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DynamicVRAM's on-demand model loading/offloading conflicted with process isolation in three ways: RPC tensor transport stalls from mid-call GPU offload, race conditions between model lifecycle and active RPC operations, and false positive memory leak detection from changed finalizer patterns. - Marshal CUDA tensors to CPU before RPC transport for dynamic models - Add operation state tracking + quiescence waits at workflow boundaries - Distinguish proxy reference release from actual leaks in cleanup_models_gc - Fix init order: DynamicVRAM must initialize before isolation proxies - Add RPC timeouts to prevent indefinite hangs on model unavailability - Prevent proxy-of-proxy chains from DynamicVRAM model reload cycles - Add torch.device/torch.dtype serializers for new DynamicVRAM RPC paths - Guard isolation overhead so non-isolated workflows are unaffected - Migrate env var to PYISOLATE_CHILD |
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| README.md | ||
| test_adapter.py | ||
Automated Testing
Running tests locally
Additional requirements for running tests:
pip install pytest
pip install websocket-client==1.6.1
opencv-python==4.6.0.66
scikit-image==0.21.0
Run inference tests:
pytest tests/inference
Quality regression test
Compares images in 2 directories to ensure they are the same
- Run an inference test to save a directory of "ground truth" images
pytest tests/inference --output_dir tests/inference/baseline
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Make code edits
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Run inference and quality comparison tests
pytest