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StephanAkkerman / crypto-forecasting-benchmark

This repository contains the codebase used in the research conducted for the paper titled "Benchmarking Cryptocurrency Forecasting Models in the Context of Data Properties and Market Factors." The study involved a rigorous assessment of thirteen different time series forecasting models over twenty-one cryptocurrencies and four distinct time frames.
MIT License
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Bump ray from 2.4.0 to 2.8.0 #41

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Bumps ray from 2.4.0 to 2.8.0.

Release notes

Sourced from ray's releases.

Ray-2.8.0

Release Highlights

This release features stability improvements and API clean-ups across the Ray libraries.

  • In Ray Serve, we are deprecating the previously experimental DAG API for deployment graphs. Model composition will be supported through deployment handles providing more flexibility and stability. The previously deprecated Ray Serve 1.x APIs have also been removed. We’ve also added a new Java APIs that aligns with the Ray Serve 2.x APIs. More API changes in the release notes below.
  • In RLlib, we’ve moved 24 algorithms into rllib_contrib (still available within RLlib for Ray 2.8).
  • We’ve added support for PyTorch-compatible input files shuffling for Ray Data. This allows users to randomly shuffle input files for better model training accuracy. This release also features new Ray Data datasources for Databricks and BigQuery.
  • On the Ray Dashboard, we’ve added new metrics for Ray Data in the Metrics tab. This allows users to monitor Ray Data workload including real time metrics of cluster memory, CPU, GPU, output data size, etc. See the doc for more details.
  • Ray Core now supports profiling GPU tasks or actors using Nvidia Nsight. See the documentation for instructions.
  • We fixed 2 critical bugs raised by many kuberay / ML library users, including a child process leak issue from Ray worker that leaks the GPU memory (#40182) and an job page excessive loading time issue when Ray HA cluster restarts a head node (#40742)
  • Python 3.7 support is officially deprecated from Ray.

Ray Libraries

Ray Data

🎉 New Features:

  • Add support for shuffling input files (#40154)
  • Support streaming read of PyTorch dataset (#39554)
  • Add BigQuery datasource (#37380)
  • Add Databricks table / SQL datasource (#39852)
  • Add inverse transform functionality to LabelEncoder (#37785)
  • Add function arg params to Dataset.map and Dataset.flat_map (#40010)

💫Enhancements:

  • Hard deprecate DatasetPipeline (#40129)
  • Remove BulkExecutor code path (#40200)
  • Deprecate extraneous Dataset parameters and methods (#40385)
  • Remove legacy iteration code path (#40013)
  • Implement streaming output backpressure (#40387)
  • Cap op concurrency with exponential ramp-up (#40275)
  • Store ray dashboard metrics in _StatsActor (#40118)
  • Slice output blocks to respect target block size (#40248)
  • Drop columns before grouping by in Dataset.unique() (#40016)
  • Standardize physical operator runtime metrics (#40173)
  • Estimate blocks for limit and union operator (#40072)
  • Store bytes spilled/restored after plan execution (#39361)
  • Optimize sample_boundaries in SortTaskSpec (#39581)
  • Optimization to reduce ArrowBlock building time for blocks of size 1 (#38833)

🔨 Fixes:

  • Fix bug where _StatsActor errors with PandasBlock (#40481)
  • Remove deprecated do_write (#40422)
  • Improve error message when reading HTTP files (#40462)
  • Add flag to skip get_object_locations for metrics (#39884)
  • Fall back to fetch files info in parallel for multiple directories (#39592)
  • Replace deprecated .pieces with updated .fragments (#39523)
  • Backwards compatibility for Preprocessor that have been fit in older versions (#39173)
  • Removing unnecessary data copy in convert_udf_returns_to_numpy (#39188)
  • Do not eagerly free root RefBundles (#39016)

📖Documentation:

... (truncated)

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dependabot[bot] commented 1 year ago

Superseded by #45.