排除 Neo 推理错误 - Amazon SageMaker
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排除 Neo 推理错误

此部分包含有关如何预防和解决您在部署和/或调用端点时可能遇到的一些常见错误的信息。此部分适用于 PyTorch 1.4.0 或更高版本和 MXNet v1.7.0 或更高版本。

  • 如果您在推理脚本中定义了 model_fn，请确保对有效输入数据的第一次推理（预热推理）是在 model_fn() 中完成的，否则调用 predict API 时终端上可能会显示以下错误消息：

    An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (0) from <users-sagemaker-endpoint> with message "Your invocation timed out while waiting for a response from container model. Review the latency metrics for each container in Amazon CloudWatch, resolve the issue, and try again."

  • 确保设置下表所示的环境变量。如果未设置，则可能会显示以下错误消息：

    在终端上：

    An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (503) from <users-sagemaker-endpoint> with message "{ "code": 503, "type": "InternalServerException", "message": "Prediction failed" } ".

    在 CloudWatch 中：

    W-9001-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - AttributeError: 'NoneType' object has no attribute 'transform'
    键 值
    SAGEMAKER_PROGRAM inference.py
    SAGEMAKER_SUBMIT_DIRECTORY /opt/ml/model/code
    SAGEMAKER_CONTAINER_LOG_LEVEL 20
    SAGEMAKER_REGION <您的区域>

  • 创建 Amazon SageMaker 模型时，请确保将 MMS_DEFAULT_RESPONSE_TIMEOUT 环境变量设置为 500 或更高的值；否则，终端上可能会显示以下错误消息：

    An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (0) from <users-sagemaker-endpoint> with message "Your invocation timed out while waiting for a response from container model. Review the latency metrics for each container in Amazon CloudWatch, resolve the issue, and try again."