# SmolLM3-3B LoRA Training — Deployment Runbook

## Prerequisites

- [ ] GPU server deployed and accessible via SSH
- [ ] SSH creds from Mike (host, user, key/password)
- [ ] Docker + Docker Compose + NVIDIA Container Toolkit installed on GPU server
- [ ] This repo at `/home/openclaw/dev/smollora`

## Step 1: SSH In & Prep the Host

```bash
ssh <user>@<host>
```

Verify GPU is visible:
```bash
nvidia-smi
docker run --rm --gpus all nvidia/cuda:12.4.0-base-ubuntu22.04 nvidia-smi
```

If Docker/nvidia toolkit missing, install before continuing.

## Step 2: Create Persistent Directories

```bash
sudo mkdir -p /srv/smollora/{data,output,hf-cache}
sudo chown -R $(whoami):$(whoami) /srv/smollora
```

## Step 3: Copy Project Files to Server

From the OpenClaw box:
```bash
scp -r /home/openclaw/dev/smollora <user>@<host>:/tmp/smollora
```

On the GPU server:
```bash
cp -r /tmp/smollora ~/smollora
cd ~/smollora
```

## Step 4: Build & Start Container

```bash
cd ~/smollora
docker compose build
docker compose up -d
```

Verify it's running:
```bash
docker compose ps
docker compose logs --tail=20
```

## Step 5: Exec In & Kick Off Training

```bash
docker compose exec smollora bash
```

Inside the container, run the pipeline:
```bash
/app/run.sh
```

Watch for:
- ✅ Datasets downloading successfully
- ✅ Samples counted (should be thousands)
- ✅ Model loading without OOM
- ✅ First few training steps completing (check loss is decreasing)
- ✅ No CUDA OOM errors in first 50 steps

If data prep already ran and you just want to re-train:
```bash
SKIP_PREP=1 /app/run.sh
```

## Step 6: Monitor

From the host (no need to stay in the container):
```bash
# Follow logs
docker compose logs -f

# GPU utilization
watch -n5 nvidia-smi
```

Expected timeline on a single A100:
- Data prep: ~10-20 min
- Training (3 epochs, ~20-40k samples): ~2-4 hours
- Could be longer on smaller GPUs

## Step 7: Verify Output

```bash
# Check the LoRA adapter was saved
ls -la /srv/smollora/output/final/
# Should see: adapter_config.json, adapter_model.safetensors, tokenizer files
```

## Step 8: Notify Mike

Message Mike:
> 🎭 LoRA training is running on the GPU box. Data prep done, [N] samples, training started at [time]. Estimated completion: [est]. I'll check back periodically — will ping you if anything blows up or when it finishes.

## Troubleshooting

| Problem | Fix |
|---------|-----|
| CUDA OOM | Reduce `BATCH_SIZE` to 2, increase `GRAD_ACCUM` to 8, or reduce `MAX_LENGTH` to 2048 |
| Dataset download fails | Check internet; can pre-download and mount into `/data` |
| Docker can't see GPU | Install nvidia-container-toolkit: `sudo apt-get install -y nvidia-container-toolkit && sudo systemctl restart docker` |
| Training loss not decreasing | Check LR — try `1e-4` or `5e-5`; verify labels aren't all -100 |
| Disk full | Clean up `/srv/smollora/hf-cache` after model loads; processed data is small |

## Rollback

If everything goes sideways:
```bash
docker compose down
rm -rf /srv/smollora/output/*
# Fix whatever broke, then:
docker compose up -d
```