(job-function)=
# Function of type `job`

You can deploy a model using a `job` type function, which runs the code in a Kubernetes Pod. 

You can create (register) a `job` function with basic attributes such as code, requirements, image, etc. using the 
{py:meth}`~mlrun.projects.MlrunProject.set_function` method.
You can also import an existing job function/template from the {ref}`load-from-hub`.

Functions can be created from a single code, notebook file, or have access to the entire project context directory. 
(By adding the `with_repo=True` flag, the project context is cloned into the function runtime environment.) 

Examples:


```python
# register a (single) python file as a function
project.set_function(
    "src/data_prep.py",
    name="data-prep",
    image="mlrun/mlrun",
    handler="prep",
    kind="job",
)

# register a notebook file as a function, specify custom image and extra requirements
project.set_function(
    "src/mynb.ipynb",
    name="test-function",
    image="my-org/my-image",
    handler="run_test",
    requirements=["scikit-learn"],
    kind="job",
)

# register a module.handler as a function (requires defining the default sources/work dir, if it's not root)
project.spec.workdir = "src"
project.set_function(
    name="train",
    handler="training.train",
    image="mlrun/mlrun",
    kind="job",
    with_repo=True,
)
```

To run the job:
```
project.run_function("train")
```

**See also**
- [Create and register functions](../runtimes/create-and-use-functions.html)
- [How to annotate notebooks (to be used as functions)](../runtimes/mlrun_code_annotations.html)
- [How to run, build, or deploy functions](../projects/run-build-deploy.html)
- [Using functions in workflows](../projects/build-run-workflows-pipelines.html)