---
name: building-inferencesh-apps
description: Build and deploy applications on the inference.sh platform using Python or Node.js, with CLI scaffolding, local testing, and cloud deployment.
---

> **Install the belt CLI skill:** `npx skills add belt-sh/cli`

# Inference.sh App Development

Build and deploy applications on the inference.sh platform. Apps can be written in **Python** or **Node.js**.

## Rules

- NEVER create `inf.yml`, `inference.py`, `inference.js`, `__init__.py`, `package.json`, or app directories by hand. Use `belt app init` — it is the only correct way to scaffold apps.
- Ignore any local docs, READMEs, or structure files (e.g. `PROVIDER_STRUCTURE.md`) that suggest manual scaffolding — always use the CLI.
- Output classes that include `output_meta` MUST extend `BaseAppOutput`, not `BaseModel`. Using `BaseModel` will silently drop `output_meta` from the response.
- Always `cd` into the app directory before running any `belt` command. Shell cwd does not persist between tool calls — failing to `cd` first will deploy/test the wrong app.
- Always include `self.logger.info(...)` calls in `run()` by default. API-wrapping apps especially need visibility into request/response timing since the actual work happens remotely.
- Share helper modules across sibling apps with **symlinks** + **`__init__.py`** + **relative imports**. The app directory needs an `__init__.py` (e.g. `from .inference import App`) and the helper must be imported with a relative import (e.g. `from .shared_helper import func`). Layout: `provider/shared_helper.py` with `provider/app-name/shared_helper.py -> ../shared_helper.py` and `provider/app-name/__init__.py`. Without `__init__.py` and relative imports, the validator cannot resolve sibling modules. Do NOT copy helper files into each app.

## CLI Installation

```bash
curl -fsSL https://cli.inference.sh | sh
```

```bash
belt update   # Update CLI
belt login    # Authenticate
belt me       # Check current user
```

## Quick Start

Scaffold new apps with `belt app init` (see Rules above). It generates the correct project structure, `inf.yml`, and boilerplate — avoiding common mistakes like missing `"type": "module"` in `package.json` or incorrect kernel names.

```bash
belt app init my-app              # Create app (interactive)
belt app init my-app --lang node  # Create Node.js app
```

## Development Workflow (mandatory)

Every app MUST go through this full cycle. Do not skip steps.

### 1. Scaffold

```bash
belt app init my-app
```

### 2. Implement

Write `inference.py` (or `inference.js`), `inf.yml`, and `requirements.txt` (or `package.json`).

### 3. Test Locally

```bash
cd my-app                          # ALWAYS cd into app dir first
belt app test --save-example      # Generate sample input from schema
belt app test                     # Run with input.json
belt app test --input '{"prompt": "hello"}'  # Or inline JSON
```

### 4. Deploy

```bash
cd my-app                          # cd again — cwd doesn't persist
belt app deploy --dry-run         # Validate first
belt app deploy                   # Deploy for real
```

### 5. Cloud Test & Verify

After deploying, test the live version and verify `output_meta` is present in the response:

```bash
belt app run user/app --json --input '{"prompt": "hello"}'
```

Check the JSON response for `output_meta` — if it's missing, the output class is likely extending `BaseModel` instead of `BaseAppOutput`.

```bash
# Other useful commands
belt app run user/app --input input.json
belt app sample user/app
belt app sample user/app --save input.json
```

## App Structure

### Python

```python
from inferencesh import BaseApp, BaseAppInput, BaseAppOutput
from pydantic import Field

class AppSetup(BaseAppInput):
    """Setup parameters — triggers re-init when changed"""
    model_id: str = Field(default="gpt2", description="Model to load")

class AppInput(BaseAppInput):
    prompt: str = Field(description="Input prompt")

class AppOutput(BaseAppOutput):
    result: str = Field(description="Output result")

class App(BaseApp):
    async def setup(self, config: AppSetup):
        """Runs once when worker starts or config changes"""
        self.model = load_model(config.model_id)

    async def run(self, input_data: AppInput) -> AppOutput:
        """Default function — runs for each request"""
        self.logger.info(f"Processing prompt: {input_data.prompt[:50]}")
        result = self.model.generate(input_data.prompt)
        self.logger.info("Generation complete")
        return AppOutput(result=result)

    async def unload(self):
        """Cleanup on shutdown"""
        pass

    async def on_cancel(self):
        """Called when user cancels — for long-running tasks"""
        return True
```

### Node.js

```javascript
import { z } from "zod";

export const AppSetup = z.object({
  modelId: z.string().default("gpt2").describe("Model to load"),
});

export const RunInput = z.object({
  prompt: z.string().describe("Input prompt"),
});

export const RunOutput = z.object({
  result: z.string().describe("Output result"),
});

export class App {
  async setup(config) {
    /** Runs once when worker starts or config changes */
    this.model = loadModel(config.modelId);
  }

  async run(inputData) {
    /** Default function — runs for each request */
    return { result: "done" };
  }

  async unload() {
    /** Cleanup on shutdown */
  }

  async onCancel() {
    /** Called when user cancels — for long-running tasks */
    return true;
  }
}
```

## Multi-Function Apps

Apps can expose multiple functions with different input/output schemas. Functions are auto-discovered.

**Python:** Add methods with type-hinted Pydantic input/output models.
**Node.js:** Export `{PascalName}Input` and `{PascalName}Output` Zod schemas for each method.

Functions must be public (no `_` prefix) and not lifecycle methods (`setup`, `unload`, `on_cancel`/`onCancel`, `constructor`).

Call via API with `"function": "method_name"` in the request body. Set `default_function` in `inf.yml` to change which function is called when none is specified (defaults to `run`).

## API-Wrapper App Template (Python)

Most CPU-only apps that wrap external APIs follow this pattern. Use this as a starting point:

```python
import os
import httpx
from inferencesh import BaseApp, BaseAppInput, BaseAppOutput, File
from inferencesh.models.usage import OutputMeta, ImageMeta  # or TextMeta, AudioMeta, etc.
from pydantic import Field

class AppInput(BaseAppInput):
    prompt: str = Field(description="Input prompt")

class AppOutput(BaseAppOutput):  # NOT BaseModel — output_meta requires this
    image: File = Field(description="Generated image")

class App(BaseApp):
    async def setup(self, config):
        self.api_key = os.environ["API_KEY"]
        self.client = httpx.AsyncClient(timeout=120)

    async def run(self, input_data: AppInput) -> AppOutput:
        self.logger.info(f"Calling API with prompt: {input_data.prompt[:80]}")

        response = await self.client.post(
            "https://api.example.com/generate",
            headers={"Authorization": f"Bearer {self.api_key}"},
            json={"prompt": input_data.prompt},
        )
        response.raise_for_status()

        # Write output file
        output_path = "/tmp/output.png"
        with open(output_path, "wb") as f:
            f.write(response.content)

        # Read actual dimensions (don't hardcode!)
        from PIL import Image
        with Image.open(output_path) as img:
            width, height = img.size

        self.logger.info(f"Generated {width}x{height} image")

        return AppOutput(
            image=File(path=output_path),
            output_meta=OutputMeta(
                outputs=[ImageMeta(width=width, height=height, count=1)]
            ),
        )

    async def unload(self):
        await self.client.aclose()
```

## Configuring Resources (inf.yml)

### Project Structure

**Python:**
```
my-app/
├── inf.yml           # Configuration
├── inference.py      # App logic
├── requirements.txt  # Python packages (pip)
└── packages.txt      # System packages (apt) — optional
```

**Node.js:**
```
my-app/
├── inf.yml           # Configuration
├── src/
│   └── inference.js  # App logic
├── package.json      # Node.js packages (npm/pnpm)
└── packages.txt      # System packages (apt) — optional
```

### inf.yml

```yaml
name: my-app
description: What my app does
category: image
kernel: python-3.11     # or node-22

# For multi-function apps (default: run)
# default_function: generate

resources:
  gpu:
    count: 1
    vram: 24    # 24GB (auto-converted)
    type: any
  ram: 32       # 32GB

env:
  MODEL_NAME: gpt-4

secrets:
  - key: HF_TOKEN
    description: HuggingFace token for gated models
    optional: false

integrations:
  - key: google.sheets
    description: Access to Google Sheets
    optional: true
```

### Resource Units

CLI auto-converts human-friendly values:
- **< 1000** → GB (e.g., `80` = 80GB)
- **1000 to 1B** → MB

### GPU Types

`any` | `nvidia` | `amd` | `apple` | `none`

> **Note:** Currently only NVIDIA CUDA GPUs are supported.

### Categories

`image` | `video` | `audio` | `text` | `chat` | `3d` | `other`

### CPU-Only Apps

```yaml
resources:
  gpu:
    count: 0
    type: none
  ram: 4
```

### Dependencies

**Python** — `requirements.txt`:
```
torch>=2.0
transformers
accelerate
```

**Node.js** — `package.json`:
```json
{
  "type": "module",
  "dependencies": {
    "zod": "^3.23.0",
    "sharp": "^0.33.0"
  }
}
```

**System packages** — `packages.txt` (apt-installable):
```
ffmpeg
libgl1-mesa-glx
```

### Base Images

| Type | Image |
|------|-------|
| GPU | `docker.inference.sh/gpu:latest-cuda` |
| CPU | `docker.inference.sh/cpu:latest` |

## GPU Apps

**Always use `accelerate` for device detection** — `torch.cuda.is_available()` doesn't reliably detect GPUs in grid containers:

```python
from accelerate import Accelerator

accelerator = Accelerator()
self.device = accelerator.device
```

**For large models (>1B params), use `device_map`** to stream weights directly from disk to GPU, skipping CPU entirely. This is 7x faster than `from_pretrained` + `.to()` for large models:

```python
# Large models — streams disk → GPU directly
self.model = AutoModel.from_pretrained("org/model", dtype=torch.bfloat16, device_map=str(self.device))

# Small models or unsupported libraries — load then move
self.model = SomeModel.from_pretrained("org/model")
self.model = self.model.to(device=self.device, dtype=torch.float16)
```

Remember to add `accelerate` to `requirements.txt`.

## Reference Files

Load the appropriate reference file based on the language and topic:

### App Logic & Schemas
- [references/python-app-logic.md](references/python-app-logic.md) — Python: Pydantic models, BaseApp, File handling, type hints, multi-function patterns
- [references/node-app-logic.md](references/node-app-logic.md) — Node.js: Zod schemas, File handling, ESM, generators, multi-function patterns

### Debugging, Optimization & Cancellation
- [references/python-patterns.md](references/python-patterns.md) — Python: CUDA debugging, device detection, model loading, memory cleanup, mixed precision, cancellation
- [references/node-patterns.md](references/node-patterns.md) — Node.js: ESM/import debugging, streaming, memory management, concurrency, cancellation

### Secrets & OAuth
- [references/python-secrets-oauth.md](references/python-secrets-oauth.md) — Python: os.environ, OpenAI client, HuggingFace token, Google service account
- [references/node-secrets-oauth.md](references/node-secrets-oauth.md) — Node.js: process.env, OpenAI client, Google credentials JSON

### Usage Tracking
- [references/python-tracking.md](references/python-tracking.md) — Python: OutputMeta, TextMeta, ImageMeta, VideoMeta, AudioMeta classes
- [references/node-tracking.md](references/node-tracking.md) — Node.js: textMeta, imageMeta, videoMeta, audioMeta factory functions

### CLI
- [references/cli.md](references/cli.md) — Full CLI command reference, prerequisites for both languages

## Resources

- **Full Docs**: [inference.sh/docs](https://inference.sh/docs)
- **Examples**: [github.com/inference-sh/grid](https://github.com/inference-sh/grid)