| # AtmosIQ — GPU Training Setup (run when you rent the Vast.ai machine) | |
| Everything (tensors + code) is on HuggingFace: AtmosIQ/atmosiq-processed. | |
| The Azure CPU VM is gone; this rebuilds nothing on GPU — just download + train. | |
| ## 0. Pick the machine (budget: ~$3.49) | |
| - VRAM: 16 GB comfortable (12 GB works with --window 5). 24 GB (RTX 3090/4090) ideal. | |
| - Disk: ~40 GB (tensors ~9 GB + torch env ~6 GB + checkpoints). | |
| - Cheap picks: RTX 3090 24GB (~$0.20-0.30/hr), RTX 4090 (~$0.30-0.40/hr), | |
| RTX 3060 12GB (~$0.10-0.15/hr), T4 16GB. Any of these finishes in budget. | |
| - Full training (~30 min) costs well under $1 on a 3090/4090. | |
| ## 1. Setup | |
| pip install torch numpy pandas huggingface_hub | |
| hf download AtmosIQ/atmosiq-processed --repo-type dataset --local-dir atmosiq --include "tensors/*" "scripts/model.py" "scripts/dataset.py" "scripts/train.py" | |
| # (or download the whole repo; tensors/ is the ~9GB part you need) | |
| ## 2. Smoke test (confirm GPU + loss drops, ~1 min) | |
| python scripts/train.py --tensors atmosiq/tensors --ssl_epochs 1 --epochs 2 --max_days 100 --log_every 20 | |
| # expect: "device: cuda", s/day ~0.01-0.05, val_nll decreasing | |
| ## 3. Full training (~30-60 min) | |
| python scripts/train.py --tensors atmosiq/tensors --ssl_epochs 10 --epochs 30 | |
| # -> atmosiq_best.pt (best val_nll checkpoint) | |
| ## 4. Save the trained model back | |
| hf upload AtmosIQ/atmosiq-processed atmosiq_best.pt models/atmosiq_best.pt --repo-type dataset | |
| ## Memory knobs if VRAM is tight | |
| - --window 5 (or 3) # shorter temporal window = less activation memory | |
| - tensors stay in CPU RAM; only per-day windows go to GPU (already how dataset.py works) | |
| - if still OOM: reduce hidden in model.py AtmosIQNet(hidden=64) | |
| ## What the run does | |
| SSL masked-reconstruction pretrain (all nodes) -> supervised Student-t fine-tune | |
| on TRAIN-fold labeled cells (spatial split, leakage-safe). Beats the M1 baseline | |
| floor (PM ~0.30 / gases <0.10) via spatial message-passing + physics + temporal. |