« Changelog History


Ray v0.6.4 Release Notes

Release Date: 2019-03-06 // about 5 years ago

  • 💥 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