distributed
https://github.com/dask/distributed
Python
A distributed task scheduler for Dask
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- Issues
- Increasing value of `OMP_NUM_THREADS` reduces performance even when controlling for `n_workers` and `threads_per_worker`
- `RuntimeError: Not enough arguments provided: missing keys` in `dask.persist` with mix of `Future` and `Delayed`
- Memory leak when submitting futures
- Repeated calls to `memory_color` take around 12% of CPU time of scheduler
- Worker plugin can not be registered on worker unless its entire package source uploaded on server
- Improve structure of event logging
- PubSub functionality kills stream-based connections due to race conditions
- Client.map() keys as a list breaks Dask queue system
- Support collective style tasks
- [QST][Bug?] Can I fit/evaluate many XGBoost models on the same cluster?
- Docs
- Python not yet supported