distributed
https://github.com/dask/distributed
Python
A distributed task scheduler for Dask
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- Issues
- Queuing does not prevent root task overproduction unless you have enough tasks
- [DNM] Is >=20 worker round-robin even tested?
- Pick worker with lowest memory use by percentage, not absolute
- [DNM] `queued`<->`no-worker` transitions
- LocalCluster does not apply worker resources as documented
- Cache root-ish-ness for consistency
- Queuing: lowest-memory worker as tiebreaker
- Remove deprecated LoopRunner API
- Non-blocking operations on scheduler
- Polars DataFrame Support
- Docs
- Python not yet supported