Package: argo-jupyter-scheduler
argo-jupyter-schedulerTable of Contentsargo-jupyter-schedulerInstallationWhat is it?Optional featuresSending to SlackA deeper diveJobJob DefinitionInternalsOutput FilesWorkflow StepsAdditional thoughtsKnown issuesLicenseArgo-Jupyter-SchedulerSubmit long-running notebooks to run without the need to keep your JupyterLab server running. And submit a notebook to run on a specified schedule.Installationpip install argo-jupyter-schedulerWhat is it?Argo-Jupyter-Scheduler is a plugin to theJupyter-SchedulerJupyterLab extension.What does that mean?This means this is an application that gets installed in the JupyterLab base image and runs as an extension in JupyterLab. Specifically, you will see this icon at the bottom of the JupyterLab Launcher tab:And this icon on the toolbar of your Jupyter Notebook:This also means, as a lab extension, this application is running within each user's separate JupyterLab server. The record of the notebooks you've submitted is specific to you and you only. There is no central Jupyter-Scheduler.However, instead of using the base Jupyter-Scheduler, we are usingArgo-Jupyter-Scheduler.Why?If you want to run your Jupyter Notebook on a schedule, you need to be assured that the notebook will be executed at the times you specified. The fundamental limitation with Jupyter-Scheduler is that when your JupyterLab server is not running, Jupyter-Scheduler is not running. Then the notebooks you had scheduled won't run. What about notebooks that you want to run right now? If the JupyterLab server is down, then how will the status of the notebook run be recorded?The solution is Argo-Jupyter-Scheduler: Jupyter-Scheduler front-end with an Argo-Workflows back-end.Optional featuresSending to SlackArgo-Jupyter-Scheduler allows sending HTML output of an executed notebook to a
Slack channel:See the Slack API docs on how to create a bot token (starts withxoxb)Invite your bot to a Slack channel which will be used for sending outputWhen scheduling a notebook (as described above):Select a conda environment that haspapermillinstalledAdd the followingParameters:name:SLACK_TOKEN, value:xoxb-<Slack bot token>name:SLACK_CHANNEL, value:<Slack channel name>(without leading#, likescheduled-jobs).Create job:Slack output:A deeper diveIn the Jupyter-Scheduler lab extension, you can create two things, aJoband aJob Definition.JobAJob, or notebook job, is when you submit your notebook to run.In Argo-Jupyter-Scheduler, thisJobtranslates into aWorkflowin Argo-Workflows. So when you create aJob, your notebook job will create a Workflow that will run regardless of whether or not your JupyterLab server is.At the moment, permission to submit Jobs is required, managed by the Keycloak roles for theargo-server-ssoclient. If your user has either theargo-adminor theargo-developerroles, they will be permitted to create and submit Jobs (and Job Definitions).We are also relying on theNebari Workflow Controllerto ensure the user's home directory and conda-store environments are mounted to the Workflow. This allows us to ensure:the files in the user's home directory can be used by the notebook jobthe output of the notebook can be saved locallywhen the conda environment that is used gets updated, it is also updated for the notebook job (helpful for scheduled jobs)the node-selector and image you submit your notebook job from are the same ones used by the workflowJob DefinitionAJob-Definitionis simply a way to create to Jobs that run on a specified schedule.In Argo-Jupyter-Scheduler,Job Definitiontranslate into aCron-Workflowin Argo-Workflows. So when you create aJob Definition, you create aCron-Workflowwhich in turn creates aWorkflowto run when scheduled.AJobis toWorkflowasJob Definitionis toCron-Workflow.InternalsJupyter-Scheduler creates and uses ascheduler.sqlitedatabase to manage and keep track of the Jobs and Job Definitions. If you can ensure this database is accessible and can be updated when the status of a job or a job definition change, then you can ensure the view the user sees from JupyterLab match is accurate.By default this database is located at~/.local/share/jupyter/scheduler.sqlitebut this is a traitlet that can be modified. And since we have access to this database, we can update the database directly from the workflow itself.To accomplish this, the workflow runs in two steps. First the workflow runs the notebook, usingpapermilland the conda environment specified. And second, depending on the success of this notebook run, updates the database with this status.And when a job definition is created, a corresponding cron-workflow is created. To ensure the database is properly updated, the workflow that the cron-workflow creates has these three steps. First, create a job record in the database with a status ofIN PROGRESS. Second, run the notebook, again usingpapermilland the conda environment specified. And third, update the newly created job record with the status of the notebook run.Output FilesIn addition topapermill, which creates the output notebook,jupyter nbconvertis used to produce HTML output. To make these output files
downloadable via the web UI, it's important they match the format that
Jupyter-Scheduler expects, which is achieved by reusingcreate_output_filenamewithin Argo-Jupyter-Scheduler when creating output files.The expected output filenames include timestamps that must match the start time
of a job. For cron jobs this is tricky because the start time is set whenevercreate-job-idis run. All workflow steps are run in separate containers and
thecreate-job-idcontainer is run after thepapermillstep, which creates
the output files.Also, thepapermillcontainer is defined differently because it needs to have
access to filesystem mount points where thepapermillandjupytercommands
are located as well as access the environment variables. Due to this, the
commands executed within thepapermillcontainer cannot be changed once it's
been defined.This also means that thepapermillcontainer cannot have access to the job
start time and hence cannot create filenames with the expected timestamps. To
solve this problem, thepapermillstep always creates output files with the
same default filenames and there is an additionalrename-filesstep that runs
aftercreate-job-id, which makes sure the timestamps match the job start time.
To pass the start time value between containers, the SQLite database is used.Finally, becausecreate-job-idcreates a new job every time it runs, this job
will also have a new id. The job id is important since it's the same as the name
of the staging directory where job output files are expected to be found by
Jupyter-Scheduler. But the output files are created in thepapermillstep,
which has the id of a job that defined the workflow originally when it was
scheduled, not the current one created increate-job-id. To point to the
proper location on disk, a symlink is created connecting staging job
directories. This is also done in therename-filesstep by looking up the job
ids in the SQLite database.For non-cron jobs, there is nocreate-job-idstep. The rest of the workflow
steps are the same, but no database lookups are performed and no symlinks are
created. This is not necessary because the start time is immediately available
and the job id matches the job staging area.Workflow StepsHere's the overview of the workflow steps:mainrunspapermillandjupyter nbconvertto create output filescreate-job-idcreates a new job that can run without JupyterLab (only
for cron jobs)rename-filesupdates timestamps of output files and adds symlinks between
job staging directoriessend-to-slacksends HTML output to a Slack channel (only ifSLACK_TOKENandSLACK_CHANNELare provided viaParameterswhen scheduling a job)failureorsuccesssets status as "Failed" or "Completed" in the web UI.These steps are executed sequentially in separate containers. If a step fails,
thefailurestep is called in the end. Otherwise, thesuccessstep is
called.Additional ThoughtsAt the moment, Argo-Jupyter-Scheduler is closely coupled with Nebari (via the Nebari-Workflow-Controller) which doesn't make it very useable for other projects. There's no need for this to necessarily be the case. By leveraging Traitlets, we can include other ways of modifying the pod spec for the running workflow and enable it to be used by other projects. If you're interested in this project and would like to see it extended, feel free to open an issue to discuss your ideas. Thank you :)Known IssuesAll of the core features of Jupyter-Scheduler have been mapped over to Argo-Jupyter-Scheduler. Unfortunately, there is currently a limitation withUpdate Job Definitionand withPause/Resumefor Job Definitions. Although thePauseworks, theResumefails for the same reasonUpdate Job Definitiondoes and this is because the upstream Nebari-Workflow-Controller (seeKnown Limitations) has a limitation whereby it can't resubmit workflows/cron-workflows; there are more details inthis issue.Licenseargo-jupyter-scheduleris distributed under the terms of theMITlicense.