Building Inferencesh Apps

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. BaseApp has no logger attribute: import logging and set self.logger = logging.getLogger(__name__) in setup() first. 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

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

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.

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

belt app init my-app

2. Implement

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

3. Test Locally

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

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:

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.

# 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

import logging

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.logger = logging.getLogger(__name__)
        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

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:

import logging
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.logger = logging.getLogger(__name__)
        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

name: my-app               # the slug that addresses the app: namespace/name
title: My App              # optional — how it reads on the app's page
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

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

Dependencies

Python — requirements.txt:

torch>=2.0
transformers
accelerate

Node.js — package.json:

{
  "type": "module",
  "dependencies": {
    "zod": "^3.23.0",
    "sharp": "^0.33.0"
  }
}

System packages — packages.txt (apt-installable):

ffmpeg
libgl1-mesa-glx

Base Images

GPU Apps

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

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:

# 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 — Python: Pydantic models, BaseApp, File handling, type hints, multi-function patterns
• references/node-app-logic.md — Node.js: Zod schemas, File handling, ESM, generators, multi-function patterns

Debugging, Optimization & Cancellation

• references/python-patterns.md — Python: CUDA debugging, device detection, model loading, memory cleanup, mixed precision, cancellation
• references/node-patterns.md — Node.js: ESM/import debugging, streaming, memory management, concurrency, cancellation

Secrets & OAuth

• references/python-secrets-oauth.md — Python: os.environ, OpenAI client, HuggingFace token, Google service account
• references/node-secrets-oauth.md — Node.js: process.env, OpenAI client, Google credentials JSON

Usage Tracking

• references/python-tracking.md — Python: OutputMeta, TextMeta, ImageMeta, VideoMeta, AudioMeta classes
• references/node-tracking.md — Node.js: textMeta, imageMeta, videoMeta, audioMeta factory functions

CLI

• references/cli.md — Full CLI command reference, prerequisites for both languages

Resources

• Full Docs: inference.sh/docs
• Examples: github.com/inference-sh/grid