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Changelog History
Page 2

  • v0.8.0 Changes

    December 18, 2019

    ๐Ÿš€ Ray 0.8.0 Release Notes

    ๐Ÿš€ This is the first release with gRPC direct calls enabled by default for both tasks and actors, which substantially improves task submission performance.

    Highlights

    • โฑ 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.

    ๐Ÿ‘ฎ 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)
    • ๐Ÿ‘‰ Make sure numpy >= 1.16.0 is installed for fast pickling support (#6486)
    • ๐Ÿ‘€ Avoid workers starting with the same random seed (#6471)
    • Properly handle a forwarded task that gets forwarded back (#6271)

    RLlib

    • ๐Ÿšš (Bug Fix): Remove the extra 0.5 in the Diagonal Gaussian entropy (#6475)
    • AlphaZero and Ranked reward implementation (#6385)

    Tune

    • โž• Add example and tutorial for DCGAN (#6400)
    • Report trials by state fairly (#6395)
    • ๐Ÿ›  Fixed bug in PBT where initial trial result is empty. (#6351)

    Other Libraries

    • โšก๏ธ [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

    ๐Ÿš€ 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

    ๐Ÿš€ Ray 0.7.7 Release Notes

    Highlights

    • ๐Ÿ‘ Remote functions and actors now support kwargs and positionals (#5606).
    • โฑ ray.get now supports a timeout argument (#6107). If the object isn't available before the timeout passes, a RayTimeoutError is raised.
    • โœ… 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.
    • โž• Added documentation for how to deploy Ray on YARN clusters using Skein (#6119, #6173).
    • โฑ The Ray scheduler now attempts to schedule tasks fairly to avoid starvation (#5851).

    Core

    RLlib

    • ๐Ÿ‘€ Now using pytorch's function to see if gpu is available. #5890
    • ๐Ÿ›  Fixed APEX priorities returning zero all the time. #5980
    • ๐Ÿ›  Fixed leak of TensorFlow assign operations in DQN/DDPG. #5979
    • ๐Ÿ›  Fixed choosing the wrong neural network model for Atari in 0.7.5. #6087
    • โž• Added large scale regression test for RLlib. #6093
    • ๐Ÿ›  Fixed and added test for LR annealing config. #6101
    • โฌ‡๏ธ Reduced log verbosity. #6154
    • โž• 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
    • ๐Ÿ”€ 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 โ€œRunnerโ€ parameter. #5868
    • ๐Ÿ›  Fixed queue_trials so to enable cluster autoscaling with a CPU-Only Head Node. #5900
    • โž• 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

    ๐Ÿš€ Ray 0.7.6 Release Notes

    Highlights

    ๐Ÿ‘ 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.

    ๐Ÿ”Š 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

    • ๐Ÿ”จ Progress towards refactoring the Python worker on top of the core worker. #5750, #5771, #5752
    • ๐Ÿ›  Fix an issue in local mode where multiple actors didn't work properly. #5863
    • ๐Ÿ›  Fix class attributes and methods for actor classes. #5802
    • ๐Ÿ‘Œ Improvements in error messages and handling. #5782, #5746, #5799
    • Serialization improvements. #5841, #5725
    • ๐Ÿ“š Various documentation improvements. #5801, #5792, #5414, #5747, #5780, #5582

    RLlib

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

    Tune

    • ๐Ÿ‘Œ Improved MedianStoppingRule. #5402
    • โž• Add PBT + Memnn example. #5723
    • โž• Add support for function-based stopping condition. #5754
    • ๐Ÿ’พ 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

    ๐Ÿš€ Ray 0.7.4 Release Notes

    Highlights

    ๐Ÿ“š There were many documentation improvements (#5391, #5389, #5175). As we continue to improve the documentation we value your feedback through the โ€œDoc suggestion?โ€ link at the top of the documentation. Notable improvements:

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

    - Weโ€™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
    

    ๐Ÿ“š 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
    ๐Ÿ“ฆ 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!

    ๐Ÿ’ฅ Breaking change: The redis_address parameter was renamed to address (#5412, #5602) and the former will be removed in the future.

    Core

    • ๐Ÿšš Move Java bindings on top of the core worker #5370
    • ๐Ÿ‘Œ Improve log file discoverability #5580
    • Clean up and improve error messages #5368, #5351

    RLlib

    • ๐Ÿ‘Œ Support custom action space distributions #5164
    • โž• Add TensorFlow eager support #5436
    • โž• 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
    • ๐Ÿšš Moved RLlib to top level directory #5324

    Tune

    • Experimental Implementation of the BOHB algorithm #5382
    • ๐Ÿ’ฅ Breaking change: Nested dictionary results are now flattened for CSV writing: {โ€œaโ€: {โ€œbโ€: 1}} => {โ€œa/bโ€: 1} #5346
    • โž• Add Logger for MLFlow #5438
    • ๐Ÿ‘ TensorBoard support for TensorFlow 2.0 #5547
    • โž• Added examples for XGBoost and LightGBM #5500
    • HyperOptSearch now has warmstarting #5372

    Other Libraries

    ๐Ÿ›  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

    • ๐Ÿ‘Œ 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

    • โž• Add directional metrics for components. #4120, #4915
    • Disallow setting resources_per_trial when it is already configured. #4880
    • ๐Ÿ”ง Make PBT Quantile fraction configurable. #4912

    RLlib

    • โž• Add QMIX mixer parameters to optimizer param list. #5014
    • Allow Torch policies access to full action input dict in extra_action_out_fn. #4894
    • ๐Ÿ‘ 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
    • ๐Ÿ“š Documentation
      • Add docs on how to use TF eager execution. #4927
      • Add preprocessing example to offline documentation. #4950

    Other Libraries

    • โž• Add support for distributed training with PyTorch. #4797, #4933
    • ๐Ÿ‘ท Autoscaler will kill workers on exception. #4997
    • ๐Ÿ›  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

    • ๐Ÿ”„ 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
    • ๐Ÿ‘ท Begin moving worker code to C++. #4875, #4899, #4898
    • โฌ†๏ธ Upgrade arrow to latest master. #4858
    • Upload wheels to S3 under <branch-name>/<commit-id>. #4949
    • โž• Add hash table to Redis-Module. #4911
    • ๐ŸŽ‰ Initial support for distributed training with PyTorch. #4797

    Tune

    • Disallow setting resources_per_trial when it is already configured. #4880
    • ๐ŸŽ‰ Initial experiment tracking support. #4362

    RLlib

    • ๐Ÿ—„ 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
    • ๐Ÿ‘ Allow access to batches prior to postprocessing. #4871
    • ๐Ÿ— Port algorithms to build_trainer() pattern. #4823
    • ๐Ÿ‘ท Rename PolicyEvaluator -> RolloutWorker. #4820
    • ๐Ÿšš Rename PolicyGraph -> Policy, move from evaluation/ to policy/. #4819
    • ๐Ÿ‘Œ Support continuous action distributions in IMPALA/APPO. #4771

    ๐Ÿš€ (Revision: 6/23/2019 - Accidentally included commits that were not part of the release.)

  • v0.7.0 Changes

    May 18, 2019

    Core

    • ๐Ÿ›  Backend bug fixes. #4766, #4763, #4605
    • โž• Add experimental API for creating resources at runtime. #3742

    Tune

    RLlib

    • โœ‚ 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
    • ๐Ÿ›  RLLib bug fixes. #4736, #4735, #4652, #4630

    Autoscaler

    • โž• Add an aggressive autoscaling flag. #4285
    • ๐Ÿ›  Autoscalar bug fixes. #4782, #4653
  • v0.6.6 Changes

    April 19, 2019

    Core

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

    Tune

    • โž• Add filter flag for Tune CLI. #4337
    • ๐Ÿ‘ Better handling of tune.function in global checkpoint. #4519
    • โž• Add compatibility to nevergrad 0.2.0+. #4529
    • โž• Add --columns flag for CLI. #4564
    • โž• Add checkpoint eraser. #4490
    • ๐Ÿ›  Fix checkpointing for Gym types. #4619

    RLlib

    • ๐ŸŽ Report sampler performance metrics. #4427
    • Ensure stats are consistently reported across all algos. #4445
    • Cleanup TFPolicyGraph. #4478
    • ๐Ÿ‘ท Make batch timeout for remote workers tunable. #4435
    • ๐Ÿ›  Fix inconsistent weight assignment operations in DQNPolicyGraph. #4504
    • โž• Add support for LR schedule to DQN/APEX. #4473
    • โž• 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
    • ๐Ÿ‘Œ Support torch device and distributions. #4553

    Java

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

    Autoscaler

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