(cli)=
# Command-Line Interface
- [CLI commands](#cli-commands)
- [Building and running a function from a Git Repository](#building-and-running-a-function-from-a-git-repository)
- [Using a sources archive](#using-a-sources-archive)
## CLI commands
Use the following commands of the MLRun command-line interface (CLI) — `mlrun` — to build and run MLRun functions:
- [`build`](#cli-cmd-build)
- [`clean`](#cli-cmd-clean)
- [`config`](#cli-cmd-config)
- [`get`](#cli-cmd-get)
- [`logs`](#cli-cmd-logs)
- [`project`](#cli-cmd-project)
- [`run`](#cli-cmd-run)
- [`version`](#cli-cmd-version)
- [`watch`](#cli-cmd-watch)
- [`watch-stream`](#cli-cmd-watch-stream)
Each command supports many flags, some of which are listed in their relevant sections. To view all the flags of a command, run `mlrun --help`.
### `build`
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`
| Flag | Description |
| ------------------------- | --------------------------------------- |
| −−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|--archive` is specified, a directory to archive. (Default: './') |
| -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|--source`) are archived into a TAR file and then extracted into the archive directory |
| −−silent | Do not show build logs |
| −−with-mlrun | 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 |
| −−kfp | Running inside Kubeflow Piplines, do not use |
| −−skip | Skip if already deployed |
> **Note:** For information about using the `-a|--archive` option to create a function-sources archive, see [Using a Sources Archive](#sources-archive) later in this tutorial.
### `clean`
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]
Examples:
- 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`
| Flag | Description |
| ----------------------------- | --------------------------------------- |
| −−kind | Clean resources for all runs of a specific kind (e.g. job). |
| −−id | 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.|
Options| Description |
| ----------- | ----------- |
| −−api | 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. |
### `config`
Use the `config` CLI command to show the mlrun client environment configuration, such as location of artifacts and api.
Example: `mlrun config`
### `get`
Use the `get` CLI command to list one or more objects per kind/class.
Usage: get pods | runs | artifacts | func [name]
Examples:
- `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`
| Flag | Description |
| ----------- | ----------- |
| −−name | Name of object to return |
| -s, −−selector | Label selector |
| -n, −−namespace | Kubernetes namespace |
| −−uid | Object ID |
| −−project | Project name to return |
| -t, −−tag | Artifact/function tag of object to return |
| −−db | db path/url of object to return |
### `logs`
Use the `logs` CLI command to get or watch task logs.
Usage: logs [OPTIONS] uid
Example: `mlrun logs ba409c0cb4904d60aa8f8d1c05b40a75 --project getting-started-admin`
| Flag | Description |
| ------------ | ----------- |
| -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`. |
### `project`
Use the `project` CLI command to load and/or run a project.
Usage: mlrun project [OPTIONS] [CONTEXT]
Example: `mlrun project -r workflow.py .`
| Flag | Description |
| ---------- | ---------- |
| -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 |
| −−init_git | For new projects init git the context dir |
| -c, −−clone | Force override/clone into the context dir |
| −−sync | 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) |
| −−local | Try to run workflow functions locally |
| −−schedule | To create a schedule, define a standard crontab expression string. To use the pre-defined workflow's schedule: `set --schedule 'true'`. [See cron details](https://apscheduler.readthedocs.io/en/3.x/modules/triggers/cron.html#module-apscheduler.triggers.cron). |
### `run`
Use the `run` CLI command to execute a task and inject parameters by using a local or remote function.
Usage: mlrun [OPTIONS] URL [ARGS]...
Examples:
- `mlrun run -f db://getting-started-admin/prep-data --project getting-started-admin`
- `mlrun run -f myfunc.yaml -w -p p1=3`
| Flag | Description |
| ----------- | ----------- |
| -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=` or `env=,...`; 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 |
### `version`
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`
| Flag | Description |
| ----------- | ----------- |
| -n, −−namespace | kubernetes namespace |
| -t, −−timeout | Timeout in seconds |
### `watch-stream`
Use the `watch-stream` CLI command to watch a v3io stream and print data at a recurring interval.
Usage: mlrun watch-stream [OPTIONS] URL
Examples:
- `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`
| Flag | Description |
| ----------- | ----------- |
| -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:
```yaml
kind: job
metadata:
name: remote-demo1
project: ''
spec:
command: 'examples/training.py'
args: []
image: .mlrun/func-default-remote-demo-ps-latest
image_pull_policy: Always
build:
base_image: mlrun/mlrun:1.4.0
source: git://github.com/mlrun/mlrun
```
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:
```sh
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:
```sh
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:
```yaml
kind: job
metadata:
name: remote-git-test
project: default
tag: latest
spec:
command: 'myfunc.py'
args: []
image_pull_policy: Always
build:
commands: []
base_image: mlrun/mlrun:1.4.0
source: git://github.com/mlrun/ci-demo.git
```
## Using a sources archive
The `-a|--archive` option of the CLI [`build`](#cli-cmd-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`](#cli-cmd-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/training.py** as the application to execute on `run` commands:
```yaml
kind: job
metadata:
name: remote-demo4
project: ''
spec:
command: 'examples/training.py'
args: []
image_pull_policy: Always
build:
commands: []
base_image: mlrun/mlrun:1.4.0
```
Next, run the following MLRun CLI command to build the function; replace the `<...>` placeholders to match your configuration:
```sh
mlrun build -a [-s ]
```
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/$V3IO_USERNAME/tars`.
So, for a user named "admin", for example, the function sources from the local working directory will be archived and then extracted into an **admin/tars** directory in the "users" data container of the configured platform cluster (which is accessed via the `v3io` data mount):
```sh
mlrun build . -a v3io:///users/$V3IO_USERNAME/tars
```
> **Note:**
> - `.` 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:
```sh
mlrun run -f . -w -p p1=3
```