[Data] Estimate object store memory from in-flight tasks #42504
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
bveeramani
requested review from
ericl,
scv119,
c21,
amogkam,
scottjlee,
raulchen,
stephanie-wang and
Zandew
as code owners
bveeramani
commented
Jan 19, 2024
python/ray/data/_internal/execution/streaming_executor_state.py
Outdated
Show resolved
Hide resolved
Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
raulchen
reviewed
Jan 22, 2024
python/ray/data/_internal/execution/interfaces/op_runtime_metrics.py
Outdated
Show resolved
Hide resolved
Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
raulchen
approved these changes
Jan 24, 2024
| @@ -311,10 +311,13 @@ def base_resource_usage(self) -> ExecutionResources: | |||
| def current_resource_usage(self) -> ExecutionResources: | |||
| # Both pending and running actors count towards our current resource usage. | |||
| num_active_workers = self._actor_pool.num_total_actors() | |||
| object_store_memory = self.metrics.obj_store_mem_cur | |||
| if self.metrics.obj_store_mem_pending is not None: | |||
| object_store_memory += self.metrics.obj_store_mem_pending | |||
There was a problem hiding this comment.
(not blocking this PR. just a note.)
I plan to move this method to OpRuntimeMetrics. Currently it's weird that we have 2 different places reporting resource metrics.
Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Why are these changes needed?
Ray Data's streaming executor launches as many as 50 tasks in a single scheduling step. If the executor doesn't account for the potential output of in-flight tasks, it launches too many tasks (since tasks don't immediately output data) and causes spilling.
This PR fixes the issue by considering data buffered at the Ray Core level to computations of topology resource usage.
Related issue number
Fixes #42374
Checks
git commit -s) in this PR.scripts/format.shto lint the changes in this PR.method in Tune, I've added it in
doc/source/tune/api/under thecorresponding
.rstfile.