #4588

    Tune

    • ➕ Add filter flag for Tune CLI. #4337
    • 👍 Better handling of tune.function in global checkpoint. #4519
    • ➕ Add compatibility to nevergrad 0.2.0+. #4529
    • ➕ Add --columns flag for CLI. #4564
    • ➕ Add checkpoint eraser. #4490
    • 🛠 Fix checkpointing for Gym types. #4619

    RLlib

    • 🐎 Report sampler performance metrics. #4427
    • Ensure stats are consistently reported across all algos. #4445
    • Cleanup TFPolicyGraph. #4478
    • 👷 Make batch timeout for remote workers tunable. #4435
    • 🛠 Fix inconsistent weight assignment operations in DQNPolicyGraph. #4504
    • ➕ Add support for LR schedule to DQN/APEX. #4473
    • ➕ Add option for RNN state and value estimates to span episodes. #4429
    • Create a combination of ExternalEnv and MultiAgentEnv, called ExternalMutliAgentEnv. #4200
    • Support prev_state/prev_action in rollout and fix multiagent. #4565
    • 👌 Support torch device and distributions. #4553

    Java

    • ✅ TestNG outputs more verbose error messages. #4507
    • Implement GcsClient. #4601
    • Avoid unnecessary memory copy and addd a benchmark. #4611

    Autoscaler

    • ➕ Add support for separate docker containers on head and worker nodes. #4537
    • ➕ Add an aggressive autoscaling flag. #4285