Configuring functions#

MLRun Functions (function objects) can be created by using any of the following methods:

  • new_function(): creates a function from code repository/archive.

  • code_to_function(): creates a function from local or remote source code (single file) or from a notebook (code file will be embedded in the function object).

  • import_function(): imports a function from a local or remote YAML function-configuration file or from a function object in the MLRun database (using a DB address of the format db://<project>/<name>[:<tag>]) or from the function marketplace (e.g. hub://describe). See MLRun Functions Marketplace.

When you create a function, you can:

  • Use the save() function method to save a function object in the MLRun database.

  • Use the export() method to save a YAML function-configuration to your preferred local or remote location.

  • Use the run() method to execute a task.

  • Use the as_step() method to convert a function to a Kubeflow pipeline step.

  • Use the .deploy() method to build/deploy the function. (Deploy for batch functions builds the image and adds the required packages. For online/real-time runtimes like nuclio and serving it also deploys it as an online service.)

Functions are stored in the project and are versioned so you can always view previous code and go back to previous functions if needed.

The general concepts described in this section are illustrated in the following figure:


In this section

Providing Function Code#

When using code_to_function() or new_function(), you can provide code in several ways:

Provide code as part of the function object#

This method is great for small and single file functions or for using code derived from notebooks. This example uses the mlrun code_to_function() method to create functions from code files or notebooks.

# create a function from py or notebook (ipynb) file, specify the default function handler
my_func = mlrun.code_to_function(name='prep_data', filename='./', kind='job', 
image='mlrun/mlrun', handler='my_func')

For more on how to create functions from notebook code, see Converting notebook code to a function.

Provide code as part of the function image#

Providing code as part of the image is good for ensuring that the function image has the integrated code and dependencies, and it avoids the overhead of loading code at runtime.

Use the deploy() method to build a function image with source code, dependencies, etc. Specify the build configuration using the build_config() method.

    # create a new job function from base image and archive + custom build commands
    fn = mlrun.new_function('archive', kind='job', command='./')
    fn.build_config(base_image='mlrun/mlrun', source='git://',
                    commands=["pip install pandas"])
    # deploy (build the container with the extra build commands/packages)
    # run the function (specify the function handler to execute)
    run_results ='my_func', params={"x": 100})

Alternatively, you can use a pre-built image:

# provide a pre-built image with your code and dependencies
fn = mlrun.new_function('archive', kind='job', command='./', image='some/pre-built-image:tag')
# run the function (specify the function handler to execute)
run_results ='my_func', params={"x": 100})

You can use this option with new_function() method.

Provide code from a git, zip, tar archive into the function at runtime#

This option is the most efficient when doing iterative development with multiple code files and packages. You can make small code changes and re-run the job without building images, etc. You can use this option with the new_function() method.

The local, job, mpijob and remote-spark runtimes support dynamic load from archive or file shares. (Other runtimes will be added later.) Enable this by setting the<archive> and or simply by setting the source attribute in new_function). In the CLI, use the --source flag.

fn = mlrun.new_function('archive', kind='job', image='mlrun/mlrun', command='./', 
run_results ='my_func', params={"x": 100})

See more details and examples on running jobs with code from Archives or shares.

Specifying the function execution handler or command#

The function is configured with code and dependencies, however you also need to set the main execution code either by handler or command.


A handler is a method (not a script) that executes the function, for either a one-time run or ongoing online services.


The command='./' specifies the command that is executed in the function container/workdir.

By default MLRun tries to execute python code with the specified command. For executing non-python code, set mode="pass" (passthrough) and specify the full execution command, e.g.:

new_function(... command="bash --myarg xx", mode="pass")  

If you need to add arguments in the command, use "mode=args" template ({..}) in the command to pass the task parameters as arguments for the execution command, for example:

new_function(... command='' --x {xparam}", mode="args")

where {xparam} is substituted with the value of the xparam parameter.
It is possible to use argument templates also when using mode="pass".

See also Execute non Python code and Inject parameters into command line.