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CommunityData:Hyak
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== Common Troubles and How to Solve Them == === Help! I'm over CPU quota and Hyak is angry! === '''Don't panic.''' Everyone has done this at least once. Mako has done it dozens of times. It is a little bit difficult to deal with but can be solved. You are not in trouble. The usual reason for this to happen is because you've accidentally run something on a login node that ought to be run on a compute node. The solution is to find the badly behaved process and then use kill to kill the process. If it's a script or command on your commandline, '''Ctrl-c''' to kill it. If you backgrounded it, type <code>fg</code> to foreground it and then '''Ctrl-c'''. But if you ran parallel, you'll need to kill parallel itself. <code>ps -faux | grep <your username></code> will show you all the things you are running (or have someone else run it for you if the spam is so terrible you can't get a command to run). The first column has the usernames, the second column has the process IDs, the last column has the things you're running. [[File:faux.jpg]] In the screenshot, the red is the user name being grepped for. At the end of the line the last three entries are the time (in hyak time, type date if you want to compare hyak time to your time), then how much CPU time something has consumed, then a little diagram of parent and child processes. You want parallel (in the example, 9977). Killing the child process (in the example, 9992) won't likely help because parallel will just go on to the next task you queued up for it. You will need to run something like: <code>kill <process id></code> === My R Job is getting Killed === First, make sure you're running on a compute node (like n2344) or else the int_machine and don't use a --time-min flag -- there seems to be a bug with --time-min where it evicts jobs incorrectly. Second, see if you can narrow down where in your R code the problem is happening. Kaylea has seen it primarily when reading or writing files, and this tip is from that experience. Breaking the read or write into smaller chunks (if that makes sense for your project) might be all it takes.
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