Command-Line Interface#

CLI commands#

Use the following commands of the MLRun command-line interface (CLI) — mlrun — to build and run MLRun functions:

Each command supports many flags, some of which are listed in their relevant sections. To view all the flags of a command, run mlrun <command name> --help.


Use the build CLI command to build all the function dependencies from the function specification into a function container (Docker image).

Usage: mlrun build [OPTIONS] FUNC_URL

Example: mlrun build myfunc.yaml



−−name TEXT

Function name

−−project TEXT

Project name

−−tag TEXT

Function tag

-i, −−image TEXT

Target image path

-s, −−source TEXT

Path/URL of the function source code. A PY file, or if `-a

-b, −−base-image TEXT                              

Base Docker image

-c, −−command TEXT

Build commands; for example, ‘-c pip install pandas’

−−secret-name TEXT

Name of a container-registry secret

-a, −−archive TEXT

Path/URL of a target function-sources archive directory: as part of the build, the function sources (see `-s


Do not show build logs


Add the MLRun package (“mlrun”)

−−db TEXT

Save the run results to path or DB url

-r, −−runtime TEXT

Function spec dict, for pipeline usage


Running inside Kubeflow Piplines, do not use


Skip if already deployed

Note: For information about using the -a|--archive option to create a function-sources archive, see Using a Sources Archive later in this tutorial.


Use the clean CLI command to clean runtime resources. When run without any flags, it cleans the resources for all runs of all runtimes.

Usage: mlrun clean [OPTIONS] [KIND] [id]


  • Clean resources for all runs of all runtimes: mlrun clean

  • Clean resources for all runs of a specific kind (e.g. job): mlrun clean job

  • Clean resources for specific job (by uid): mlrun clean mpijob 15d04c19c2194c0a8efb26ea3017254b




Clean resources for all runs of a specific kind (e.g. job).


Delete the resources of the mlrun object twith this identifier. For most function runtimes, runtime resources are per Run, and the identifier is the Run’s UID. For DASK runtime, the runtime resources are per Function, and the identifier is the Function’s name.




URL of the mlrun-api service.

-ls, −−label-selector                

Delete only runtime resources matching the label selector.

-f, −−force

Delete the runtime resource even if they’re not in terminal state or if the grace period didn’t pass.

-gp, −−grace-period            

Grace period, in seconds, given to the runtime resource before they are actually removed, counted from the moment they moved to the terminal state.


Use the config CLI command to show the mlrun client environment configuration, such as location of artifacts and api.

Example: mlrun config


Use the get CLI command to list one or more objects per kind/class.

Usage: get pods | runs | artifacts | func [name]


  • mlrun get runs --project getting-started-admin

  • mlrun get pods --project getting-started-admin

  • mlrun get artifacts --project getting-started-admin

  • mlrun get func prep-data --project getting-started-admin




Name of object to return

-s, −−selector

Label selector

-n, −−namespace

Kubernetes namespace


Object ID


Project name to return

-t, −−tag

Artifact/function tag of object to return


db path/url of object to return


Use the logs CLI command to get or watch task logs.

Usage: logs [OPTIONS] uid

Example: mlrun logs ba409c0cb4904d60aa8f8d1c05b40a75 --project getting-started-admin



-p, −−project TEXT

Project name

−−offset INTEGER

Retrieve partial log, get up to size bytes starting at the offset from beginning of log

−−db TEXT

API service url

-w, −−watch

Retrieve logs of a running process, and watch the progress of the execution until it completes. Prints out the logs and continues to periodically poll for, and print, new logs as long as the state of the runtime that generates this log is either pending or running.


Use the project CLI command to load and/or run a project.

Usage: mlrun project [OPTIONS] [CONTEXT]

Example: mlrun project -r .



-n, −−name TEXT

Project name

-u, −−url TEXT

Remote git or archive url of the project

-r, −−run TEXT

Run workflow name of .py file

-a, −−arguments TEXT

Kubeflow pipeline arguments name and value tuples (with -r flag), e.g. -a x=6

-p, −−artifact_path TEXT

Target path/url for workflow artifacts. The string {{workflow.uid}} is replaced by workflow id

-x, −−param TEXT

mlrun project parameter name and value tuples, e.g. -p x=37 -p y=‘text’

-s, −−secrets TEXT

Secrets file= or env=ENV_KEY1,…

−−namespace TEXT

k8s namespace

−−db TEXT

API service url


For new projects init git the context dir

-c, −−clone

Force override/clone into the context dir


Sync functions into db

-w, −−watch

Wait for pipeline completion (with -r flag)

-d, −−dirty

Allow run with uncommitted git changes

−−git_repo TEXT

git repo (org/repo) for git comments

−−git_issue INTEGER

git issue number for git comments

−−handler TEXT

Workflow function handler name

−−engine TEXT

Workflow engine (kfp/local)


Try to run workflow functions locally


Use the run CLI command to execute a task and inject parameters by using a local or remote function.

Usage: mlrun [OPTIONS] URL [ARGS]…


  • mlrun run -f db://getting-started-admin/prep-data --project getting-started-admin

  • mlrun run -f myfunc.yaml -w -p p1=3



-p, −−param TEXT

Parameter name and value tuples; for example, -p x=37 -p y='text'

-i, −−inputs TEXT

Input artifact; for example, -i infile.txt=s3://mybucket/infile.txt

−−in-path TEXT

Base directory path/URL for storing input artifacts

−−out-path TEXT

Base directory path/URL for storing output artifacts

-s, −−secrets TEXT

Secrets, either as file=<filename> or env=<ENVAR>,...; for example, -s file=secrets.txt

−−name TEXT

Run name

−−project TEXT

Project name or ID

-f, −−func-url TEXT

Path/URL of a YAML function-configuration file, or db:///[:tag] for a DB function object

−−task TEXT

Path/URL of a YAML task-configuration file

−−handler TEXT

Invoke the function handler inside the code file


Use the version CLI command to get the mlrun server version.

The watch Command#

Use the watch CLI command to read the current or previous task (pod) logs.

Usage: mlrun watch [OPTIONS] POD

Example: mlrun watch prep-data-6rf7b



-n, −−namespace

kubernetes namespace

-t, −−timeout

Timeout in seconds


Use the watch-stream CLI command to watch a v3io stream and print data at a recurring interval.

Usage: mlrun watch-stream [OPTIONS] URL


  • mlrun watch-stream v3io:///users/my-test-stream

  • mlrun watch-stream v3io:///users/my-test-stream -s 1

  • mlrun watch-stream v3io:///users/my-test-stream -s 1 -s 2

  • mlrun watch-stream v3io:///users/my-test-stream -s 1 -s 2 --seek EARLIEST



-s, −−shard-ids

Shard id to listen on (can be multiple).

–seek TEXT

Where to start/seek (EARLIEST or LATEST)

-i, −−interval

Interval in seconds. Default = 3

-j, −−is-json

Indicates that the payload is json (will be deserialized).

Building and running a function from a Git repository#

To build and run a function from a Git repository, start out by adding a YAML function-configuration file in your local environment. This file should describe the function and define its specification. For example, create a myfunc.yaml file with the following content in your working directory:

kind: job
  name: remote-demo1
  project: ''
  command: 'examples/'
  args: []
  image: .mlrun/func-default-remote-demo-ps-latest
  image_pull_policy: Always
    base_image: mlrun/mlrun:1.2.0
    source: git://

Then, run the following CLI command and pass the path to your local function-configuration file as an argument to build the function’s container image according to the configured requirements. For example, the following command builds the function using the myfunc.yaml file from the current directory:

mlrun build myfunc.yaml

When the build completes, you can use the run CLI command to run the function. Set the -f option to the path to the local function-configuration file, and pass the relevant parameters. For example:

mlrun run -f myfunc.yaml -w -p p1=3

You can also try the following function-configuration example, which is based on the MLRun CI demo:

kind: job
  name: remote-git-test
  project: default
  tag: latest
  command: ''
  args: []
  image_pull_policy: Always
    commands: []
    base_image: mlrun/mlrun:1.2.0
    source: git://

Using a sources archive#

The -a|--archive option of the CLI build command enables you to define a remote object path for storing TAR archive files with all the required code dependencies. The remote location can be, for example, in an AWS S3 bucket or in a data container in an Iguazio MLOps Platform (“platform”) cluster. Alternatively, you can also set the archive path by using the MLRUN_DEFAULT_ARCHIVE environment variable. When an archive path is provided, the remote builder archives the configured function sources (see the -s|-source build option) into a TAR archive file, and then extracts (untars) all of the archive files (i.e., the function sources) into the configured archive location.

To use the archive option, first create a local function-configuration file. For example, you can create a function.yaml file in your working directory with the following content; the specification describes the environment to use, defines a Python base image, adds several packages, and defines examples/ as the application to execute on run commands:

kind: job
  name: remote-demo4
  project: ''
  command: 'examples/'
  args: []
  image_pull_policy: Always
    commands: []
    base_image: mlrun/mlrun:1.2.0

Next, run the following MLRun CLI command to build the function; replace the <...> placeholders to match your configuration:

mlrun build <function-configuration file path> -a <archive path/URL> [-s <function-sources path/URL>]

For example, the following command uses the function.yaml configuration file (.), relies on the default function-sources path (./), and sets the target archive path to v3io:///users/$V3IO_USERNAME/tars. So, for a user named “admin”, for example, the function sources from the local working directory will be archived and then extracted into an admin/tars directory in the “users” data container of the configured platform cluster (which is accessed via the v3io data mount):

mlrun build . -a v3io:///users/$V3IO_USERNAME/tars


  • . is a shorthand for a function.yaml configuration file in the local working directory.

  • The -a|--archive option is used to instruct MLRun to create an archive file from the function-code sources at the location specified by the -s|--sources option; the default sources location is the current directory (./).

After the function build completes, you can run the function with some parameters. For example:

mlrun run -f . -w -p p1=3