The processor spends pulling our container. What is cpu time in user space, as opposed to system / kernel space? It’s the amount of time First, let’s look at cpu time in user space during the pull of alpine. Most of these pulls are between 4 and 10 seconds, so there isn’t a ton of data recorded, but I’ll quickly show an example I installed watchme, and opted to pipe results directly into files named according to the parameters. How I’m handing off the process for watchme to run and watch. Notice how the “singularity pull” command is wrapped with “watchme monitor” - this is Is my quick submission loop (the sbatch command submits the job in the file pull-job.sh): This meant that I launched a job, and manipulated only the amount of memory. Is there varying performance based on the amount of memory available? The goal was to create plots, taking a measurement each second, and I wanted to collect resource usage during a Singularity pull of severalĬontainers including ubuntu, busybox, centos, alpine, and nginx. Why Should I Care? and then talk about why in the world you should care at all.įeel free to jump around if one is more interesting to you.Measure Memory Usage for a containerized sklearn model.Monitor Container Pulls on the Sherlock cluster using Singularity.Git log -all -oneline -pretty =tformat: "%H" -grep "ADD results" cca3c4eb84d9c38527ec93a9a620bfab07d798f2.5d2b047dabe74e76e1585341bb956fd633bd0832 - task-air-oakland/oakland.If you are interested, here is an asciinema video of that in action.īut let’s skip over the dummy examples and jump into something a little more fun - using # watchme export $ watchme export air-quality task-air-oakland oakland.txt Or an entire analysis (e.g., running a container) and taking samples $ watchme monitor singularity pull docker://busybox If you want to change the default, youįor example, here is what a set of three watchers might look like in your watchme home. Your watchers will live by default in subfolders of $HOME/.watchme, and We do this with a combination of cron jobs (scheduling) and git repositories (versionĬontrol). Interactive Python specifically, interaction from within Python.Ī watcher is a configuration to check a resource like a website at some frequency.Variables and Environment change defaults and settings via environmnet variables, or set variables that work across watcher tasks.Concepts including watchers and their types.Contribute a Task ranging from interacting with web APIs to local processes or networking. Get a watcher from GitHub, meaning cloning a repo to use.Export: data for a particular result file and task.Remove a task from a watcher, if it’s not frozen.Schedule your watcher to run at some frequency using cron.Run: your watcher manually if you want to test or otherwise.Activate or deactivate your watcher and/or associated tasks.Protect or freeze your watcher against accidental deletion.Inspect your watcher configuration easily.
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