funsloth-runpod

# RunPod Training Manager

Run Unsloth training on RunPod GPU instances.

## Prerequisites

1. **RunPod API Key**: `echo $RUNPOD_API_KEY` (get at runpod.io/console/user/settings)
2. **RunPod SDK**: `pip install runpod`
3. **Training notebook/script**: From `funsloth-train`

## Workflow

### 1. Select GPU

| GPU | VRAM | Cost | Best For |
|-----|------|------|----------|
| RTX 3090 | 24GB | ~$0.35/hr | Budget 7-14B |
| RTX 4090 | 24GB | ~$0.55/hr | Fast 7-14B |
| A100 40GB | 40GB | ~$1.50/hr | 14-34B |
| A100 80GB | 80GB | ~$2.00/hr | 70B |
| H100 | 80GB | ~$3.50/hr | Fastest |

RunPod typically has better prices than HF Jobs.

### 2. Choose Deployment

- **Pod** (Recommended): Persistent, SSH access, network storage
- **Serverless**: Pay per second, complex setup (better for inference)

### 3. Configure Network Volume (Recommended)

```python
import runpod
volume = runpod.create_network_volume(name="funsloth-training", size_gb=50, region="US")
```

Allows: resume training, download checkpoints, share between pods.

### 4. Launch Pod

Use the [official Unsloth Docker image](https://docs.unsloth.ai/new/how-to-fine-tune-llms-with-unsloth-and-docker) for a pre-configured environment:

```python
import runpod

pod = runpod.create_pod(
    name="funsloth-training",
    image_name="unsloth/unsloth",  # Official image, supports all GPUs incl. Blackwell
    gpu_type_id="{gpu_type}",
    volume_in_gb=50,
    network_volume_id="{volume_id}",
    env={
        "HF_TOKEN": "{token}",
        "WANDB_API_KEY": "{key}",
        "JUPYTER_PASSWORD": "unsloth",
    },
    ports="8888/http,22/tcp",
)
```

The Unsloth image includes Jupyter Lab (port 8888) and example notebooks in `/workspace/unsloth-notebooks/`.

### 5. Upload and Run

```bash
# SSH into pod
ssh root@{pod_ip}

# Upload script
scp train.py root@{pod_ip}:/workspace/

# Run training (use tmux for persistence)
tmux new -s training
cd /workspace && python train.py
# Ctrl+B, D to detach
```

### 6. Monitor

```bash
# SSH mon