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google-deepmind / meltingpot

A suite of test scenarios for multi-agent reinforcement learning.
Apache License 2.0
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Bump ray[default,rllib] from 2.0.0 to 2.2.0 #91

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps ray[default,rllib] from 2.0.0 to 2.2.0.

Release notes

Sourced from ray[default,rllib]'s releases.

Ray-2.2.0

Release Highlights

Ray 2.2 is a stability-focused release, featuring stability improvements across many Ray components.

  • Ray Jobs API is now GA. The Ray Jobs API allows you to submit locally developed applications to a remote Ray Cluster for execution. It simplifies the experience of packaging, deploying, and managing a Ray application.
  • Ray Dashboard has received a number of improvements, such as the ability to see cpu flame graphs of your Ray workers and new metrics for memory usage.
  • The Out-Of-Memory (OOM) Monitor is now enabled by default. This will increase the stability of memory-intensive applications on top of Ray.
  • [Ray Data] we’ve heard numerous users report that when files are too large, Ray Data can have out-of-memory or performance issues. In this release, we’re enabling dynamic block splitting by default, which will address the above issues by avoiding holding too much data in memory.

Ray Libraries

Ray AIR

🎉 New Features:

  • Add a NumPy first path for Torch and TensorFlow Predictors (#28917)

💫Enhancements:

  • Suppress "NumPy array is not writable" error in torch conversion (#29808)
  • Add node rank and local world size info to session (#29919)

🔨 Fixes:

  • Fix MLflow database integrity error (#29794)
  • Fix ResourceChangingScheduler dropping PlacementGroupFactory args (#30304)
  • Fix bug passing 'raise' to FailureConfig (#30814)
  • Fix reserved CPU warning if no CPUs are used (#30598)

📖Documentation:

  • Fix examples and docs to specify batch_format in BatchMapper (#30438)

🏗 Architecture refactoring:

  • Deprecate Wandb mixin (#29828)
  • Deprecate Checkpoint.to_object_ref and Checkpoint.from_object_ref (#30365)

Ray Data Processing

🎉 New Features:

  • Support all PyArrow versions released by Apache Arrow (#29993, #29999)
  • Add select_columns() to select a subset of columns (#29081)
  • Add write_tfrecords() to write TFRecord files (#29448)
  • Support MongoDB data source (#28550)
  • Enable dynamic block splitting by default (#30284)
  • Add from_torch() to create dataset from Torch dataset (#29588)
  • Add from_tf() to create dataset from TensorFlow dataset (#29591)
  • Allow to set batch_size in BatchMapper (#29193)
  • Support read/write from/to local node file system (#29565)

💫Enhancements:

  • Add include_paths in read_images() to return image file path (#30007)
  • Print out Dataset statistics automatically after execution (#29876)

... (truncated)

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