runpod

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• SKILL.md

SKILL.md (first lines)

# RunPod Cloud GPU

Run open-source AI models on cloud GPUs via RunPod serverless. Pay-per-second, no minimums.

## Setup

```bash
# 1. Create account at https://runpod.io
# 2. Add API key to .env
echo "RUNPOD_API_KEY=your_key_here" >> .env

# 3. Deploy any tool with --setup
python3 -m video_toolkit.image_edit --setup
python3 -m video_toolkit.upscale --setup
python3 -m video_toolkit.dewatermark --setup
python3 -m video_toolkit.qwen3_tts --setup
```

Each `--setup` command:
1. Creates a RunPod **template** from the Docker image
2. Creates a serverless **endpoint** with appropriate GPU
3. Saves the endpoint ID to `.env` (e.g. `RUNPOD_QWEN_EDIT_ENDPOINT_ID`)

## Available Images

All images are public on GHCR — no authentication needed.

| Tool | Docker Image | GPU | VRAM | Typical Cost |
|------|-------------|-----|------|-------------|
| image_edit | `ghcr.io/conalmullan/video-toolkit-qwen-edit:latest` | A6000/L40S | 48GB+ | ~$0.05-0.15/job |
| upscale | `ghcr.io/conalmullan/video-toolkit-realesrgan:latest` | RTX 3090/4090 | 24GB | ~$0.01-0.05/job |
| dewatermark | `ghcr.io/conalmullan/video-toolkit-propainter:latest` | RTX 3090/4090 | 24GB | ~$0.05-0.30/job |
| qwen3_tts | `ghcr.io/conalmullan/video-toolkit-qwen3-tts:latest` | ADA 24GB | 24GB | ~$0.01-0.05/job |

**Total monthly cost:** Rarely exceeds $10 even with heavy use.

## How It Works

All tools follow the same pattern:

```
Local CLI → Upload input to cloud storage → RunPod API → Poll for result → Download output
```

1. **File transfer:** Tools use Cloudflare R2 when configured (`R2_ACCOUNT_ID`, `R2_ACCESS_KEY_ID`, `R2_SECRET_ACCESS_KEY`, `R2_BUCKET_NAME`), falling back to free upload services
2. **RunPod API:** Tools call the `/run` endpoint, then poll `/status/{job_id}` until complete
3. **Cold vs warm start:** First request after idle spins up a worker (~30-90s). Subsequent requests are fast (~5-15s)

## Endpoint Management

### Workers

```
workersMin: 0    — Scale to zero when idle (no cost)
workersMax: 1    — Max concurrent jobs (increase for throughput)
idleTimeout: 5   — Seconds before worker scales down
```

Across all endpoints, you share a total worker pool based on your RunPod plan. If you hit limits, reduce `workersMax` on endpoints you're not actively using.

### Checking Endpoint Status

Each tool stores its endpoint ID in `.env`:

| Tool | Env Var |
|------|---------|
| image_edit | `RUNPOD_QWEN_EDIT_ENDPOINT_ID` |
| upscale | `RUNPOD_UPSCALE_ENDPOINT_ID` |
| dewatermark | `RUNPOD_DEWATERMARK_ENDPOINT_ID` |
| qwen3_tts | `RUNPOD_QWEN3_TTS_ENDPOINT_ID` |

### Disabling an Endpoint

To free worker slots without deleting the endpoint, set `workersMax=0` via the RunPod dashboard or GraphQL API.

## RunPod API Reference

Use these to query and manage endpoints programmatically. RunPod disables GraphQL introspection, so these field names are verified and must be exact.

### Authentication

All API calls require `Authorization: Bearer $RUNPOD_API_KEY`.

- **GraphQL:** `POST https://api.runpod.io/graphql`
- **REST (Serverless):** `https://api.runpod.ai/v2/{endpoint_id}/...`

### GraphQL Queries

**List all endpoints:**
```graphql
query { myself { endpoints { id name gpuIds templateId workersMax workersMin } } }
```

**Current spend rate:**
```graphql
query { myself { currentSpendPerHr spendDetails { localStoragePerHour networkStoragePerHour gpuComputePerHour } } }
```

**List pods:**
```graphql
query { myself { pods { id name runtime { uptimeInSeconds } machine { gpuDisplayName } desiredStatus } } }
```

> **Common mistakes:** Field names are camelCase with full words — `localStoragePerHour` not `localStoragePerHr`. Endpoints are `endpoints` not `serverlessWorkers`. `spending` is not a field — use `currentSpendPerHr` and `spendDetails`.

### GraphQL Mutations

**Update endpoint GPU or config:**
```graphql
mutation { saveEndpoint(input: {
  id: "endpoint_id",
  name: "endpoint-name",
  templateId: "template_id",
  gpuIds: "AMPERE_24",
  workersMin: 0,
  workersMax: 1
}) { id gpuIds } }
```

From xaralis/video-toolkit, MIT.

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