All Versions
25
Latest Version
Avg Release Cycle
31 days
Latest Release
1263 days ago

Changelog History
Page 3

  • v0.6.5 Changes

    March 25, 2019

    Core

    • 👷 Build system fully converted to Bazel. #4284, #4280, #4281
    • Introduce a set data structure in the GCS. #4199
    • 👉 Make all arguments to _remote() optional. #4305
    • 👌 Improve object transfer latency by setting TCP_NODELAY on all TCP connections. #4318
    • ➕ Add beginning of experimental serving module. #4095
    • ✂ Remove Jupyter notebook based UI. #4301
    • ➕ Add ray timeline command line command for dumping Chrome trace. #4239

    Tune

    • ➕ Add custom field for serializations. #4237
    • Begin adding Tune CLI. #3983, #4321, #4322
    • ➕ Add optimization to reuse actors. #4218
    • ➕ Add warnings if the Tune event loop gets clogged. #4353
    • Switch preferred API from tune.run_experiments to tune.run. #4234
    • 🌲 Make the logging from the function API consistent and predictable. #4011

    RLlib

    • 💥 Breaking: Flip sign of entropy coefficient in A2C and Impala. #4374
    • ➕ Add option to continue training even if some workers crash. #4376
    • ➕ Add asynchronous remote workers. #4253
    • ➕ Add callback accessor for raw observations. #4212

    Java

    • 👌 Improve single-process mode. #4245, #4265
    • 📦 Package native dependencies into jar. #4367
    • 🎉 Initial support for calling Python functions from Java. #4166

    Autoscaler

    • ⏪ Restore error messages for setup errors. #4388

    Known Issues

    • Object broadcasts on large clusters are inefficient. #2945

  • v0.6.4 Changes

    March 06, 2019

    💥 Breaking

    • Removed redirect_output and redirect_worker_output from ray.init, removed deprecated _submit method. #4025
    • 🚚 Move TensorFlowVariables to ray.experimental.tf_utils. #4145

    Core

    • 0️⃣ Stream worker logging statements to driver by default. #3892
    • ➕ Added experimental ray signaling mechanism, see the documentation. #3624
    • 👷 Make Bazel the default build system. #3898
    • Preliminary experimental streaming API for Python. #4126
    • ➕ Added web dashboard for monitoring node resource usage. #4066
    • 👌 Improved propagation of backend errors to user. #4039
    • Many improvements for the Java frontend. #3687, #3978, #4014, #3943, #3839, #4038, #4039, #4063, #4100, #4179, #4178
    • 👌 Support for dataclass serialization. #3964
    • Implement actor checkpointing. #3839
    • First steps toward cross-language invocations. #3675
    • 👍 Better defaults for Redis memory usage. #4152

    Tune

    • 💥 Breaking : Introduce ability to turn off default logging. Deprecates custom_loggers. #4104
    • 👌 Support custom resources. #2979
    • ➕ Add initial parameter suggestions for HyperOpt. #3944
    • ➕ Add scipy-optimize to Tune. #3924
    • ➕ Add Nevergrad. #3985
    • ➕ Add number of trials to the trial runner logger. #4068
    • 👌 Support RESTful API for the webserver. #4080
    • 👍 Local mode support. #4138
    • Dynamic resources for trials. #3974

    RLlib

    • Basic infrastructure for off-policy estimation. #3941
    • ➕ Add simplex action space and Dirichlet action distribution. #4070
    • Exploration with parameter space noise. #4048
    • Custom supervised loss API. #4083
    • ➕ Add torch policy gradient implementation. #3857

    Autoscaler and Cluster Setup

    • ➕ Add docker run option (e.g. to support nvidia-docker). #3921

    Modin

    Known Issues

    • Object broadcasts on large clusters are inefficient. #2945
    • IMPALA is broken #4329

  • v0.6.3 Changes

    March 06, 2019

    Core

    • 🎉 Initial work on porting the build system to Bazel. #3918, #3806, #3867, #3842
    • 👍 Allow starting Ray processes inside valgrind, gdb, tmux. #3824, #3847
    • 🛠 Stability improvements and bug fixes. #3861, #3962, #3958, #3855, #3736, #3822, #3821, #3925
    • Convert Python C extensions to Cython. #3541
    • 👷 ray start can now be used to start Java workers. #3838, #3852
    • 🏗 Enable LZ4 compression in pyarrow build. #3931
    • ⚡️ Update Redis to version 5.0.3. #3886
    • 👉 Use one memory-mapped file for Plasma store. #3871,

    Tune

    • 👌 Support for BayesOpt. #3864
    • 👌 Support for SigOpt. #3844
    • 👌 Support executing infinite recovery retries for a trial. #3901
    • 👌 Support export_formats option to export policy graphs. #3868
    • 🌲 Cluster and logging improvements. #3906

    RLlib

    • 👌 Support for Asynchronous Proximal Policy Optimization (APPO). #3779
    • 👌 Support for MARWIL. #3635
    • 👌 Support for evaluation option in DQN. #3835
    • 🐛 Bug fixes. #3865, #3810, #3938
    • Annotations for API stability. #3808

    Autoscaler and Cluster Setup

    Modin

    Known Issues

    • Object broadcasts on large clusters are inefficient. #2945

  • v0.6.2 Changes

    January 17, 2019

    💥 Breaking Changes

    • ⏱ The timeout argument of ray.wait now uses seconds instead of milliseconds. #3706

    Core

    • 0️⃣ Limit default redis max memory to 10GB. #3630
    • Define a Node class to manage Ray processes. #3733
    • Garbage collection of actor dummy objects. #3593
    • Split profile table among many keys in the GCS. #3676
    • 🐳 Automatically try to figure out the memory limit in a docker container. #3605
    • 👌 Improve multi-threading support. #3672
    • ⚠ Push a warning to all users when large number of workers have been started. #3645
    • 🔨 Refactor code ray.ObjectID code. #3674

    Tune

    • 🔄 Change log handling for Tune. #3661
    • 👍 Tune now supports resuming from cluster failure. #3309, #3725, #3657, #3681
    • 👌 Support Configuration Merging for Suggestion Algorithms. #3584
    • 👌 Support nested PBT mutations. #3455

    RLlib

    • ➕ Add starcraft multiagent env as example. #3542
    • 👍 Allow development without needing to compile Ray. #3623
    • 📚 Documentation for I/O API and multi-agent improvements. #3650
    • Export policy model checkpoint. #3637
    • 🔨 Refactor PyTorch custom model support. #3634

    Autoscaler

    • ➕ Add an initial_workers option. #3530
    • ➕ Add kill and get IP commands to CLI for testing. #3731
    • 🔧 GCP allow manual network configuration. #3748

    Known Issues:

    • Object broadcasts on large clusters are inefficient. #2945

  • v0.6.1 Changes

    December 24, 2018

    Core

    • ➕ Added experimental option to limit Redis memory usage. #3499
    • ➕ Added option for restarting failed actors. #3332
    • 🛠 Fixed Plasma TensorFlow operator memory leak. #3448
    • 🛠 Fixed compatibility issue with TensorFlow and PyTorch. #3574
    • 🔨 Miscellaneous code refactoring and cleanup. #3563 #3564 #3461 #3511
    • 📚 Documentation. #3427 #3535 #3138
    • Several stability improvements. #3592 #3597

    RLlib

    • 👍 Multi-GPU support for Multi-agent PPO. #3479
    • Unclipped actions are sent to learner. #3496
    • rllib rollout now also preprocesses observations. #3512
    • Basic Offline Data API added. #3473
    • 👌 Improvements to metrics reporting in DQN. #3491
    • AsyncSampler no longer auto-concats. #3556
    • QMIX Implementation (Experimental). #3548
    • 🐎 IMPALA performance improvements. #3402
    • 👍 Better error messages. #3444
    • 🐎 PPO performance improvements. #3552

    Autoscaler

    Ray Tune

    • Lambdas now require tune.function wrapper. #3457
    • 🔀 Custom loggers, sync functions, and trial names are now supported. #3465
    • 👌 Improvements to fault tolerance. #3414
    • 📄 Variant Generator docs clarification. #3583
    • trial_resources now renamed to resources_per_trial. #3580

    Modin

    • Modin 0.2.5 is now bundled with Ray
    • 👍 Greater than memory support for object store. #3450

    Known Issues

    • Object broadcasts on large clusters are inefficient. #2945