Runtime optimizations for backlogger. Caching to optimize from O(n) to O(1).

[enlorenz]

No description provided.

[coderabbitai]

No actionable comments were generated in the recent review. 🎉

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📝 Walkthrough

Walkthrough

Adds cache-backed pending-job statistics and a mutation/versioning mechanism to avoid recomputation when queue/backlog are unchanged (environment.py). Adjusts plot filename tokens to include drop_weight and rename job_age token (plot.py). Handles all-fixed-weights case in weight combination generation (train_iter.py).

Changes

Cohort / File(s)	Summary
Job statistics & queue/backlog mutation src/environment.py	Adds _queue_backlog_version, _cached_queue_backlog_version, and _mark_queue_backlog_mutation(); tracks queue/backlog mutations in step(), fast-paths _update_pending_job_stats() when versions match; recomputes backlog metrics from backlog_queue and updates self.state when mutated.
Plot filename tokens src/plot.py	Changes saved filename prefix format: renames the job-age token from d to a and appends a new d token for drop_weight (string construction only).
Fixed-weights combination handling train_iter.py	Adds branch for zero variable-weights: if fixed weights sum ≈ 1.0, builds a single combination placing fixed values in their positions and appends it to combinations. Existing multi-variable branches unchanged.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~22 minutes

Possibly related PRs

• Next empty slot bump #2 — Touches ComputeClusterEnv step() and job-queue operations; likely overlaps with the queue/backlog mutation/versioning changes here.
• Rework of Rewards and Queueing + Small hotfixes #17 — Modifies src/environment.py queue/backlog handling and step() workflow; closely related to the pending-stats caching and mutation tracking changes.

🚥 Pre-merge checks | ✅ 2 | ❌ 2

❌ Failed checks (1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 28.57% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.
Description check	❓ Inconclusive	No pull request description was provided by the author; the field is empty and therefore cannot be evaluated for relevance or clarity.	Add a pull request description explaining the purpose, scope, and impact of the runtime optimizations and caching changes for better context.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title accurately describes the main optimization changes across multiple files: caching mechanism in environment.py to avoid O(n) recomputation, plot.py filename adjustments, and train_iter.py weight handling improvements.
Merge Conflict Detection	✅ Passed	✅ No merge conflicts detected when merging into master

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[coderabbitai]

Actionable comments posted: 1

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (1)

src/plot.py (1)

272-272: ⚠️ Potential issue | 🟡 Minor

Inconsistent prefix format in plot_cumulative_savings — missing drop_weight and using old d label for job_age.

Line 94 was updated to e{eff}_p{price}_i{idle}_a{job_age}_d{drop}, but line 272 still uses the old format e{eff}_p{price}_i{idle}_d{job_age}, which:

1. Labels job_age_weight as d instead of a.
2. Omits drop_weight entirely.

Proposed fix

-        prefix = f"e{env.weights.efficiency_weight}_p{env.weights.price_weight}_i{env.weights.idle_weight}_d{env.weights.job_age_weight}"
+        prefix = f"e{env.weights.efficiency_weight}_p{env.weights.price_weight}_i{env.weights.idle_weight}_a{env.weights.job_age_weight}_d{env.weights.drop_weight}"

🤖 Fix all issues with AI agents

In `@src/environment.py`:
- Around line 215-220: The fast-path cache that returns early when
_cached_queue_count == queue_count and _cached_backlog_size ==
current_backlog_size is unsafe because equal counts can hide content changes;
replace this count-only invalidation with a robust versioning or hashing
approach: introduce a monotonically increasing mutation/version id (e.g.,
_queue_backlog_version) that is incremented on every queue or backlog mutation
(every place that modifies the queue/backlog such as enqueue/assign/refill
routines), store that version alongside _cached_* (e.g.,
_cached_queue_backlog_version) and only skip recalculation when the versions
match; alternatively compute and compare a lightweight content checksum of the
active queue/backlog before skipping. Ensure fields referenced in observations
(pending_core_hours, pending_avg_duration, pending_max_nodes) are recomputed
unless the version/hash indicates no mutation.

🧹 Nitpick comments (2)

src/environment.py (2)

216-217: Redundant hasattr guards — attributes are always initialized in _reset_timeline_state.

_cached_queue_count and _cached_backlog_size are initialized at lines 207–208 in _reset_timeline_state, which is called from __init__ before any step() can invoke _update_pending_job_stats. The hasattr checks add unnecessary overhead on every step.

Proposed simplification (assuming the cache logic itself is fixed)

-        if (hasattr(self, '_cached_queue_count') and 
-            hasattr(self, '_cached_backlog_size') and
-            self._cached_queue_count == queue_count and 
+        if (self._cached_queue_count == queue_count and 
             self._cached_backlog_size == current_backlog_size):

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231-235: List comprehensions over deque are fine here but could use islice for very large backlogs.

These iterate the entire backlog deque three separate times to build three arrays. If the backlog grows large, a single-pass extraction would be more efficient:

Single-pass alternative

-            backlog_durations = np.array([job[0] for job in self.backlog_queue], dtype=np.int32)
-            backlog_nodes = np.array([job[2] for job in self.backlog_queue], dtype=np.int32)
-            backlog_cores = np.array([job[3] for job in self.backlog_queue], dtype=np.int32)
+            backlog_data = np.array([(job[0], job[2], job[3]) for job in self.backlog_queue], dtype=np.int32)
+            backlog_durations = backlog_data[:, 0]
+            backlog_nodes = backlog_data[:, 1]
+            backlog_cores = backlog_data[:, 2]