问题描述
根据>是否可以使用一个用于Dataflow实例的自定义计算机?您可以通过将名称指定为custom-<number of cpus>-<memory in mb>
According to Is it possible to use a Custom machine for Dataflow instances? you can set the custom machine type for a dataflow operation by specifying the name as custom-<number of cpus>-<memory in mb>
但是答案是针对Java Api和旧的Dataflow版本，而不是新的Apache Beam实现和Python.
But that answer is for the Java Api and the old Dataflow version, not the new Apache Beam implementation and Python.
如果我在2.0.0 google-cloud-dataflow python API中提供了--worker_machine_type custom-8-5376，则会出现以下错误:
If I supply --worker_machine_type custom-8-5376in the 2.0.0 google-cloud-dataflow python API, I get the following error:
我还尝试在计算引擎中定义一个新的实例模板，并在--worker_machine_type参数中提供该模板的名称，但这也不起作用.
I also tried defining a new instance template in the compute engine and supplying the name of that template in the --worker_machine_type parameter, but that doesn't work, either.
如何使用自定义计算机类型在Dataflow 2.0.0上运行工作流?
How can you run a workflow on Dataflow 2.0.0 with a custom machine type?
推荐答案
每个自定义计算机类型doc: https://cloud.google.com/compute/docs/instances/creating-instance-with-custom-machine-type
per custom machine type doc:https://cloud.google.com/compute/docs/instances/creating-instance-with-custom-machine-type
因此对于8个vCPU，最小为7424MiB.
So for 8 vCPUs, 7424MiB is the minimum.
能否请您再试一次?
这篇关于Python Dataflow SDK中的自定义机器类型的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！