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 2.9.1 #50

Open dependabot[bot] opened 10 months ago

dependabot[bot] commented 10 months ago

Bumps ray from 2.4.0 to 2.9.1.

Release notes

Sourced from ray's releases.

Ray-2.9.1

This patch release contains fixes for Ray Core, Ray Data, and Ray Serve.

Ray Core

🔨 Fixes:

  • Adding debupgy as the ray debugger (#42311)
  • Fix task events profile events per task leak (#42248)
  • Make sure redis sync context and async context connect to the same redis instance (#42040)

Ray Data

🔨 Fixes:

  • [Data] Retry write if error during file clean up (#42326)

Ray Serve

🔨 Fixes:

  • Improve handling the websocket server disconnect scenario (#42130)
  • Fix pydantic config documentation (#42216)
  • Address issues under high network delays:
    • Enable setting queue length response deadline via environment variable (#42001)
    • Add exponential backoff for queue_len_response_deadline_s (#42041)

Ray-2.9.0

Release Highlights

  • This release contains fixes for the Ray Dashboard. Additional context can be found here: https://www.anyscale.com/blog/update-on-ray-cves-cve-2023-6019-cve-2023-6020-cve-2023-6021-cve-2023-48022-cve-2023-48023 
  • Ray Train has now upgraded support for spot node preemption -- allowing Ray Train to handle preemption node failures differently than application errors.
  • Ray is now compatible with Pydantic versions =2.5.0, addressing a piece of user feedback we’ve consistently received.
  • The Ray Dashboard now has a page for Ray Data to monitor real-time execution metrics.
  • Streaming generator is now officially a public API (#41436, #38784). Streaming generator allows writing streaming applications easily on top of Ray via Python generator API and has been used for Ray Serve and Ray data for several releases. See the documentation for details. 
  • We’ve added experimental support for new accelerators: Intel GPU (#38553), Intel Gaudi Accelerators (#40561), and Huawei Ascend NPU (#41256).

Ray Libraries

Ray Data

🎉 New Features:

💫 Enhancements:

  • Optimize OpState.outqueue_num_blocks (#41748)
  • Improve stall detection for StreamingOutputsBackpressurePolicy (#41637)
  • Enable read-only Datasets to be executed on new execution backend (#41466, #41597)
  • Inherit block size from downstream ops (#41019)
  • Use runtime object memory for scheduling (#41383)

... (truncated)

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