ray-0.6.6

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

ray-0.6.5

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

ray-0.6.4

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

• 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

• Upgrade Modin to 0.3.1, see the release notes. #4058

Known Issues

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

ray-0.6.3

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

• Faster cluster launch and update. #3720
• Bug fixes. #3916, #3860, #3937, #3782, #3969
• Kubernetes configuration improvements. #3875, #3909

Modin

• Update Modin to 0.3.0. #3936
  • Modin 0.3.0 release notes

Known Issues

• Object broadcasts on large clusters are inefficient. #2945

ray-0.6.2

Breaking Changes

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

Core

• 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

ray-0.6.1

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

• Bug fixes, botocore handling fix. #3454 #3503 #3447

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
  • import modin after import ray
  • Modin 0.2.5 release notes
• Greater than memory support for object store. #3450

Known Issues

• Object broadcasts on large clusters are inefficient. #2945

ray-0.6.0

Breaking Changes

• Renamed _submit to _remote. #3321
• Object store memory capped at 20GB by default. #3243
• Now ray.global_state.client_table() returns a list instead of a dictionary.
• Renamed ray.global_state.dump_catapult_trace to ray.global_state.chrome_tracing_dump.

Known Issues

• The Plasma TensorFlow operator leaks memory. #3404
• Object broadcasts on large clusters are inefficient. #2945
• Ape-X leaks memory. #3452
• Action clipping can impede learning (please set clip_actions: False as a workaround) #3496

Core

• New raylet backend on by default and legacy backend removed. #3020 #3121
• Support for Python 3.7. #2546
• Support for fractional resources (e.g., GPUs).
• Added ray stack for improved debugging (to get stack traces of Python processes on current node). #3213
• Better error messages for low-memory conditions. #3323
• Log file names reorganized under /tmp/ray/. #2862
• Improved timeline visualizations. #2306 #3255

Modin

• Modin is shipped with Ray. After running import ray you can run import modin. #3109

RLlib

• Multi agent support for Ape-X and IMPALA. #3147
• Multi GPU support for IMPALA. #2766
• TD3 optimizations for DDPG. #3353
• Support for Dict and Tuple observation spaces. #3051
• Support for parametric and variable-length action spaces. #3384
• Support batchnorm layers. #3369
• Support custom metrics. #3144

Autoscaler

• Added ray submit for submitting scripts to clusters. #3312
• Added --new flag for ray attach. #2973
• Added option to allow private IPs only. #3270

Tune

• Support for fractional GPU allocations for trials. #3169
• Better checkpointing and setup. #2889
• Memory tracking and notification. #3298
• Bug fixes for SearchAlgorithms. #3081
• Add a raise_on_failed_trial flag in run_experiments. #2915
• Better handling of node failures. #3238

Training

• Experimental support for distributed SGD. #2858 #3033

ray-0.5.3

API

• Add ray.is_initialized() to check if ray.init() has been called. #2818

Fixes and Improvements

• Fix issue in which ray stop fails to kill plasma object store. #2850
• Remove dependence on psutil. #2892

RLlib

• Set better default for VF clip PPO parameter to avoid silent performance degradation. #2921
• Reward clipping should default to off for non-Atari environments. #2904
• Fix LSTM failing to train on truncated sequences. #2898

Tune

• Fixed a small bug in trial pausing and cleaned up error messages. #2815

ray-0.5.2

Breaking Changes

• Local mode has changed from ray.init(driver_mode=ray.PYTHON_MODE) to ray.init(local_mode=True) to improve clarity.

Autoscaler and Cluster Setup

• Added many convenience commands such as ray up, ray attach, ray exec, and ray rsync to simplify launching jobs with Ray.
• Added experimental support for local/on-prem clusters.

RLlib

• Added the IMPALA algorithm.
• Added the ARS algorithm.
• Added the A2C variant of A3C.
• Added support for distributional DQN.
• Made improvements to multiagent support.
• Added support for model-based rollouts and custom policies.
• Added initial set of reference Atari results.

Tune

• SearchAlgorithms can now be used separately from TrialSchedulers and are found in ray.tune.suggest.
• All TrialSchedulers have been consolidated under ray.tune.schedulers.
• Minor API changes:
  • For Experiment configuration, repeat has been renamed to num_samples.
  • Now, register_trainable is handled implicitly.

ray-0.5.0

Bump version to 0.5.0. (#2351)