better worker allocation for gunicorn + CUDA environment #265
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- test_issue_243.py: Test script to replicate VRAM duplication issue - ISSUE_243_ANALYSIS.md: Initial analysis of gunicorn/CUDA issue - ISSUE_243_REAL_WORLD_ANALYSIS.md: Analysis of whisper-wrapper implementation These are investigation/documentation artifacts, not SDK changes.
Removed outdated files: - test_issue_243.py (app-level test, no longer relevant) - ISSUE_243_ANALYSIS.md (superseded) - ISSUE_243_REAL_WORLD_ANALYSIS.md (superseded) New consolidated documentation: - ISSUE_243_INVESTIGATION.md: Complete investigation with SDK-level solution Key changes from previous analysis: - Focus on SDK-level VRAM management (not app-level) - Runtime VRAM checking via enhanced _profile_cuda_memory decorator - _get_model_requirements() API for apps to declare memory needs - Conservative worker count when CUDA detected - Runtime status via ?includeVRAM=true parameter - Addresses dynamic VRAM availability (not static calculation) - Process-safe torch.cuda.empty_cache() usage documented
Updated investigation document with: - Component 5: Automatic Memory Profiling - 80% VRAM requirement for first request (conservative) - Historical measurement for subsequent requests - Hash-based filenames for race-condition-safe persistence - Atomic writes via temp file + rename - Updated request flow to show 3-level priority: 1. App override (explicit) 2. Historical measurement 3. Conservative 80% - Updated implementation checklist with new components - Revised open questions and conclusion
… isn't sufficient
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…hon version that provides pamameter hashing
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## develop #265 +/- ##
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Coverage ? 59.45%
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Files ? 6
Lines ? 846
Branches ? 0
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Hits ? 503
Misses ? 343
Partials ? 0
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this turned out to be a massive over-engineering based on wrong assumption and outdated information. Closing without merge. Will start a new PR to address the issue more neatly. |
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addresses #243