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Deployment slots share the same resources. The percentage of CPU used is what is taken by all slots combined. After a swap, it should still monitor the production slot.


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As a short term solution you can replay you last good build. Read the docs for more information Update: There is a bug , so the proposed way will work only in multibranch pipeline. So as a short term solution you can convert your job to a multibranch pipeline and to use a replay when it’s needed As a long term solution I would separate build and ...


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Problem solved, I think: the agent comes with its own libcurl.so.3 in /opt/oms/lib, it doesn't use the system one at all. Once I replaced that, it seems to be succeeding. Previously: $ cd /opt/microsoft/omsconfig/Scripts $ ./PerformRequiredConfigurationChecks.py instance of OMI_Error { OwningEntity=OMI:CIMOM MessageID=OMI:MI_Result:1 Message=...


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The problem is with the image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:18.12.01 ("Linux Data Science VM" (on the Azure portal). This is an old image with R 3.4 instead of the release version of 3.5. I succeeded with the following virtual machine (Ubuntu Server 19.04.19, size D64 v3): az vm create \ --resource-group <resource ...


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