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