High python CPU usage associated w/ Gurobi Cloud?



  • Greg Glockner

    2 things to check:

    1. Ensure your local machine configuration was updated correctly so that the computation is running on the cloud. The console and the log should show something like:
      Capacity available on '999999-default' cloud pool - connecting...
      Established HTTPS encrypted connection with Compute Server
    2. Only the solve itself takes place on the cloud; anything else including data processing or model building is still done on your local computer in Python.
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  • Margaret McCall

    Thanks, Greg. On point #1, I don't see anything like that, and I'm not quite sure where I would expect to. This may be because my call to gurobi is not directly in the script I'm running (it's called within a module); I don't know if it matters that I'm using a jupyter notebook instead of running a .py file in the terminal... (I guess this is an issue with me understanding which console/log you're talking about).

    However, when I run my script, I see the pool in the cloud manager turn from yellow to green and to #Ready. Is this enough to tell that it's running in the cloud?

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  • Eli Towle

    Hi Margaret,

    By default, the messages Greg mentioned would appear in the logfile and output produced by Gurobi when you solve a model. E.g.:

    Gurobi 9.0.2 (mac64, gurobi_cl) logging started Fri Jun 26 12:42:43 2020
    Using license file /Users/towle/gurobi.lic
    Set parameter CloudAccessID
    Set parameter CloudSecretKey
    Set parameter CloudPool to value 999999-default
    Set parameter LogFile to value gurobi.log
    Waiting for cloud server to start (pool 999999-default)...
    Compute Server job ID: xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx
    Capacity available on '999999-default' cloud pool - connecting...
    Established HTTPS encrypted connection

    Gurobi Optimizer version 9.0.2 build v9.0.2rc0 (mac64)
    Copyright (c) 2020, Gurobi Optimization, LLC

    That said, I think you're using Pyomo, so the output is probably disabled by default. You can enable it by setting \( \texttt{tee=True} \) in the \( \texttt{solve()} \) command:

    results = opt.solve(model, tee=True)

    If the pool status icon turns green and is "Ready", then the pool successfully launched and is ready to solve models. After you begin optimizing, a new job should show up in the Jobs view of the Cloud Manager interface. Do you see your jobs there?



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