Kubeflow ML管道：云原生MLOps流水线搭建

from typing import Dict, List, Any, Optionalfrom dataclasses import dataclass, fieldfrom datetime import datetime @dataclassclass PipelineStep:    """管道步骤定义：容器化的独立执行单元"""    name: str    container_image: str    command: List[str]    dependencies: List[str] = field(default_factory=list)    parameters: Dict[str, str] = field(default_factory=dict)    outputs: List[str] = field(default_factory=list)    status: str = "pending" class KubeflowPipeline:    """Kubeflow Pipelines编排引擎模拟"""    def __init__(self, name: str):        self.name = name        self.steps: Dict[str, PipelineStep] = {}        self.run_history: List[Dict] = []        self.artifacts: Dict[str, Any] = {}     def add_step(self, step: PipelineStep):        """添加管道步骤"""        self.steps[step.name] = step     def _topological_sort(self) -> List[str]:        """拓扑排序确定执行顺序"""        visited = set()        result = []        def dfs(name: str):            if name in visited:                return            visited.add(name)            for dep in self.steps[name].dependencies:                dfs(dep)            result.append(name)        for name in self.steps:            dfs(name)        return result     def run(self, params: Dict[str, str] = None) -> Dict:        """执行管道并记录溯源信息"""        params = params or {}        run_id = f"run-{len(self.run_history) + 1}"        order = self._topological_sort()        for step_name in order:            step = self.steps[step_name]            step.status = "running"            print(f"[{step_name}] Running container {step.container_image}")            for output in step.outputs:                artifact_key = f"{run_id}/{step_name}/{output}"                self.artifacts[artifact_key] = {"data": f"output_of_{step_name}"}            step.status = "succeeded"        run_record = {            "run_id": run_id,            "pipeline_version": "v1.0",            "timestamp": datetime.now().isoformat(),            "params": params,            "step_statuses": {n: s.status for n, s in self.steps.items()},            "image_tags": {n: s.container_image for n, s in self.steps.items()}        }        self.run_history.append(run_record)        return run_record     def reproduce(self, run_id: str) -> Dict:        """根据历史运行记录重放复现"""        target = None        for r in self.run_history:            if r["run_id"] == run_id:                target = r                break        if not target:            return {"error": f"Run {run_id} not found"}        return self.run(target["params"]) class TrainingJob:    """分布式训练算子模拟（如PyTorchJob）"""    def __init__(self, name: str, framework: str = "pytorch"):        self.name = name        self.framework = framework        self.workers: List[Dict] = []        self.ps_count: int = 0        self.status: str = "created"     def add_worker(self, replicas: int, gpus: int = 0, cpu: str = "4"):        """添加worker角色配置"""        self.workers.append({            "replicas": replicas,            "gpus": gpus,            "cpu": cpu,            "memory": "16Gi"        })     def submit(self) -> str:        """提交训练任务到K8s集群"""        total_gpus = sum(w["gpus"] * w["replicas"] for w in self.workers)        self.status = "running"        print(f"[{self.framework}Job] {self.name} submitted with {total_gpus} GPUs")        return f"job-{self.name}" class KatibExperiment:    """Katib超参搜索实验模拟"""    def __init__(self, name: str, strategy: str = "bayesian"):        self.name = name        self.strategy = strategy        self.search_space: Dict[str, Any] = {}        self.trials: List[Dict] = []        self.best_trial: Optional[Dict] = None     def set_search_space(self, space: Dict[str, Any]):        """定义超参搜索空间"""        self.search_space = space     def run(self, max_trials: int = 50, parallel: int = 5) -> Dict:        """执行超参搜索"""        import random        for i in range(max_trials):            params = {}            for param_name, spec in self.search_space.items():                if spec["type"] == "float":                    params[param_name] = round(                        random.uniform(spec["min"], spec["max"]), 6)                elif spec["type"] == "int":                    params[param_name] = random.randint(spec["min"], spec["max"])            objective = round(random.uniform(0.85, 0.99), 4)            trial = {"trial_id": i, "params": params, "objective": objective}            self.trials.append(trial)        self.best_trial = max(self.trials, key=lambda t: t["objective"])        return self.best_trial