(images-usage)=
# Images and their usage in MLRun

Every release of MLRun includes several images for different usages. The build and the infrastructure images are described, and located, in the [README](https://github.com/mlrun/mlrun/blob/development/dockerfiles/README.md). They are also published to [dockerhub](https://hub.docker.com/u/mlrun) and [quay.io](https://quay.io/organization/mlrun).

**In this section**
- [Using images](#using-images)
- [MLRun images](#mlrun-images)
- [Building MLRun images](#building-mlrun-images)
- [MLRun images and external docker images](#mlrun-images-and-external-docker-images)

## Using images

See {ref}`build-function-image`.

## MLRun images  

Every release of MLRun includes several images for different usages. All images are published to 
[dockerhub](https://hub.docker.com/u/mlrun) and [quay.iohttps://quay.io/organization/mlrun](https://quay.io/organization/mlrun]).

The images are:

`mlrun/mlrun`: An MLRun image includes preinstalled OpenMPI. Useful as a base image for simple jobs.
`mlrun/mlrun-gpu`: The same as `mlrun/mlrun` but for GPUs, including Open MPI. 
`mlrun/ml-base`: The image for file acquisition, compression, dask jobs, simple training jobs and other utilities. Like `mlrun/mlrun` with the addition of Miniconda and other python packages.
`mlrun/jupyter`: An image with Jupyter giving a playground to use MLRun in the open source. Built on top of jupyter/scipy-notebook, with the addition of MLRun and several demos and examples.
`mlrun/mlrun-api`: The image used for running the MLRun API.
`mlrun/mlrun-ui`: The image used for running the MLRun UI.

## Building MLRun images

To build all images, run this command from the root directory of the mlrun repository:

`MLRUN_VERSION=X MLRUN_DOCKER_REPO=X MLRUN_DOCKER_REGISTRY=X make docker-images`

Where:
- MLRUN_VERSION this is used as the tag of the image and also as the version injected into the code (e.g. latest or 0.7.0 or 0.6.5-rc6, defaults to unstable)
- MLRUN_DOCKER_REPO is the docker repository (defaults to mlrun)
- MLRUN_DOCKER_REGISTRY is the docker registry (e.g. quay.io/, gcr.io/, defaults to empty (docker hub))

For example, running `MLRUN_VERSION=x.y.z make docker-images` generates these images:
- mlrun/mlrun-api:x.y.z
- mlrun/mlrun:x.y.z
- mlrun/mlrun-gpu:x.y.z
- mlrun/jupyter:x.y.z
- mlrun/ml-base:x.y.z

You can also build only a specific image, for example, `make mlrun` (builds only the api image).

The possible commands are:
- `mlrun`
- `mlrun-gpu`

To run an image locally and explore its contents: `docker run -it <image-name>:<image-tag> /bin/bash`
or to load python (or run a script): `docker run -it <image-name>:<image-tag> python`

## MLRun images and external docker images

There is no difference in the usage between the MLRun images and external docker images. However:
- MLRun images resolve auto tags: If you specify ```image="mlrun/mlrun"``` the API fills in the tag by the client version, e.g. changes it to `mlrun/mlrun:1.5.1`. So, if the client gets upgraded you'll automatically get a new image tag. 
- Where the data node registry exists, MLRun Appends the registry prefix, so the image loads from the datanode registry. This pulls the image more quickly, and also supports air-gapped sites. When you specify an MLRun image, for example `mlrun/mlrun:1.5.1`, the actual image used is similar to `datanode-registry.iguazio-platform.app.vm/mlrun/mlrun:1.5.1`.

These characteristics are great when you’re working in a POC or development environment. But MLRun typically upgrades packages as part of the image, and therefore the default MLRun images can break your product flow. 

### Working with images in production
```{admonition} Warning
For production, **create your own images** to ensure that the image is fixed.
```

- Pin the image tag, e.g. `image="mlrun/mlrun:1.5.1"`. This maintains the image tag at the version you specified, even when the client is upgraded. Otherwise, an upgrade of the client would also upgrade the image. (If you specify an external (not MLRun images) docker image, like python, the result is the docker/k8s default behavior, which defaults to `latest` when the tag is not provided.)
- Pin the versions of requirements, again to avoid breakages, e.g. `pandas==1.4.0`. (If you only specify the package name, e.g. pandas, then pip/conda (python's package managers) just pick up the latest version.)