Comments (2)
This might be kind of a dumb question, but how well is dask able to predict a priori the memory requirements of any task before the task is dispatched to a worker? If dask had 10 workers each with 1GB RAM each would it know if it had a number of operations in the graph that required some 10 GB workers?
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Dask only knows the size of the result of a task after the task has computed. It would expand the network on-the-fly as it saw the total memory use increasing and that it still had more tasks to compute.
In normal operation Dask assumes that all tasks can run on all workers unless the user explicitly states otherwise (doc page, issue for expansion) so Dask would fail in the case of the 10GB task in the presence of the 1GB worker (unless we were to implement the issue linked to above).
The motivation for different deployment policies would be to remove the need for users to think about the size of their cluster, both in terms of scaling out, and in terms of cleaning up afterwards.
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Related Issues (20)
- Munge authentication failure on SGE HOT 6
- Refresh DRMAACluster docstring HOT 1
- test_adaptive's test_adaptive_memory times out HOT 2
- CI failure in test_stop_single_worker on Python 3 HOT 2
- Unable to start SGE workers via docker-compose on Windows 8.1 HOT 6
- Move back towards Distributed's _retire_workers method HOT 5
- Make resource constraints first class citizens
- Release? HOT 2
- How to limit number of threads for a DRMAACluster worker? HOT 7
- Allow setting ip of scheduler HOT 1
- Docstring of `DRMAACluster` does not match function signature HOT 1
- outputPath and errorPath are ignored HOT 3
- New release? HOT 6
- Give absolute path to worker script HOT 4
- Does not work with user install of dask HOT 4
- Closing all workers HOT 2
- test_stop_single_worker failing on CI HOT 4
- worker_info dict has no key 'name' HOT 3
- distributed.utils.ignoring no longer exists - should be replaced with contextlib.suppress HOT 4
- Archive this repository HOT 6
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