⚡ Bolt: [performance improvement] Optimize YOLO predictions with verbose=False#18
⚡ Bolt: [performance improvement] Optimize YOLO predictions with verbose=False#18kingkillery wants to merge 1 commit into
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… stdout blocking Co-authored-by: kingkillery <200727508+kingkillery@users.noreply.github.com>
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WalkthroughAdded a documentation note describing a YOLO synchronous stdout bottleneck, then implemented the recommended fix by adding Changes
Estimated code review effort🎯 2 (Simple) | ⏱️ ~8 minutes Poem
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Actionable comments posted: 1
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In @.jules/bolt.md:
- Around line 1-3: Update the entry header date to the PR's actual date by
changing the heading text "## 2024-04-13 - Ultralytics YOLO Synchronous stdout
Bottleneck" to "## 2026-04-13 - Ultralytics YOLO Synchronous stdout Bottleneck"
so the timeline in .jules/bolt.md matches the change; leave the rest of the note
(the Learning and Action lines) unchanged.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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⚙️ Run configuration
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📒 Files selected for processing (2)
.jules/bolt.mdcommonforms/inference.py
| ## 2024-04-13 - Ultralytics YOLO Synchronous stdout Bottleneck | ||
| **Learning:** Ultralytics YOLO models by default print bounding box predictions and model details to stdout synchronously for every prediction call. Inside of loops or high-throughput batching, this blocks the main thread and introduces significant I/O latency, tanking performance. | ||
| **Action:** Always set `verbose=False` inside `.predict()` when using YOLO models in production or batch loops where logs are not explicitly needed. |
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Fix the note date to match this change’s actual date.
The heading says 2024-04-13, but this PR was created on 2026-04-13. Keeping the exact date aligned avoids timeline confusion in project learnings.
🛠️ Proposed doc fix
-## 2024-04-13 - Ultralytics YOLO Synchronous stdout Bottleneck
+## 2026-04-13 - Ultralytics YOLO Synchronous stdout Bottleneck📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ## 2024-04-13 - Ultralytics YOLO Synchronous stdout Bottleneck | |
| **Learning:** Ultralytics YOLO models by default print bounding box predictions and model details to stdout synchronously for every prediction call. Inside of loops or high-throughput batching, this blocks the main thread and introduces significant I/O latency, tanking performance. | |
| **Action:** Always set `verbose=False` inside `.predict()` when using YOLO models in production or batch loops where logs are not explicitly needed. | |
| ## 2026-04-13 - Ultralytics YOLO Synchronous stdout Bottleneck | |
| **Learning:** Ultralytics YOLO models by default print bounding box predictions and model details to stdout synchronously for every prediction call. Inside of loops or high-throughput batching, this blocks the main thread and introduces significant I/O latency, tanking performance. | |
| **Action:** Always set `verbose=False` inside `.predict()` when using YOLO models in production or batch loops where logs are not explicitly needed. |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In @.jules/bolt.md around lines 1 - 3, Update the entry header date to the PR's
actual date by changing the heading text "## 2024-04-13 - Ultralytics YOLO
Synchronous stdout Bottleneck" to "## 2026-04-13 - Ultralytics YOLO Synchronous
stdout Bottleneck" so the timeline in .jules/bolt.md matches the change; leave
the rest of the note (the Learning and Action lines) unchanged.
💡 What: Added
verbose=Falseto theself.model.predict()calls withincommonforms/inference.py.🎯 Why: Ultralytics YOLO models print bounding box predictions and model details to stdout synchronously for every prediction call by default. When running inference inside list comprehension loops or through batching, these console logs block the main thread, introducing significant I/O latency that degrades overall inference performance.
📊 Impact: Eliminates unnecessary stdout I/O blocking during inference tasks, making the widget extraction phase noticeably faster, especially when processing multi-page documents or when operating in fast mode (ONNX loop).
🔬 Measurement: Verified code runs successfully by executing the test suite (
uv run pytest tests/) and ensuring it completes successfully and doesn't crash on standard PDFs. Time profiling inference runs natively should verify faster times for batch/multi-page inference.PR created automatically by Jules for task 11795846412233117074 started by @kingkillery
Summary by CodeRabbit
Bug Fixes
Documentation