/runpod

name: runpod
description: Cloud GPU processing via RunPod serverless. Use when setting up RunPod endpoints, deploying Docker images, managing GPU resources, troubleshooting endpoint issues, or understanding costs. Covers all 5 toolkit images (qwen-edit, realesrgan, propainter, sadtalker, qwen3-tts).

RunPod Cloud GPU

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

Setup

# 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
python tools/image_edit.py --setup
python tools/upscale.py --setup
python tools/dewatermark.py --setup
python tools/sadtalker.py --setup
python tools/qwen3_tts.py --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 | | sadtalker | `ghcr.io/conalmullan/video-toolkit-sadtalker:latest` | RTX 4090 | 24GB | ~$0.05-0.15/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` | | sadtalker | `RUNPOD_SADTALKER_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:**

query { myself { endpoints { id name gpuIds templateId workersMax workersMin } } }

**Current spend rate:**

query { myself { currentSpendPerHr spendDetails { localStoragePerHour networkStoragePerHour gpuComputePerHour } } }

**List pods:**

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:**

mutation { saveEndpoint(input: {
  id: "endpoint_id",
  name: "endpoint-name",
  templateId: "template_id",
  gpuIds: "AMPERE_24",
  workersMin: 0,
  workersMax: 1
}) { id gpuIds } }

`saveEndpoint` requires `name` and `templateId` even for updates — query first to get current values.

REST API (Serverless)

| Action | Method | URL | |--------|--------|-----| | Submit job | POST | `/v2/{id}/run` | | Check status | GET | `/v2/{id}/status/{job_id}` | | Cancel job | POST | `/v2/{id}/cancel/{job_id}` | | List pending | GET | `/v2/{id}/requests` | | Health/stats | GET | `/v2/{id}/health` |

**Health response** includes job counts and worker state:

{
  "jobs": { "completed": 16, "failed": 1, "inProgress": 0, "inQueue": 2, "retried": 0 },
  "workers": { "idle": 0, "initializing": 1, "ready": 0, "running": 0, "throttled": 0 }
}

> **Note:** `/requests` only returns pending/queued jobs. Completed job history is not available via the API — check the RunPod web console for logs.

GPU Type IDs

| ID | GPU | VRAM | Typical Cost | |----|-----|------|-------------| | `AMPERE_24` | RTX 3090 | 24GB | ~$0.34/hr | | `ADA_24` | RTX 4090 | 24GB | ~$0.69/hr | | `AMPERE_48` | A6000 | 48GB | ~$0.76/hr | | `AMPERE_80` | A100 | 80GB | ~$1.99/hr |

**Availability note:** `ADA_24` (4090) is frequently throttled/unavailable on RunPod. Always configure endpoints with **multiple fallback GPU types** (comma-separated) to avoid jobs getting stuck in queue indefinitely:

gpuIds: "AMPERE_24,ADA_24"   # Try 3090 first, fall back to 4090

All toolkit tools also enforce a 5-minute queue timeout — if no GPU is available within 300 seconds, the job is automatically can