- config: batch_size_per_card 14 -> 32 (5090 32GB headroom)
- setup_server.sh: pin nvidia-cudnn-cu13>=9.17 to match the sm_120 wheel
(without it conv2d hits "Cannot load symbol cublasLtCreate" abort)
- new scripts/recreate_container.sh: one-shot rebuild with --shm-size 8g,
preserves /root/.netrc so wandb auth survives, runs setup_server.sh
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Global.use_wandb: True + top-level wandb.project=kr_lp_pgnet
- Add wandb to setup_server.sh pip install list
User must run `docker exec -it kr_lp_pgnet wandb login` once before
training so the API key lands in /root/.netrc inside the container.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- Install libgl1 and other OpenCV runtime deps via apt
- Skip paddle/blinker/opencv lines from PaddleOCR requirements (they conflict with the sm_120 wheel or system packages)
- Force opencv>=4.10 (numpy 2 ABI) then pin numpy<2 (imgaug uses removed np.sctypes)
- Smoke-test PGNet module imports before declaring success
Verified end-to-end on RTX 5090 + paddle 3.3.0.dev20251209.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
songhyeonsu