Inference.sh应用开发指南：Python/Node.js构建与部署AI应用

building-inferencesh-apps by inferen-sh/skills

466 周安装量

310 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/inferen-sh/skills --skill building-inferencesh-apps

AI/机器学习云服务开发运维

🇨🇳中文介绍

Inference.sh 应用开发

在 inference.sh 平台上构建和部署应用程序。应用可以使用 Python 或 Node.js 编写。

规则

切勿手动创建 inf.yml、inference.py、inference.js、__init__.py、package.json 或应用目录。请使用 infsh app init —— 这是搭建应用结构的唯一正确方式。
忽略任何建议手动搭建的本地文档、README 或结构文件（例如 PROVIDER_STRUCTURE.md）—— 始终使用 CLI。
包含 output_meta 的输出类必须继承，而不是。使用将导致在响应中被静默丢弃。

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开发工作流（强制）

每个应用必须经过这个完整的周期。不要跳过步骤。

infsh app init my-app

编写 inference.py（或 inference.js）、inf.yml 和 requirements.txt（或 package.json）。

cd my-app                          # 始终先 cd 进入应用目录
infsh app test --save-example      # 从模式生成示例输入
infsh app test                     # 使用 input.json 运行
infsh app test --input '{"prompt": "hello"}'  # 或内联 JSON

cd my-app                          # 再次 cd —— 当前工作目录不会保持
infsh app deploy --dry-run         # 先验证
infsh app deploy                   # 实际部署

5. 云端测试与验证

部署后，测试线上版本并验证响应中是否存在 output_meta：

infsh app run user/app --json --input '{"prompt": "hello"}'

检查 JSON 响应中是否有 output_meta —— 如果缺失，输出类很可能继承了 BaseModel 而不是 BaseAppOutput。

# 其他有用的命令
infsh app run user/app --input input.json
infsh app sample user/app
infsh app sample user/app --save input.json

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

class AppSetup(BaseAppInput):
    """设置参数 —— 更改时触发重新初始化"""
    model_id: str = Field(default="gpt2", description="要加载的模型")

class AppInput(BaseAppInput):
    prompt: str = Field(description="输入提示")

class AppOutput(BaseAppOutput):
    result: str = Field(description="输出结果")

class App(BaseApp):
    async def setup(self, config: AppSetup):
        """工作进程启动或配置更改时运行一次"""
        self.model = load_model(config.model_id)

    async def run(self, input_data: AppInput) -> AppOutput:
        """默认函数 —— 为每个请求运行"""
        self.logger.info(f"处理提示: {input_data.prompt[:50]}")
        result = self.model.generate(input_data.prompt)
        self.logger.info("生成完成")
        return AppOutput(result=result)

    async def unload(self):
        """关闭时清理"""
        pass

    async def on_cancel(self):
        """用户取消时调用 —— 用于长时间运行的任务"""
        return True

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;
  }
}

应用可以暴露具有不同输入/输出模式的多个函数。函数会被自动发现。

Python： 添加带有类型提示的 Pydantic 输入/输出模型的方法。Node.js： 为每个方法导出 {PascalName}Input 和 {PascalName}Output Zod 模式。

函数必须是公共的（没有 _ 前缀）并且不是生命周期方法（setup、unload、on_cancel/onCancel、constructor）。

通过 API 调用时，在请求体中使用 "function": "method_name"。在 inf.yml 中设置 default_function 来更改未指定时调用的函数（默认为 run）。

API 包装应用模板（Python）

大多数仅使用 CPU 并包装外部 API 的应用都遵循此模式。以此作为起点：

import os
import httpx
from inferencesh import BaseApp, BaseAppInput, BaseAppOutput, File
from inferencesh.models.usage import OutputMeta, ImageMeta  # 或 TextMeta, AudioMeta 等
from pydantic import Field

class AppInput(BaseAppInput):
    prompt: str = Field(description="输入提示")

class AppOutput(BaseAppOutput):  # 不是 BaseModel —— output_meta 需要这个
    image: File = Field(description="生成的图像")

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"调用 API，提示: {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()

        # 写入输出文件
        output_path = "/tmp/output.png"
        with open(output_path, "wb") as f:
            f.write(response.content)

        # 读取实际尺寸（不要硬编码！）
        from PIL import Image
        with Image.open(output_path) as img:
            width, height = img.size

        self.logger.info(f"生成了 {width}x{height} 图像")

        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()

资源配置（inf.yml）

my-app/
├── inf.yml           # 配置
├── inference.py      # 应用逻辑
├── requirements.txt  # Python 包 (pip)
└── packages.txt      # 系统包 (apt) —— 可选

my-app/
├── inf.yml           # 配置
├── src/
│   └── inference.js  # 应用逻辑
├── package.json      # Node.js 包 (npm/pnpm)
└── packages.txt      # 系统包 (apt) —— 可选

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

# 用于多功能应用 (默认: run)
# default_function: generate

resources:
  gpu:
    count: 1
    vram: 24    # 24GB (自动转换)
    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

CLI 自动转换人类友好的值：

< 1000 → GB (例如，80 = 80GB)
1000 到 1B → MB

any | nvidia | amd | apple | none

注意： 目前仅支持 NVIDIA CUDA GPU。

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

Python — requirements.txt：

torch>=2.0
transformers
accelerate

Node.js — package.json：

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

系统包 — packages.txt（可通过 apt 安装）：

ffmpeg
libgl1-mesa-glx

类型	镜像
GPU	`docker.inference.sh/gpu:latest-cuda`
CPU	`docker.inference.sh/cpu:latest`

根据语言和主题加载相应的参考文件：

应用逻辑与模式

references/python-app-logic.md — Python：Pydantic 模型、BaseApp、文件处理、类型提示、多功能模式
references/node-app-logic.md — Node.js：Zod 模式、文件处理、ESM、生成器、多功能模式

调试、优化与取消

references/python-patterns.md — Python：CUDA 调试、设备检测、模型加载、内存清理、混合精度、取消
references/node-patterns.md — Node.js：ESM/导入调试、流式传输、内存管理、并发、取消

references/python-secrets-oauth.md — Python：os.environ、OpenAI 客户端、HuggingFace 令牌、Google 服务账户
references/node-secrets-oauth.md — Node.js：process.env、OpenAI 客户端、Google 凭据 JSON

references/python-tracking.md — Python：OutputMeta、TextMeta、ImageMeta、VideoMeta、AudioMeta 类
references/node-tracking.md — Node.js：textMeta、imageMeta、videoMeta、audioMeta 工厂函数

references/cli.md — 完整的 CLI 命令参考，两种语言的先决条件

完整文档 : inference.sh/docs
示例 : github.com/inference-sh/grid

🇺🇸English

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 infsh 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 infsh 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.

CLI Installation

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



infsh update   # Update CLI
infsh login    # Authenticate
infsh me       # Check current user

Quick Start

Scaffold new apps with infsh 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.

infsh app init my-app              # Create app (interactive)
infsh 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

infsh 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
infsh app test --save-example      # Generate sample input from schema
infsh app test                     # Run with input.json
infsh app test --input '{"prompt": "hello"}'  # Or inline JSON

4. Deploy

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

5. Cloud Test & Verify

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

infsh 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
infsh app run user/app --input input.json
infsh app sample user/app
infsh app sample user/app --save input.json

App Structure

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

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 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

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.

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

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

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

Weekly Installs

116

Repository

inferen-sh/skills

GitHub Stars

235

First Seen

4 days ago

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138,800 周安装

Inference.sh应用开发指南：Python/Node.js构建与部署AI应用

🇨🇳中文介绍

Inference.sh 应用开发

规则

相关 Skills

CLI 安装

快速开始

开发工作流（强制）

1. 搭建

2. 实现

3. 本地测试

4. 部署

5. 云端测试与验证

应用结构

Python

Node.js

多功能应用

API 包装应用模板（Python）

资源配置（inf.yml）

项目结构

inf.yml

资源单位

GPU 类型

类别

仅 CPU 应用

依赖项

基础镜像

参考文件

应用逻辑与模式

调试、优化与取消

密钥与 OAuth

使用量追踪

CLI

资源

🇺🇸English

Inference.sh App Development

Rules

CLI Installation

Quick Start

Development Workflow (mandatory)

1. Scaffold

2. Implement

3. Test Locally

4. Deploy

5. Cloud Test & Verify

App Structure

Python

Node.js

Multi-Function Apps

API-Wrapper App Template (Python)

Configuring Resources (inf.yml)

Project Structure

inf.yml

Resource Units

GPU Types

Categories

CPU-Only Apps

Dependencies

Base Images

Reference Files

App Logic & Schemas

Debugging, Optimization & Cancellation

Secrets & OAuth

Usage Tracking

CLI

Resources

最新 Skills