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AWS Sagemaker 内核似乎已经死亡并重新启动

[英]AWS Sagemaker Kernel appears to have died and restarts

I am getting a kernel error while trying to retrieve the data from an API that includes 100 pages.尝试从包含 100 页的 API 检索数据时出现内核错误。 The data size is huge but the code runs well when executed on Google Colab or on local machine.数据量巨大,但在 Google Colab 或本地机器上执行时代码运行良好。

The error I see in a window is-我在窗口中看到的错误是-

Kernel Restarting The kernel appears to have died. Kernel Restarting 内核似乎已经死了。 It will restart automatically.它会自动重启。

I am using an ml.m5.xlarge machine with a memory allocation of 1000GB and there are no pre-saved datasets in the instance.我正在使用内存分配为 1000GB 的 ml.m5.xlarge 机器,并且实例中没有预先保存的数据集。 Also, the expected data size is around 60 GB split into multiple datasets of 4 GB each.此外,预期的数据大小约为 60 GB,分为多个数据集,每个数据集为 4 GB。

Can anyone help?任何人都可以帮忙吗?

I think you could try not to load all the data into memory, or try to switch to a beefier instance type.我认为您可以尝试不将所有数据加载到内存中,或者尝试切换到更强大的实例类型。 According to https://aws.amazon.com/sagemaker/pricing/instance-types/ ml.m5.xlarge has 15GB memory.根据https://aws.amazon.com/sagemaker/pricing/instance-types/ml.m5.xlarge有 15GB 内存。

Jun

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