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Ray v0.7.1 Release Notes

Release Date: 2019-06-23 // almost 5 years ago

  • Core

    • 🔄 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
    • 👷 Begin moving worker code to C++. #4875, #4899, #4898
    • ⬆️ Upgrade arrow to latest master. #4858
    • Upload wheels to S3 under <branch-name>/<commit-id>. #4949
    • ➕ Add hash table to Redis-Module. #4911
    • 🎉 Initial support for distributed training with PyTorch. #4797

    Tune

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

    RLlib

    • 🗄 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
    • 👍 Allow access to batches prior to postprocessing. #4871
    • 🏗 Port algorithms to build_trainer() pattern. #4823
    • 👷 Rename PolicyEvaluator -> RolloutWorker. #4820
    • 🚚 Rename PolicyGraph -> Policy, move from evaluation/ to policy/. #4819
    • 👌 Support continuous action distributions in IMPALA/APPO. #4771

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