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Perago

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Perago 是一个面向 Conductor worker 的 typed Python 运行时层,用来在版本化 workspace 上执行任务。它把任务函数签名、Pydantic 输入输出契约、Conductor TaskDef、LakeFS workspace 下载与按需发布、guardrail 校验和 worker 启动边界收敛到同一套模型里。

本文档提供项目概览、安装方式和最小入口;完整说明见文档站。

安装

要求 Python 3.10 或更新版本。

uv add perago

或:

pip install perago

仓库本地开发使用:

uv sync

最小示例

下面是一个最小的 workspace task。任务作者只需要声明 typed params/output、workspace 约束和任务元数据;Perago 负责校验、TaskDef 提取和运行时集成。

from pathlib import Path

from pydantic import BaseModel, Field

from perago import WorkspaceSpec, require_glob, task


class BuildFeaturesParams(BaseModel):
    feature_set: str
    min_rows: int = Field(ge=1)


class BuildFeaturesOutput(BaseModel):
    row_count: int = Field(ge=0)
    feature_count: int = Field(ge=0)


@task(
    name="features.build",
    owner_email="data@example.com",
    workspace=WorkspaceSpec(
        prefix="/",
        pre=[require_glob("raw/**/*.parquet", min_count=1)],
    ),
)
def build_features(
    workspace: Path,
    params: BuildFeaturesParams,
) -> BuildFeaturesOutput:
    return BuildFeaturesOutput(row_count=100, feature_count=24)

workspace-free task 使用同一套 decorator 和 typed contract,但不声明 workspace: Path,也不声明 WorkspaceSpec

快速入口

perago CLI 的 MVP 入口是单任务 module target:

perago check app.workers.features_build
perago extract app.workers.features_build --output generated/features.build.json
perago start app.workers.features_build -j 4
  • perago check:导入模块并校验任务声明与本地 runtime config。
  • perago extract:生成带嵌入 schema 的 Conductor TaskDef JSON。
  • perago start:在真实 Conductor 配置下启动 supervisor-managed worker processes;workspace task 还需要 LakeFS 配置。

文档

仓库内的对应内容可从这里继续展开:

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