python - 如何在等待响应时增加 AWS Sagemaker 调用超时

Question

我向 aws sagemaker 部署了一个大型 3D 模型。推理将需要 2 分钟或更长时间。从 Python 调用预测器时出现以下错误:

An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (0) from model 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."'

在 Cloud Watch 中，我还看到容器正在处理时出现一些 PING 超时:

2020-10-07T16:02:39.718+02:00 2020/10/07 14:02:39 https://forums.aws.amazon.com/ 106#106: *251 upstream timed out (110: Connection timed out) while reading response header from upstream, client: 10.32.0.2, server: , request: "GET /ping HTTP/1.1", upstream: "http://unix:/tmp/gunicorn.sock/ping", host: "model.aws.local:8080"

如何增加调用超时？
或者有没有办法对 sagemaker 端点进行异步调用？

Answer 1

目前无法增加超时——这是 GitHub 中的一个 Unresolved 问题。仔细查看有关 SO 的问题和类似问题，您似乎可以将批量转换与推理结合使用。
引用
https://stackoverflow.com/a/55642675/806876
Sagemaker Python SDK 超时问题:https://github.com/aws/sagemaker-python-sdk/issues/1119