distributed
https://github.com/dask/distributed
Python
A distributed task scheduler for Dask
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- Issues
- Ability to specify cost value for computations
- Worker profile limited to a short timespan
- Annotation of positional arguments *args in dask.distributed.Client.submit API (2024.1.1)
- `Scheduler`/`Worker` threads hang when calling `sys.exit()`
- `mindeps` CI environment includes `numpy` and `pandas`
- Bump actions/checkout from 4.1.3 to 4.1.5
- Do not initialize logging on import
- Avoid deepcopy when submitting graph
- Support collective style tasks
- [QST][Bug?] Can I fit/evaluate many XGBoost models on the same cluster?
- Docs
- Python not yet supported