Bump ray from 2.40.0 to 2.46.0

[dependabot]

Bumps ray from 2.40.0 to 2.46.0.

Release notes

Sourced from ray's releases.

Ray-2.46.0

Release Highlights

The 2.46 Ray release comes with a couple core highlights:

1. Ray Data now supports hash shuffling for repartition and aggregations, along with support for joins. This enables many new data processing workloads to be run on Ray Data. Please give it a try and let us know if you have any feedback!
2. Ray Serve LLM now supports vLLM v1 to be forward-compatible with upcoming vLLM releases. This also opens up significant performance improvements that come with vLLM's v1 refactor.
3. There is a new Train Grafana dashboard which provides in-depth metrics on Grafana for better metrics on training workloads.

Ray Libraries

Ray Data

🎉 New Features:

• Adding support for hash-shuffle based repartitioning and aggregations (#52664)
• Added support for Joins (using hash-shuffle) (#52728)
• [LLM] vLLM support upgrades to 0.8.5 (#52344)

💫 Enhancements:

• Add memory attribute to ExecutionResources (#51127)
• Support ray_remote_args for read_tfrecords #52450
• [data.dashboard] Skip reporting internal metrics (#52666)
• Add PhysicalOperator.min_max_resource_usage_bounds (#52502)
• Speed up printing the schema (#52612)
• [data.dashboard] Dataset logger for worker (#52706)
• Support new pyiceberg version (#51744)
• Support num_cpus, memory, concurrency, batch_size for preprocess (#52574)

🔨 Fixes:

• Handle Arrow Array null types in to_numpy (#52572)
• Fix S3 serialization wrapper compatibility with RetryingPyFileSystem (#52568)
• Fixing Optimizer to apply rules until plan stabilize; (#52663)
• Fixing FuseOperators rule to properly handle the case of transformations drastically changing size of the dataset (#52570)

📖 Documentation:

• [LLM] Improve concurrency settings, improve prompt to achieve better throughput (#52634)

Ray Train

🎉 New Features:

• Add initial Train Grafana dashboard (#52709)

💫 Enhancements:

• Lazily import torch FSDP for ray.train.torch module to improve performance and reduce unnecessary dependencies (#52707)
• Deserialize the user-defined training function directly on workers, improving efficiency (#52684)

🔨 Fixes:

• Fixed error when no arguments are passed into TorchTrainer (#52693)

📖 Documentation:

• Added new XGBoostTrainer user guide (#52355)

🏗 Architecture refactoring:

• Re-enabled isort for python/ray/train to maintain code formatting consistency (#52717)

... (truncated)

Commits

• c3dd2ca 2.46.0 version change (#52774)
• 4919aa4 [train] add XGBoostTrainer user guide (#52355)
• d74d17c [core] Remove worker_context_ dependency from the task receiver (#52740)
• c6c338c [Data] Added support for Joins (using hash-shuffle) (#52728)
• a7f1f24 [Data] support num_cpus, memory, concurrency, batch_size for preprocess (#52574)
• 715a416 [data] support new pyiceberg version (#51744)
• fca1963 Fix typo in Ray Tune PyTorch Lightning docs (#52756)
• 7704041 [core] Deflake test_logging.py (#52749)
• 10cc0f2 [core] Deflake and speed up test_gcs_fault_tolerance.py (#52743)
• 279815b [Serve.llm] Add router replicas and batch size to llm config (#52655)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #70.