腾讯云TI平台详解

"""腾讯云TI平台服务调用示例使用腾讯云SDK调用TI平台API，模拟从数据集创建到模型训练任务的完整流程"""import jsonimport time class TIClient:    """模拟腾讯云TI平台的客户端，实际使用请安装腾讯云SDK"""    def __init__(self, secret_id="AKIDEXAMPLE", secret_key="SECRETKEY"):        self.secret_id = secret_id        self.secret_key = secret_key        self.datasets = []        self.training_jobs = []        self.models = []        self.job_count = 0            def create_dataset(self, name, data_type, data_url):        """创建数据集"""        dataset = {            "DatasetId": "ds-" + str(int(time.time())),            "DatasetName": name,            "DataType": data_type,            "DataUrl": data_url,            "Status": "AVAILABLE"        }        self.datasets.append(dataset)        print(f"[创建数据集] {name} 类型={data_type}")        return dataset        def list_datasets(self):        """查看数据集列表"""        print("\n[数据集列表]")        for ds in self.datasets:            print(f"  {ds['DatasetId']} | {ds['DatasetName']} | {ds['DataType']} | {ds['Status']}")        return self.datasets        def create_training_job(self, job_name, dataset_id, model_type, gpu_type="T4", node_count=1):        """创建模型训练任务"""        self.job_count += 1        job = {            "JobId": "job-" + str(self.job_count),            "JobName": job_name,            "DatasetId": dataset_id,            "ModelType": model_type,            "GPUType": gpu_type,            "NodeCount": node_count,            "Status": "PENDING",            "CreateTime": time.strftime("%Y-%m-%d %H:%M:%S")        }        self.training_jobs.append(job)        print(f"\n[创建训练任务] {job_name} 数据集={dataset_id} 模型={model_type} GPU={gpu_type}x{node_count}")        return job        def run_training_job(self, job_id, epochs=10, batch_size=32):        """启动训练任务，模拟训练过程"""        job = None        for j in self.training_jobs:            if j["JobId"] == job_id:                job = j                break        if not job:            raise ValueError(f"训练任务 {job_id} 不存在")                job["Status"] = "RUNNING"        print(f"[启动训练] {job['JobName']} epochs={epochs} batch_size={batch_size}")                # 模拟训练进度        for epoch in range(1, epochs + 1):            time.sleep(0.2)            loss = 1.0 / (epoch + 1) + 0.05            accuracy = epoch / epochs * 0.9 + 0.05            print(f"  Epoch {epoch}/{epochs} - loss: {loss:.4f} - acc: {accuracy:.4f}")                job["Status"] = "SUCCEEDED"        job["Accuracy"] = round(accuracy, 4)        print(f"[训练完成] {job['JobName']} 最终准确率={job['Accuracy']:.2%}")        return job        def create_model(self, job_id, model_name, model_version="v1"):        """基于训练结果创建模型"""        job = None        for j in self.training_jobs:            if j["JobId"] == job_id:                job = j                break        if not job:            raise ValueError(f"训练任务 {job_id} 不存在")                model = {            "ModelId": "model-" + str(len(self.models) + 1),            "ModelName": model_name,            "JobId": job_id,            "Version": model_version,            "Framework": job["ModelType"],            "Status": "READY",            "CreateTime": time.strftime("%Y-%m-%d %H:%M:%S")        }        self.models.append(model)        print(f"\n[创建模型] {model_name} 基于任务={job_id} 版本={model_version}")        return model        def deploy_model(self, model_id, service_name, replicas=1):        """部署模型为在线服务"""        model = None        for m in self.models:            if m["ModelId"] == model_id:                model = m                break        if not model:            raise ValueError(f"模型 {model_id} 不存在")                service = {            "ServiceId": "svc-" + str(len(self.models) * 10),            "ServiceName": service_name,            "ModelId": model_id,            "Replicas": replicas,            "Status": "DELOYING",            "Endpoint": f"https://{service_name}.ti.tencentcloudapi.com"        }        print(f"\n[部署模型] {service_name} 模型={model_id} 副本数={replicas}")        print(f"[部署完成] 服务地址: {service['Endpoint']}")        return service  if __name__ == "__main__":    # 初始化TI平台客户端    client = TIClient()        # 1. 创建数据集    dataset = client.create_dataset("商品识别数据集", "图像", "cos://bucket/data/products")        # 2. 查看数据集列表    client.list_datasets()        # 3. 创建并启动训练任务    job = client.create_training_job("商品识别训练", dataset["DatasetId"], "YOLOv5", gpu_type="V100", node_count=2)    client.run_training_job(job["JobId"], epochs=5, batch_size=64)        # 4. 创建并部署模型    model = client.create_model(job["JobId"], "商品识别模型")    service = client.deploy_model(model["ModelId"], "goods-recognition", replicas=3)