Scheduled jobs and workflows#

Oftentimes you may want to run a job on a regular schedule. For example, fetching from a datasource every morning, compiling an analytics report every month, or detecting model drift every hour.

Creating a job and scheduling it#

MLRun makes it very simple to add a schedule to a given job. To showcase this, the following job runs the code below, which resides in a file titled

def hello(context):
    print("You just ran a scheduled job!")

To create the job, use the code_to_function syntax and specify the kind like below:

import mlrun

job = mlrun.code_to_function(
    name="my-scheduled-job",      # Name of the job (displayed in console and UI)
    filename="",       # Python file or Jupyter notebook to run
    kind="job",                   # Run as a job
    image="mlrun/mlrun",          # Use this Docker image
    handler="hello"               # Execute the function hello() within

Running the job using a schedule

To add a schedule, run the job and specify the schedule parameter using Cron syntax like so:"0 * * * *")

This runs the job every hour. An excellent resource for generating Cron schedules is

Scheduling a workflow#


Tech Preview

After loading the project (load_project), run the project with the scheduled workflow:"main", schedule='0 * * * *')

Remote/Scheduled workflows can be performed by a project with a remote source or one that is contained on the image. Remote source will be pulled each time the workflow is run, while the local source will be loaded from the image.
To use a remote source you can either put your code in Git or archive it and then set a source to it (e.g. git://, http://some/url/, s3://some/url/file.tar.gz etc.). By default, the defined project source will be used.

  • To set project source use the project.set_source method.

  • To set workflow use the project.set_workflow method.

To use a different remote source, specify the source URL whe running the workflow with<source-URL>) method.
You can also use a context path to load the project from a local directory contained in the image used for execution:

  • To set project source use the project.set_source method (make sure pull_at_runtime is set to False).

  • To build the image with the project yaml and code use project.build_image method. Optionally specify a target_dir for the project content.

  • Create the workflow e.g. project.set_workflow(name="my-workflow", workflow_path="./src/").

  • The default workflow image is project.spec.default_image which was enriched to and built with project.build_image unless specified otherwise.

  • Run the workflow with the context path e.g."my-workflow", source="./", engine="remote"). The source can be absolute or relative path with "." or "./".

Example for a remote GitHub project - mlrun/project-demo

import mlrun
project_name = "remote-workflow-example"
source_url = "git://"
source_code_target_dir = "./project" # Optional, relative to "/home/mlrun_code". A different absolute path can be specified.

# Create a new project
project = mlrun.load_project(context=f"./{project_name}", url=source_url, name=project_name)

# Set the project source and workflow
project.set_workflow(name="main", workflow_path="")

# Build the image, load the source to the target dir and save the project

# Run the workflow, load the project from the target dir on the image"main", source="./", engine="remote", dirty=True)

You can delete a scheduled workflow in the MLRun UI. To update a scheduled workflow, re-define the schedule in the workflow, for example:"main", schedule='0 * * * *')