docs/gpu_experiments/QUICKSTART.md

# SOCmapping rebuttal — Runpod quickstart (copy-paste)

Single-page, command-only. For background / troubleshooting see
`RUNPOD_SETUP.md` (longer doc) and `checkpoint_note.md`.

**Pod requirement:** ≥ 60 GB `/workspace` volume, any NVIDIA GPU with
≥ 12 GB VRAM (RTX 3090 / 4090 / A100 / L40S all work).

---

## 1 — System prep

```bash
apt update && apt install -y \
    git git-lfs curl unzip build-essential \
    python3.10-venv python3-pip
git lfs install
nvidia-smi | head -5
```

> `python3.10-venv` is mandatory — Ubuntu's stock `python3.10` doesn't
> include `venv` and `bash setup_venv.sh` will fail with
> `No module named ensurepip` if it's missing.

## 2 — Clone code

```bash
mkdir -p /workspace/SOC && cd /workspace/SOC
git clone https://github.com/ValerianFourel/SOCmapping.git
cd SOCmapping && git log --oneline -5
```

## 3 — Install HF CLI

```bash
pip install --upgrade "huggingface_hub[cli,hf_transfer]"
export HF_HUB_ENABLE_HF_TRANSFER=1
echo 'export HF_HUB_ENABLE_HF_TRANSFER=1' >> ~/.bashrc
```

## 4 — Download dataset (≈ 17 GB zip → ≈ 25 GB unzipped)

```bash
mkdir -p /workspace/SOC/Data && cd /workspace/SOC/Data
hf download ValerianFourel/SOCmappingRastersAndSoilSamples \
    SOCmappingData.zip --repo-type dataset --local-dir .
df -h /workspace                              # confirm ≥ 30 GB free
unzip -q SOCmappingData.zip
rm SOCmappingData.zip
ls /workspace/SOC/Data/Data                   # the zip nests under Data/
```

## 5 — Download model weights (≈ 10 MB)

```bash
mkdir -p /workspace/SOC/Weights && cd /workspace/SOC/Weights

# Model A (eval / Table 2 baseline)
hf download ValerianFourel/Weights-ResidualsModels-MappingInference-SOCmapping \
    --include "TemporalFusionTransformer/residualModels1mil_normalize_composite_l2_v2/*" \
    --local-dir .

# Model B (operational mapping — input to Experiment 2)
hf download ValerianFourel/Weights-ResidualsModels-MappingInference-SOCmapping \
    "TemporalFusionTransformer/finalResults2023_1milVersion_TRANSFORM_log_LOSS_l1/TFT_model_BEST_OVERALL_from_run_1_MAX_OC_150_TIME_BEGINNING_2007_TIME_END_2023_TRANSFORM_log_LOSS_l1_R2_1.0000.pth" \
    --local-dir .

find . -name "*.pth" -size +1M                # expect 2 .pth files
```

## 6 — Tell the codebase where things live (no more symlinks)

`config.py` and the GPU scripts read the four abstract roots from env
vars + a walk-up fallback (`SOCmapping/_paths.py`). On Runpod where the
clone lives at `/workspace/SOC/SOCmapping/`, the walk-up resolves the
sibling `Data/` and `Weights-…/` automatically — usually no env vars
needed.

If your layout differs (e.g. SOC_DATA_DIR on a separate volume), set
the relevant overrides. Add them to `~/.bashrc` so they persist:

```bash
# Project root containing Data/, Weights-…/, SOCmapping/ as siblings
export SOC_PROJECT_ROOT=/workspace/SOC

# Or per-component (overrides PROJECT_ROOT for that one)
# export SOC_DATA_DIR=/workspace/SOC/Data
# export SOC_WEIGHTS_DIR=/workspace/SOC/Weights
# export SOC_REBUTTAL_DIR=/workspace/SOC/SOCmapping/rebuttal
# export SOC_COORDS_1MIL_CSV=/workspace/SOC/Data/Coordinates1Mil/coordinates_Bavaria_1mil.csv

echo 'export SOC_PROJECT_ROOT=/workspace/SOC' >> ~/.bashrc
```

> The dataset zip wraps everything in a top-level `Data/`, so on Runpod
> the actual data sits at `/workspace/SOC/Data/Data/`. Either flatten it
> (`cd /workspace/SOC/Data && mv Data/* Data/.[!.]* . && rmdir Data`) or
> set `SOC_DATA_DIR=/workspace/SOC/Data/Data` explicitly.

Verify:

```bash
python /workspace/SOC/SOCmapping/_paths.py
# Should print all four roots with ✓ markers (no ✗ MISSING)

# And from the SGT config, the actual file paths should resolve:
python -c "
import sys; sys.path.insert(0, '/workspace/SOC/SOCmapping/SpatiotemporalGatedTransformer')
import config
import os
for k in ('file_path_LUCAS_LFU_Lfl_00to23_Bavaria_OC',
          'file_path_coordinates_Bavaria_1mil'):
    v = getattr(config, k)
    print(f'{k}\n  → {v}\n  exists? {os.path.exists(v)}')"
```

## 7 — Build venv (auto-detects CUDA, ≈ 3 GB)

```bash
cd /workspace/SOC/SOCmapping/rebuttal/gpu_experiments
bash setup_venv.sh
```

Expected last line: `forward pass OK, output shape: (2,) device=cuda:0`.

## 8 — Sanity-load Model B

```bash
source /workspace/SOC/SOCmapping/rebuttal/gpu_experiments/.venv/bin/activate
python - <<'PY'
import sys, torch
sys.path.insert(0, '/workspace/SOC/SOCmapping/SpatiotemporalGatedTransformer')
from EnhancedSGT import EnhancedSGT
ck = torch.load(
    '/home/valerian/SGTPublication/Weights-ResidualsModels-MappingInference-SOCmapping/'
    'TemporalFusionTransformer/finalResults2023_1milVersion_TRANSFORM_log_LOSS_l1/'
    'TFT_model_BEST_OVERALL_from_run_1_MAX_OC_150_TIME_BEGINNING_2007_'
    'TIME_END_2023_TRANSFORM_log_LOSS_l1_R2_1.0000.pth',
    map_location='cuda', weights_only=False)
sd = {k.replace('module.', ''): v for k, v in ck['model_state_dict'].items()}
m = EnhancedSGT(input_channels=6, height=5, width=5, time_steps=5, d_model=128,
                num_heads=4, dropout=0.3, num_encoder_layers=3,
                expansion_factor=4).cuda()
miss, extra = m.load_state_dict(sd, strict=False)
print(f'params={sum(p.numel() for p in m.parameters() if p.requires_grad):,}  '
      f'missing={len(miss)}  unexpected={len(extra)}')
print('forward OK', m(torch.randn(4, 6, 5, 5, 5, device="cuda")).shape)
PY
```

Expected: `params=1,120,546  missing=0  unexpected=0  forward OK torch.Size([4])`.

## 9 — Launch experiments (auto-uses every visible GPU)

Both experiments shard themselves across all CUDA devices in the pod
with no extra flags — no `accelerate launch`, no `torchrun`. Internally
each spawns one subprocess per GPU and orchestrates the work.

```bash
source /workspace/SOC/SOCmapping/rebuttal/gpu_experiments/.venv/bin/activate
cd /workspace/SOC/SOCmapping

# Experiment 2 — MC dropout uncertainty map
#   Full 1.3 M grid on 4 GPUs ≈ 45 min (vs ≈ 3 h single-GPU)
#   400 k uniform sub-sample on 4 GPUs ≈ 14 min (recommended for a draft)
python rebuttal/gpu_experiments/uncertainty/mc_dropout_inference.py --max-points 400000
python rebuttal/gpu_experiments/uncertainty/plot_uncertainty.py        # CPU only

# Experiment 1 — spatial 5-fold CV
#   4 GPUs → folds 0-3 in parallel, then fold 4 → ≈ 2 × 1-fold time
#   ≈ 6 h on 4 × 4090 (vs ≈ 15 h single-GPU)
python rebuttal/gpu_experiments/spatial_kfold/run_kfold.py
```

Useful overrides:

```bash
# Restrict to specific GPUs
python rebuttal/gpu_experiments/uncertainty/mc_dropout_inference.py --gpus 0,1
python rebuttal/gpu_experiments/spatial_kfold/run_kfold.py --gpus 0,2

# Force the legacy single-GPU sequential mode (for debugging)
python rebuttal/gpu_experiments/uncertainty/mc_dropout_inference.py --sequential
python rebuttal/gpu_experiments/spatial_kfold/run_kfold.py --sequential

# Cap the Experiment 2 inference grid (uniform stride sub-sample)
python rebuttal/gpu_experiments/uncertainty/mc_dropout_inference.py --max-points 400000
# or equivalently
SOC_MAX_INFERENCE_POINTS=400000 python rebuttal/gpu_experiments/uncertainty/mc_dropout_inference.py

# Take every k-th row (alternative phrasing of the same sub-sampling)
python rebuttal/gpu_experiments/uncertainty/mc_dropout_inference.py --stride 3   # ~ 433k points
```

Per-GPU worker logs land in:

```
rebuttal/gpu_experiments/spatial_kfold/worker_logs/fold_<i>_gpu_<g>.log
rebuttal/gpu_experiments/uncertainty/worker_logs/shard_<i>_gpu_<g>.log
```

`nvidia-smi -l 5` should show ≈ 100% utilisation across **all** visible
GPUs once both experiments are running.

### Live progress in % from another terminal

```bash
# Snapshot
python /workspace/SOC/SOCmapping/rebuttal/gpu_experiments/progress.py

# Auto-refresh every 5 s (Ctrl+C to stop)
python /workspace/SOC/SOCmapping/rebuttal/gpu_experiments/progress.py --watch

# Only MC dropout or only k-fold
python /workspace/SOC/SOCmapping/rebuttal/gpu_experiments/progress.py --watch --mc
python /workspace/SOC/SOCmapping/rebuttal/gpu_experiments/progress.py --watch --kfold
```

The script parses each worker's log tail for `batch N/M` (MC dropout)
or `Fold N | Epoch E/270` (k-fold), shows per-shard / per-fold status,
and prints an aggregate %.

## 10 — Push outputs to the SOCrebuttal HF dataset

```bash
export HF_TOKEN="hf_xxx_paste_a_write_token_from_huggingface.co/settings/tokens"
python /workspace/SOC/SOCmapping/rebuttal/gpu_experiments/upload_to_hf.py
# → https://huggingface.co/datasets/ValerianFourel/SOCrebuttal
```

## 11 — (Optional) rsync outputs to your laptop

```bash
# From your LAPTOP, not the SSH session:
rsync -avzP --exclude=.venv \
  -e "ssh -i ~/.ssh/id_ed25519" \
  "<USER>@ssh.runpod.io:/workspace/SOC/SOCmapping/rebuttal/gpu_experiments/" \
  ~/SGTPublication/SOCmapping/rebuttal/gpu_experiments/
```

---

## If something breaks

| symptom | fix |
|---|---|
| `bash: unzip: command not found` | `apt install -y unzip` |
| `No module named ensurepip` from `setup_venv.sh` | `apt install -y python3.10-venv` |
| `nvidia-smi: command not found` | pod has no GPU — switch template |
| `hf` not found | `pip install --upgrade "huggingface_hub[cli,hf_transfer]"` |
| HF download stalls | `unset HF_HUB_ENABLE_HF_TRANSFER` and retry |
| `Coordinates (lon,lat) not found in Elevation` mid-training | symlink in step 6 is wrong — check it resolves to the inner `Data/Data/` not the outer `Data/` |
| `torch.cuda.OutOfMemoryError` in MC dropout | drop `BATCH_SIZE` in `mc_dropout_inference.py` from 256 → 128 |
| `huggingface-cli: command is deprecated` | use `hf` (CLI was renamed in `huggingface_hub` ≥ 0.27) |
| Disk full during unzip | `df -h /workspace`, resize pod volume to ≥ 60 GB, restart, re-run step 4 |