094602_5133e925 ยท completed_without_training
# Training diagnosis: 20261027 / 094602_5133e925 State: **completed_without_training**. Trained predictions: 0; scored records: 0; folds with epoch losses: 0. Reviewed dataset labels: 0. - cnn2d_unet: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - dgcnn: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - fno2d: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - gaussian_process: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - gaussian_process: requires_training_labels (30 records): GP requires a fitted training posterior; no random/synthetic prior score emitted - meshgraphnet: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - physics_risk_gnn: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - pointnet: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - sparse_unet: unavailable_backend (30 records): RuntimeError: RuntimeError: Sparse U-Net unavailable: install optional MinkowskiEngine; dense tensors are not a sparse substitute. - sparse_unet: unavailable_backend (90 records): RuntimeError: Sparse U-Net unavailable: install optional MinkowskiEngine; dense tensors are not a sparse substitute. - tabular_mlp: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - unet3d: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) - uvnet: blocked_missing_reviewed_labels (90 records): No eligible CAD-node labels in this split (reviewed, or explicitly enabled weak mappings that passed geometry checks) 1. Review CAD-node risk labels against the exact geometry_sha256 and node order; use each dataset's review_template.json. 2. CSV X/Y/W/H are image pixels. Supply a reviewed pixel-to-CAD mapping before treating them as CAD labels. 3. Run main.py --prepared-manifest <saved prepared_manifest.json> --labels-dir <reviewed-label-folder> to retain the exact labeled geometry. 4. sparse_unet requires MinkowskiEngine. Choose available models with --models if that optional backend is absent.
