本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

# 排除 Neo 推理错误
<a name="neo-troubleshooting-inference"></a>

本节包含有关如何防止和解决您在部署 and/or 调用端点时可能遇到的一些常见错误的信息。本节适用于 **PyTorch 1.4.0 或更高版本**以及 **MXNetv1.7.** 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 [https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor.predict](https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor.predict) 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：**

  ```
  W-9001-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - AttributeError: 'NoneType' object has no attribute 'transform'
  ```    
[See the AWS documentation website for more details](http://docs.aws.amazon.com/zh_cn/sagemaker/latest/dg/neo-troubleshooting-inference.html)
+ 创建 Amazon A SageMaker I 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，终端上可能会出现以下错误消息：

  ```
  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."
  ```