Bump ray[default,rllib] from 2.0.0 to 2.4.0

[dependabot]

Bumps ray[default,rllib] from 2.0.0 to 2.4.0.

Release notes

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

Ray-2.4.0

Ray 2.4 - Generative AI and LLM support

Over the last few months, we have seen a flurry of innovative activity around generative AI models and large language models (LLM). To continue our effort to ensure Ray provides a pivotal compute substrate for generative AI workloads and addresses the challenges (as explained in our blog series), we have invested engineering efforts in this release to ensure that these open source LLM models and workloads are accessible to the open source community and performant with Ray.

This release includes new examples for training, batch inference, and serving with your own LLM.

Generative AI and LLM Examples

• GPT-J (LLM) fine-tuning with Microsoft DeepSpeed and Ray Train
• GPT-J-6B Batch Prediction with Ray Data
• GPT-J-6B Serving with Ray Serve
• Stable Diffusion (Dreambooth) fine-tuning with Ray Train
• Stable Diffusion Batch Prediction with Ray Data
• Stable Diffusion Serving with Ray Serve

Ray Train enhancements

• We're introducing the LightningTrainer, allowing you to scale your PyTorch Lightning on Ray. As part of our continued effort for seamless integration and ease of use, we have enhanced and replaced our existing ray_lightning integration, which was widely adopted, with the latest changes to Pytorch Lighting.
• we’re releasing an AccelerateTrainer, allowing you to run HuggingFace Accelerate and DeepSpeed on Ray with minimal code changes. This Trainer integrates with the rest of the Ray ecosystem—including the ability to run distributed hyperparameter tuning with each trial being a distributed training job.

Ray Data highlights

• Streaming execution is enabled by default, providing users with a more efficient data processing pipeline that can handle larger datasets and minimize memory consumption. Check out the docs here: (doc)
• We've implemented asynchronous batch prefetching of Dataset.iter_batches (doc), improving performance by fetching data in parallel while the main thread continues processing, thus reducing waiting time.
• Support reading SQL databases (doc), enabling users to seamlessly integrate relational databases into their Ray Data workflows.
• Introduced support for reading WebDataset (doc), a common format for high-performance deep learning training jobs.

Ray Serve highlights

• Multi-app CLI & REST API support is now available, allowing users to manage multiple applications with different configurations within a single Ray Serve deployment. This simplifies deployment and scaling processes for users with multiple applications. (doc)
• Enhanced logging and metrics for Serve applications, giving users better visibility into their application's performance and facilitating easier debugging and monitoring. (doc)

Other enhancements

• Ray 2.4 is the last version that supports Python 3.6
• We've also added a brand new landing page

Ray Libraries

Ray AIR

💫Enhancements:

• Add nightly test for alpa opt 30b inference. (#33419)
• Add a sanity checking release test for Alpa and ray nightly. (#32995)
• Add TorchDetectionPredictor (#32199)
• Add artifact_location, run_name to MLFlow integration (#33641)
• Add *path properties to Result and ResultGrid (#33410)
• Make Preprocessor.transform lazy by default (#32872)
• Make BatchPredictor lazy (#32510, #32796)
• Use a configurable ray temp directory for the TempFileLock util (#32862)
• Add collate_fn to iter_torch_batches (#32412)
• Allow users to pass Callable[[torch.Tensor], torch.Tensor] to TorchVisionTransform (#32383)
• Automatically move DatasetIterator torch tensors to correct device (#31753)

🔨 Fixes:

... (truncated)

Commits

• cd1ba65 [docker] Disable docker builds for code cherry picks (#34744)
• 4479f66 Cherry pick doc PRs #34614 #34615 #34435 #34505 #34617 #34623 #34660 (#34676)
• fb34fc3 [train] Add AccelerateTrainer as valid AIR_TRAINER (#34639) (#34657)
• b0c23a9 [CI] fix virtualenv version to deflake linux://python/ray/tests:test_runtime_...
• 558b26b [Ci] fix pip version to deflake minimal install 3.10
• e935be9 [docker] Enable docker builds for code cherry picks (#34649)
• d5d34c1 Revert "[core]Turn on light weight resource broadcasting. (#32625)" (#34636)
• a8d7c9c [Doc] Add missing links for LightningTrainer and HuggingfaceTrainer (#34612)
• 6fc9f70 [Doc] Fix AIR benchmark configuration link failure(with pinned commit id). #3...
• d2804d9 [cherry pick][docs] for new landing page for 2.4.0 (#34546)
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[dependabot]

Superseded by #146.