fix: use pulp internal timelimit, instead of stopit by fgvieira · Pull Request #2938 · snakemake/snakemake

fgvieira · 2024-07-03T11:16:26Z

There are some issues of the solver stalling (or not behaving as expected) when running very large DAGs (e.g. #871, #1003, #1374, #1620, #2354).

To minimize those issues, I was wondering if it would make sense for the solver/scheduler to evaluate just a subset (randomly sampled) of the jobs waiting to run.

QC

The PR contains a test case for the changes or the changes are already covered by an existing test case.
The documentation (docs/) is updated to reflect the changes or this is not necessary (e.g. if the change does neither modify the language nor the behavior or functionalities of Snakemake).

Summary by CodeRabbit

New Features
- Enhanced job scheduling with improved timeout handling using asynchronous programming for better performance and responsiveness.
Chores
- Updated project dependencies, replacing stopit with timeout_decorator to support modern asynchronous handling.

johanneskoester · 2024-07-04T11:27:03Z

snakemake/scheduler.py

+                    # Subsample jobs to be run (to speedup solver)
+                    n_total_needrun = len(needrun)
+                    solver_max_jobs = int(os.environ.get("SNAKEMAKE_SOLVER_MAX_JOBS", 1000))
+                    if n_total_needrun > solver_max_jobs:
+                        import random
+                        needrun = set(random.sample(tuple(needrun), k=solver_max_jobs))
+


Mhm, interesting idea. We already have a 10s timeout for the ILP solver, which leads to falling back to the greedy solver that should be very fast. The stalling that people observe, does it happen because this fallback does not work, or is the greedy solver too slow as well to solve those instances?

The timeout is checked in line 602 (609 in this PR).

fgvieira · 2024-07-04T13:47:03Z

Some cases are quite old, but it seems to be mostly that the timeout was not triggered (and changing to greedy fixed the issue), but in some very large DAGs it seems to still persist with the greedy algorithm.

johanneskoester · 2024-07-05T10:49:56Z

Some cases are quite old, but it seems to be mostly that the timeout was not triggered (and changing to greedy fixed the issue), but in some very large DAGs it seems to still persist with the greedy algorithm.

Could be that the timeout was introduced after those issues. I would vote for the following:

keep the current timeout approach, for the greedy fallback though, also have a timeout which then does a subsample if 10s are exceeded.

fgvieira · 2024-07-11T19:05:17Z

Maybe the issue is with the timeout. I am running a workflow locally now and, even though there are no jobs running, no new jobs are being launched. If I look on top the only thing running is a cbc job (ILP solver) that has been running for 4,5 hours:

 cbc /tmp/206393e6a5dd4ee79cc331f95fc3a77a-pulp.mps -max -timeMode elapsed -branch -printingOptions all -solution /tmp/206393e6a5dd4ee79cc331f95fc3a77a-pulp.sol

If I kill it, it says:

Failed to solve scheduling problem with ILP solver in time (10s). Falling back to greedy solver.

and starts launching jobs again.

coderabbitai · 2024-08-13T10:00:27Z

Walkthrough

The recent changes involve a transition in the Snakemake codebase to utilize timeout_decorator instead of stopit for managing timeouts within job scheduling. This update includes modifications to the job selection logic, specifically refactoring the _solve_ilp method to enhance its configuration and error handling. Additionally, the removal of stopit from both the main and testing dependencies streamlines the project’s requirements.

Changes

Files	Change Summary
`snakemake/scheduler.py`	Replaced `stopit` with `timeout_decorator` for timeout management. Refactored `_solve_ilp` method to accept additional parameters and return status, with improved exception handling.
`setup.cfg`	Updated `install_requires`: removed `stopit`, added `timeout_decorator` to reflect the new dependency requirements.
`test-environment.yml`	Modified dependencies: removed `stopit`, added `timeout_decorator` to align the testing environment with the new handling of timeouts.

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johanneskoester · 2024-08-13T10:23:59Z

Maybe the issue is with the timeout. I am running a workflow locally now and, even though there are no jobs running, no new jobs are being launched. If I look on top the only thing running is a cbc job (ILP solver) that has been running for 4,5 hours:
 cbc /tmp/206393e6a5dd4ee79cc331f95fc3a77a-pulp.mps -max -timeMode elapsed -branch -printingOptions all -solution /tmp/206393e6a5dd4ee79cc331f95fc3a77a-pulp.sol
If I kill it, it says:
Failed to solve scheduling problem with ILP solver in time (10s). Falling back to greedy solver.
and starts launching jobs again.

stopit, the package we use for the timeout cannot time while the GIL is hold (https://pypi.org/project/stopit/#threading-based-resources), so that it might wait for the solving to be completed. This suprises me though because I tested this before and it worked fine.

One option to try is to use a different kind of timeout instead that can handle this without requiring the GIL to be released. Pypi lists a lot of packages.

fgvieira · 2024-08-13T11:33:41Z

I can try using another package. I am considering either wrapt_timeout_decorator or async-timeout.

Any preference?

coderabbitai

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snakemake/path_modifier.py (1 hunks)
snakemake/scheduler.py (2 hunks)

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264-264: Trailing comma missing

Add trailing comma

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279-279: Trailing comma missing

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snakemake/path_modifier.py (1)

Line range hint 13-13:
Consider the impact of removing debug logging.

The removal of the debug logging statement in the modify method reduces verbosity, which can improve performance. However, ensure that this does not hinder debugging efforts, especially in scenarios where path modification issues are common.

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36-36: Missing return type annotation for public function modify

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36-36: Missing type annotation for self in method

(ANN101)

36-36: Missing type annotation for function argument path

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36-36: Missing type annotation for function argument property

(ANN001)

snakemake/scheduler.py (1)

261-270: Ensure correctness of subsampling logic.

The subsampling mechanism uses random.sample() to limit the number of jobs. Verify that the environment variable SNAKEMAKE_SOLVER_MAX_JOBS is correctly set and that the subsampling logic does not inadvertently exclude critical jobs.

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snakemake/scheduler.py

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617-617: Local variable e is assigned to but never used

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test-environment.yml (1)

10-10: Dependency update approved.

The replacement of stopit with async-timeout aligns with the transition to asynchronous programming. This change is appropriate for enhancing non-blocking operations.

setup.cfg (1)

58-58: Dependency update approved.

The replacement of stopit with async-timeout is consistent with the shift towards asynchronous programming. This update should enhance timeout handling.

snakemake/scheduler.py (2)

495-495: Update import statements for asynchronous timeout handling.

The import of timeout and TimeoutError from async_timeout replaces the previous stopit imports. This change is necessary for transitioning to asynchronous timeout management.

615-617: Approve asynchronous timeout handling in get_temp_sizes_gb.

The use of async with timeout(10) effectively handles timeouts in an asynchronous context, aligning with the PR's objectives.

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Add return type annotation: None

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616-616: Missing type annotation for function argument self

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616-616: Missing type annotation for function argument secs

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snakemake/scheduler.py (1)

495-495: LGTM!

The transition to asynchronous timeout handling using async_timeout is well-implemented.

snakemake/scheduler.py

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snakemake/scheduler.py

495-495: wrapt_timeout_decorator.timeout imported but unused

Remove unused import: wrapt_timeout_decorator.timeout

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643-643: Undefined name timeout

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test-environment.yml (1)

10-10: Dependency change approved.

The replacement of stopit with wrapt_timeout_decorator aligns with the PR objectives to improve timeout management. Ensure that the new package is compatible with the rest of the dependencies.

setup.cfg (1)

58-58: Dependency change approved.

The replacement of stopit with wrapt_timeout_decorator aligns with the PR objectives to improve timeout management. Ensure that the new package is compatible with the rest of the dependencies.

snakemake/scheduler.py (1)

615-622: Exception handling approved.

The transition from TimeoutException to TimeoutError aligns with the new asynchronous timeout strategy. Ensure that all related code paths are tested.

snakemake/scheduler.py

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Do not suggest type annotations for functions that are defined inside of functions or methods.

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test-environment.yml (1)

71-71: Dependency update approved.

The replacement of stopit with wrapt_timeout_decorator aligns with the PR objectives to improve timeout management.

snakemake/scheduler.py (3)

642-643: Import change approved.

The import of wrapt_timeout_decorator is appropriate for the new asynchronous timeout management strategy.

614-618: Exception handling update approved.

The update to handle TimeoutError is consistent with the use of wrapt_timeout_decorator.

644-644: Decorator usage approved.

The @timeout(10) decorator is correctly applied to enforce a timeout on the _solve_ilp method.

johanneskoester · 2024-08-21T09:25:36Z

I can try using another package. I am considering either wrapt_timeout_decorator or async-timeout.

Any preference?

wrapt seems like a good choice, as async-timeout maybe makes things unnecessarily complex because it is async, also it might have the same GIL issues.

fgvieira · 2024-08-22T11:01:56Z

@johanneskoester I looked a bit more into the pulp binary, and it seems it has an option to limit the time it takes to find the solution. If it cannot find one in the given time limit, it just reports a sub-optimal one.
Any reason why you decided not to use it? I think it would make for a much better solution.

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Outside diff range, codebase verification and nitpick comments (2)

snakemake/scheduler.py (2)
613-623: Consider parameterizing the timeout value.

The timeout value of 10 seconds is hardcoded in the call to _solve_ilp. Consider making this a configurable parameter to allow for flexibility in different scenarios.
-        status = self._solve_ilp(prob, time_limit=10)
+        status = self._solve_ilp(prob, time_limit=self.workflow.scheduling_settings.ilp_timeout)
Ensure that ilp_timeout is defined in the scheduling settings.

Line range hint 640-663: Improve handling of the PATH environment variable.

The manipulation of the PATH environment variable could be made safer and clearer by using a context manager to ensure it is restored even if an exception occurs.
from contextlib import contextmanager

@contextmanager
def temporary_path_prepend(path):
    old_path = os.environ["PATH"]
    os.environ["PATH"] = f"{path}:{old_path}"
    try:
        yield
    finally:
        os.environ["PATH"] = old_path

# Usage in _solve_ilp
with temporary_path_prepend(self.workflow.scheduling_settings.solver_path):
    solver = (
        pulp.getSolver(self.workflow.scheduling_settings.ilp_solver)
        if self.workflow.scheduling_settings.ilp_solver
        else pulp.apis.LpSolverDefault
    )

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sonarqubecloud · 2024-08-22T13:12:29Z

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johanneskoester · 2024-09-04T12:09:57Z

@johanneskoester I looked a bit more into the pulp binary, and it seems it has an option to limit the time it takes to find the solution. If it cannot find one in the given time limit, it just reports a sub-optimal one. Any reason why you decided not to use it? I think it would make for a much better solution.

Also an interesting idea indeed. One should evaluate whether the greedy or the suboptimal solution is preferable. For now, let us merge this in order to get the fix out.

johanneskoester

Nice!

johanneskoester · 2024-09-04T12:13:09Z

@johanneskoester I looked a bit more into the pulp binary, and it seems it has an option to limit the time it takes to find the solution. If it cannot find one in the given time limit, it just reports a sub-optimal one. Any reason why you decided not to use it? I think it would make for a much better solution.

Also an interesting idea indeed. One should evaluate whether the greedy or the suboptimal solution is preferable. For now, let us merge this in order to get the fix out.

Ah, now I get what you meant. Indeed, the solution provided here is perfect already. We use the internal timeout functionality of pulp but still use greedy to solve the scheduling problem if the solution is not optimal. Nice!

🤖 I have created a release *beep* *boop* --- ## [8.19.1](v8.19.0...v8.19.1) (2024-09-04) ### Bug Fixes * fix issues with misinterpretation of max-jobs-per-timespan and max-jobs-per-seconds ([#3067](#3067)) ([d82453b](d82453b)) * pip deployment path ([#3062](#3062)) ([bf9305b](bf9305b)) * return empty set if rate limiter at max ([#3060](#3060)) ([4e59963](4e59963)) * use wrapt_timeout_decorator, instead of stopit ([#2938](#2938)) ([3b64e41](3b64e41)) * Wrong linenumbers reported when linting ([#2985](#2985)) ([3a8bd36](3a8bd36)) ### Documentation * update `doc-environment.yml` file and Documentation Setup documentation ([#3058](#3058)) ([a540a2e](a540a2e)) --- This PR was generated with [Release Please](https://github.com/googleapis/release-please). See [documentation](https://github.com/googleapis/release-please#release-please). --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: Johannes Köster <johannes.koester@tu-dortmund.de>

hsiehph · 2024-09-06T16:04:08Z

I got a question about this... is it recommended to use --scheduler greedy --scheduler-ilp-solver PULP_CBC_CMD for large workflow (>200,000 jobs) with v8.19.3? Using --scheduler greedy alone helped but it became slow again after running through 60% of the pipeline. not sure why that is the case .... Any suggestions are welcome!

fgvieira · 2024-09-06T19:11:41Z

With this latest patch, I'd say to not use greedy as it will try to use ILP for 10 seconds and, if it cannot find an optimal solution, switch to greedy.

fgvieira requested a review from johanneskoester as a code owner July 3, 2024 11:16

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Subsample jobs to speedup solver

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This was referenced Sep 4, 2024

Solver occasionally does not converge(?) so that the workflow does not start #871

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pulp filling up /tmp to 100% for many jobs - with workaround? #1003

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Greedy solver not chosen despite ILP cbc taking >6min and thus >10s #1374

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chore(main): release 8.19.1 #3065

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Customizable timeouts for the ILP scheduler #1620

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Snakemake very slow on large workflow #2354

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snakemake 8.19.1 Homebrew/homebrew-core#183441

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fgvieira changed the title ~~fix: use wrapt_timeout_decorator, instead of stopit~~ fix: use pulp internal timelimit, instead of stopit Sep 29, 2024

peterjc mentioned this pull request Feb 17, 2025

deprecation warning from stopit - pkg_resources is deprecated as an API #3020

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feat: migrate to scheduler plugin interface and scheduler plugins #3676

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victorlin mentioned this pull request Feb 9, 2026

CI: No module named 'pkg_resources' nextstrain/cli#507

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coderabbitai bot commented Aug 13, 2024 •

edited

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CodeRabbit Configuration File (`.coderabbit.yaml`)

fgvieira commented Aug 22, 2024 •

edited

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hsiehph commented Sep 6, 2024 •

edited by fgvieira

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