FE-1412: Scale the Python client's timeout for seeded trials

[kube]

🌟 What is the purpose of this PR?

A trial's seeded runs execute sequentially in the CLI, so one optimization.evaluate may take seedsPerTrial × the single-run time. The client's fixed 240 s response deadline must scale with it.

FE-1468 (#9262) has merged; this PR now sits at the bottom of stack #9280 on main, with FE-1470 (#9278) above.

🔗 Related links

• FE-1412 (internal): this PR
• FE-1408 (internal): parent — seeded trials in the CLI

🔍 What does this change?

In @local/petrinaut-python:

• describe() reads the typed study.seedsPerTrial, rejects values outside 1–100 as a protocol error, and multiplies the per-response deadline by it.
• evaluate() describes first when the session has not been described, so the scaled deadline is in place before the first trial.
• A new end-to-end test drives a two-seed trial against the built CLI and asserts the derived seed sequence [42, 1013904268], the per-seed replicates, and the mean objective.

The seeded runs are sequential, so the image needs no worker permissions or pool caps.

Review fixes. The hand-rolled MAX_SEEDS_PER_TRIAL constant and its range check are deleted: FE-1468's schema bounds seedsPerTrial to 1–100, so an out-of-range value already fails model validation in _validated, which closes the session and raises PetrinautProtocolError — the same observable behaviour with one owner for the bound.

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

• modifies workspaces but not a publishable library.

📜 Does this require a change to the docs?

The changes in this PR:

• require changes to docs which are made as part of this PR: the bindings and optimizer READMEs document the scaled deadline.

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

• do not affect the execution graph.

🛡 What tests cover this?

• Bindings (28 tests): timeout scaling from the described seedsPerTrial, rejection of out-of-range values, and the two-seed e2e against the built CLI.
• apps/petrinaut-opt: 75 tests, unchanged.

❓ How to test this?

turbo run test:unit --filter @local/petrinaut-python --filter @apps/petrinaut-opt

🤖 Generated with Claude Code

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Project	Deployment	Actions	Updated (UTC)
hash	Ready	Preview	Aug 21, 2026 3:20pm
petrinaut	Ready	Preview	Aug 21, 2026 3:20pm
petrinaut-docs	Ready	Preview	Aug 21, 2026 3:20pm

1 Skipped Deployment

Project	Deployment	Actions	Updated (UTC)
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[cursor]

PR Summary

Medium Risk
Changes how long the Python client waits for CLI evaluations (up to 100× the base 240s deadline). Wrong scaling could hang studies or kill valid multi-seed trials.

Overview
The Python bindings now multiply the protocol read deadline by the study’s seedsPerTrial after describe(), because one evaluate can run that many simulations sequentially.

If evaluate() is called first, it describes once so the scaled timeout is in place before the trial. Out-of-range seedsPerTrial still fails via the existing pydantic schema (1–100). Docs note that petrinaut-opt ignores per-seed replicates and uses the mean objective.

Tests cover timeout scaling, implicit describe-on-evaluate, invalid seeds, and a two-seed e2e against the real CLI (derived seeds [42, 1013904268]).

^{Reviewed by Cursor Bugbot for commit d4126df. Bugbot is set up for automated code reviews on this repo. Configure here.}

[Copilot]

Pull request overview

Scales Python optimization response timeouts for sequential seeded trials and documents the behavior.

Changes:

• Validates seedsPerTrial and scales response deadlines.
• Adds unit and CLI end-to-end coverage.
• Documents seeded execution and timeout behavior.

Reviewed changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
session.py	Stores the base response timeout.
optimization.py	Validates seed count and scales timeouts.
test_optimization_session.py	Tests scaling and invalid values.
test_e2e_cli.py	Tests a two-seed CLI trial.
Python README.md	Documents scaled deadlines.
Optimizer README.md	Documents sequential seeded trials.

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[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
⚠️ Please upload report for BASE (cf/fe-1468-python-pydantic-models-from-cli-schemas@ecae501). Learn more about missing BASE report.

Additional details and impacted files

@@                                  Coverage Diff                                  @@
##             cf/fe-1468-python-pydantic-models-from-cli-schemas    #9229   +/-   ##
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  Coverage                                                      ?   59.63%           
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  Files                                                         ?     1421           
  Lines                                                         ?   138778           
  Branches                                                      ?     6557           
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  Hits                                                          ?    82756           
  Misses                                                        ?    54958           
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[codspeed-hq]

Merging this PR will degrade performance by 15.38%

❌ 2 regressed benchmarks
✅ 96 untouched benchmarks

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Benchmark	BASE	HEAD	Efficiency
❌	bit_matrix/dense/iter_row[64]	140.8 ns	170 ns	-17.16%
❌	bit_matrix/dense/iter_row[200]	185.8 ns	215 ns	-13.57%

Tip

Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.

---

_{Comparing cf/fe-1412-python-client-support-seeded-trials-in-the-optimizer-service (279a4bc) with cf/fe-1270-create-python-bindings-to-petrinaut-core (2009728)}

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[github-actions]

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2002	$$26.0 \mathrm{ms} \pm 298 \mathrm{μs}\left({\color{gray}1.83 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.42 \mathrm{ms} \pm 14.4 \mathrm{μs}\left({\color{gray}-2.860 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1002	$$13.3 \mathrm{ms} \pm 121 \mathrm{μs}\left({\color{red}7.08 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$44.2 \mathrm{ms} \pm 393 \mathrm{μs}\left({\color{gray}2.45 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.7 \mathrm{ms} \pm 135 \mathrm{μs}\left({\color{lightgreen}-5.009 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1527	$$24.7 \mathrm{ms} \pm 230 \mathrm{μs}\left({\color{gray}2.31 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$27.3 \mathrm{ms} \pm 199 \mathrm{μs}\left({\color{gray}2.07 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.80 \mathrm{ms} \pm 19.7 \mathrm{μs}\left({\color{gray}-1.649 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.5 \mathrm{ms} \pm 105 \mathrm{μs}\left({\color{gray}1.09 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.75 \mathrm{ms} \pm 23.2 \mathrm{μs}\left({\color{gray}0.870 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.02 \mathrm{ms} \pm 15.2 \mathrm{μs}\left({\color{gray}0.204 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 52	$$3.42 \mathrm{ms} \pm 23.8 \mathrm{μs}\left({\color{gray}1.76 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.11 \mathrm{ms} \pm 31.0 \mathrm{μs}\left({\color{gray}0.785 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.59 \mathrm{ms} \pm 25.9 \mathrm{μs}\left({\color{gray}-0.090 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 108	$$4.11 \mathrm{ms} \pm 20.7 \mathrm{μs}\left({\color{gray}-0.747 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.37 \mathrm{ms} \pm 28.4 \mathrm{μs}\left({\color{gray}1.19 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.50 \mathrm{ms} \pm 25.7 \mathrm{μs}\left({\color{gray}0.920 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.10 \mathrm{ms} \pm 23.2 \mathrm{μs}\left({\color{gray}0.608 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.70 \mathrm{ms} \pm 15.3 \mathrm{μs}\left({\color{gray}0.319 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.54 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}-0.654 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 2	$$2.67 \mathrm{ms} \pm 13.0 \mathrm{μs}\left({\color{gray}-0.387 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.97 \mathrm{ms} \pm 17.7 \mathrm{μs}\left({\color{gray}0.232 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.75 \mathrm{ms} \pm 13.7 \mathrm{μs}\left({\color{gray}0.316 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.93 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}-0.277 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.02 \mathrm{ms} \pm 16.9 \mathrm{μs}\left({\color{gray}-1.109 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.76 \mathrm{ms} \pm 16.1 \mathrm{μs}\left({\color{gray}-0.347 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 26	$$2.93 \mathrm{ms} \pm 27.6 \mathrm{μs}\left({\color{gray}-2.428 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.42 \mathrm{ms} \pm 22.4 \mathrm{μs}\left({\color{gray}0.373 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.99 \mathrm{ms} \pm 18.9 \mathrm{μs}\left({\color{gray}-1.171 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 27	$$3.23 \mathrm{ms} \pm 19.4 \mathrm{μs}\left({\color{gray}-2.576 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.37 \mathrm{ms} \pm 23.4 \mathrm{μs}\left({\color{gray}1.01 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 17.2 \mathrm{μs}\left({\color{gray}0.359 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.27 \mathrm{ms} \pm 23.9 \mathrm{μs}\left({\color{gray}-0.129 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$43.7 \mathrm{ms} \pm 245 \mathrm{μs}\left({\color{gray}2.13 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$33.9 \mathrm{ms} \pm 185 \mathrm{μs}\left({\color{gray}-0.850 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$36.6 \mathrm{ms} \pm 218 \mathrm{μs}\left({\color{gray}0.383 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$47.3 \mathrm{ms} \pm 1.34 \mathrm{ms}\left({\color{gray}4.56 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$41.9 \mathrm{ms} \pm 221 \mathrm{μs}\left({\color{gray}-1.026 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$50.0 \mathrm{ms} \pm 308 \mathrm{μs}\left({\color{gray}0.152 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$40.6 \mathrm{ms} \pm 300 \mathrm{μs}\left({\color{gray}-0.425 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$92.8 \mathrm{ms} \pm 655 \mathrm{μs}\left({\color{gray}0.286 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$34.1 \mathrm{ms} \pm 206 \mathrm{μs}\left({\color{lightgreen}-36.660 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$275 \mathrm{ms} \pm 825 \mathrm{μs}\left({\color{lightgreen}-10.571 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$11.0 \mathrm{ms} \pm 55.2 \mathrm{μs}\left({\color{gray}0.638 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$11.2 \mathrm{ms} \pm 67.5 \mathrm{μs}\left({\color{gray}-0.192 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$11.1 \mathrm{ms} \pm 59.1 \mathrm{μs}\left({\color{gray}-0.051 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$11.1 \mathrm{ms} \pm 72.0 \mathrm{μs}\left({\color{gray}0.696 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$11.0 \mathrm{ms} \pm 57.6 \mathrm{μs}\left({\color{gray}-0.145 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$11.0 \mathrm{ms} \pm 64.8 \mathrm{μs}\left({\color{gray}-0.113 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$11.2 \mathrm{ms} \pm 68.8 \mathrm{μs}\left({\color{gray}0.699 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$11.2 \mathrm{ms} \pm 57.0 \mathrm{μs}\left({\color{gray}-0.046 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$11.2 \mathrm{ms} \pm 83.9 \mathrm{μs}\left({\color{gray}-0.423 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$11.4 \mathrm{ms} \pm 63.2 \mathrm{μs}\left({\color{gray}-1.079 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity

Function	Value	Mean	Flame graphs
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/block/v/1	$$11.8 \mathrm{ms} \pm 104 \mathrm{μs}\left({\color{gray}1.52 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$11.5 \mathrm{ms} \pm 63.8 \mathrm{μs}\left({\color{gray}0.753 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$11.4 \mathrm{ms} \pm 63.3 \mathrm{μs}\left({\color{gray}-0.394 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$11.5 \mathrm{ms} \pm 62.7 \mathrm{μs}\left({\color{gray}0.597 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$11.6 \mathrm{ms} \pm 68.4 \mathrm{μs}\left({\color{gray}0.363 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$11.5 \mathrm{ms} \pm 66.6 \mathrm{μs}\left({\color{gray}0.050 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$11.5 \mathrm{ms} \pm 53.9 \mathrm{μs}\left({\color{gray}-0.332 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$11.5 \mathrm{ms} \pm 60.6 \mathrm{μs}\left({\color{gray}-0.759 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$11.5 \mathrm{ms} \pm 77.9 \mathrm{μs}\left({\color{gray}0.668 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.53 \mathrm{ms} \pm 51.7 \mathrm{μs}\left({\color{gray}-0.047 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$59.8 \mathrm{ms} \pm 586 \mathrm{μs}\left({\color{red}6.55 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$112 \mathrm{ms} \pm 895 \mathrm{μs}\left({\color{gray}2.93 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$67.1 \mathrm{ms} \pm 556 \mathrm{μs}\left({\color{red}5.06 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$76.5 \mathrm{ms} \pm 584 \mathrm{μs}\left({\color{gray}4.44 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$85.2 \mathrm{ms} \pm 615 \mathrm{μs}\left({\color{gray}2.70 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$91.2 \mathrm{ms} \pm 604 \mathrm{μs}\left({\color{gray}1.93 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$43.8 \mathrm{ms} \pm 305 \mathrm{μs}\left({\color{gray}0.120 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$72.5 \mathrm{ms} \pm 478 \mathrm{μs}\left({\color{gray}-0.556 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$50.6 \mathrm{ms} \pm 376 \mathrm{μs}\left({\color{gray}1.05 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$60.0 \mathrm{ms} \pm 358 \mathrm{μs}\left({\color{gray}1.06 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$62.2 \mathrm{ms} \pm 388 \mathrm{μs}\left({\color{gray}0.883 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$62.1 \mathrm{ms} \pm 341 \mathrm{μs}\left({\color{gray}-0.134 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$126 \mathrm{ms} \pm 590 \mathrm{μs}\left({\color{gray}-1.620 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$135 \mathrm{ms} \pm 533 \mathrm{μs}\left({\color{gray}-2.366 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$19.3 \mathrm{ms} \pm 126 \mathrm{μs}\left({\color{gray}0.366 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$543 \mathrm{ms} \pm 1.03 \mathrm{ms}\left({\color{gray}2.99 \mathrm{\%}}\right) $$	Flame Graph

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