docs/gpu_experiments/README.md

GPU experiments for the Geoderma rebuttal (GEODER-D-26-01032)

Deadline: 2026-05-29.

Context

These two experiments address the only remaining GPU-dependent reviewer requests for this revision. Every CPU-based analysis is already complete in rebuttal/; the headline numbers (Table 2 bootstrap CIs, β_year sensitivities, NN-distance distribution, residual-SD breakdown, land-use regression, spatial-vs-random split comparison, T3.1 multi-run spatial-CV evidence) live in rebuttal/rebuttal_numbers.md.

Reviewer concern	Experiment
R1.3 single spatial split is weak	Experiment 1
R3.6 no confidence intervals on Table 2	Experiment 1
R3.8 no proper test set	Experiment 1
R3.9 no uncertainty quantification	Experiment 2
R4.4 uncertainty maps expected in DSM	Experiment 2

The two existing checkpoints (Model A, Model B) and which experiment uses which are documented in checkpoint_note.md. No retraining of the existing checkpoints is needed. Experiment 1 trains 5 fresh models from scratch (one per fold). Experiment 2 uses the already-trained Model B for stochastic inference only.

Files

rebuttal/gpu_experiments/
├── README.md                    ← this file
├── checkpoint_note.md           ← Model A vs Model B disambiguation
├── spatial_kfold/
│   └── run_kfold.py             ← Experiment 1
└── uncertainty/
    ├── mc_dropout_inference.py  ← Experiment 2, step 1: inference
    └── plot_uncertainty.py      ← Experiment 2, step 2: figure

After running, the outputs land in those same directories alongside the scripts (see each script's docstring for the full output list).

Experiment 1 — Spatial 5-fold CV (≈ 15 GPU hours)

• Script: spatial_kfold/run_kfold.py
• Command:

  cd /home/valerian/SGTPublication
  python rebuttal/gpu_experiments/spatial_kfold/run_kfold.py
  

• Outputs (for the manuscript):
  • spatial_kfold/kfold_results.md — paste this Table into manuscript §2.5
  • spatial_kfold/figure_kfold.png — new Figure (supplement or main)
  • spatial_kfold/kfold_predictions_all_folds.parquet — per-row test predictions (lon, lat, OC_actual, OC_predicted, fold_id, year, altitude)
• Answers: R1.3, R3.6, R3.8

Experiment 2 — MC Dropout uncertainty (≈ 3 GPU hours)

• Scripts:
  • uncertainty/mc_dropout_inference.py — run first; writes GeoTIFFs + parquet
  • uncertainty/plot_uncertainty.py — run second; builds the 3-panel figure
• Commands:

  cd /home/valerian/SGTPublication
  python rebuttal/gpu_experiments/uncertainty/mc_dropout_inference.py
  python rebuttal/gpu_experiments/uncertainty/plot_uncertainty.py
  

• Outputs (for the manuscript):
  • uncertainty/SGT_1mil_2023_mean_mc30.tif — MC mean prediction (UTM 32N, 250 m)
  • uncertainty/SGT_1mil_2023_std_mc30.tif — MC uncertainty (std), same grid
  • uncertainty/figure_uncertainty_3panel.png — Figure for §4.6 (300 dpi)
  • uncertainty/figure_uncertainty_3panel.pdf — vector for journal submission
• Answers: R3.9, R4.4

Parallelisation — built in

Both scripts shard themselves across every visible CUDA device with no extra launcher. Plain python script.py is enough:

• Experiment 1 orchestrates one worker subprocess per GPU and schedules the 5 folds across them (4 folds in parallel, then fold 4 on the first free GPU). Total wall time ≈ 2 × (single-fold time) on 4 GPUs.
• Experiment 2 shards the 1.3 M Bavaria inference grid into equal-sized contiguous slices, one per GPU, and concatenates the shards back into a single GeoTIFF/parquet at the end. Total wall time ≈ (single-GPU time) / N_GPUs.

Override with --gpus 0,1 (subset) or --sequential (debug). Per-GPU stdout lands in {spatial_kfold,uncertainty}/worker_logs/<id>_gpu_<g>.log.

If you really want to run both experiments concurrently on disjoint GPU subsets:

python rebuttal/gpu_experiments/spatial_kfold/run_kfold.py        --gpus 0,1 &
python rebuttal/gpu_experiments/uncertainty/mc_dropout_inference.py --gpus 2,3 &
wait

The k-fold checkpoints are saved per fold so a crash on fold N does not invalidate folds 0..N-1.

What to do with the outputs

• kfold_results.md → paste the table into manuscript §2.5 and cite the row in the responses to R1.3 and R3.6.
• figure_kfold.png → new Figure in the supplement (or main text if the editor agrees).
• figure_uncertainty_3panel.png → new Figure in manuscript §4.6.
• All four → referenced in the response letter with explicit page / line numbers.

Important assumptions flagged in the scripts

Every # ASSUMPTION: comment in the two scripts marks a place where the spec the user provided and the actual codebase disagree, or where a default needed to be chosen. Skim them before launching — they are the only places where the project lead might want to override the default.

The four notable ones:

1. Optimizer / lr. Spec said Adam @ 1e-3 + exponential decay. train.py uses Adam @ 2e-4 with no scheduler. Scripts follow train.py (the spec's overriding instruction was "match the original exactly").
2. Log transform. Spec said log1p / expm1. train.py uses torch.log(OC + 1e-10) / np.exp(pred). Scripts follow train.py.
3. Model class. Spec referred to "the SGT 1.1M". train.py imports SimpleSGT but the saved 1.1 M checkpoints are EnhancedSGT — running.py has from EnhancedSGT import EnhancedSGT as SimpleSGT. Scripts use EnhancedSGT (verified by state-dict shape and parameter count = 1,121,637).
4. Normalisation in MC dropout. The MC accumulator is in original SOC units (the log transform is inverted inside the MC loop). The spec said this explicitly; flagging here only to note it.

Sanity checks before running

# 1. confirm Model B exists at the documented path
ls -la /home/valerian/SGTPublication/Weights-ResidualsModels-MappingInference-SOCmapping/TemporalFusionTransformer/finalResults2023_1milVersion_TRANSFORM_log_LOSS_l1/

# 2. confirm model_ready_dataset.parquet exists
ls -la /home/valerian/SGTPublication/rebuttal/model_ready_dataset.parquet

# 3. confirm rasterio + pyproj are importable (for Experiment 2 GeoTIFFs)
python -c "import rasterio, pyproj; print(rasterio.__version__, pyproj.__version__)"

# 4. confirm the EnhancedSGT module imports and parameter count matches
python -c "import sys; sys.path.insert(0, '/home/valerian/SGTPublication/SOCmapping/SpatiotemporalGatedTransformer'); from EnhancedSGT import EnhancedSGT; m = EnhancedSGT(input_channels=6, height=5, width=5, time_steps=5, d_model=128); print(sum(p.numel() for p in m.parameters() if p.requires_grad))"
# expected: 1121637