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. They are also published to DockerHub and quay.io.

This release of MLRun supports only Python 3.9.

In this section

Using images#

See Build function image.

MLRun runtime images#

All images are published to DockerHub and quay.io.

The images are:

  • mlrun/mlrun: An MLRun image includes preinstalled OpenMPI and other ML packages. Useful as a base image for simple jobs.

  • mlrun/mlrun-kfp: The same as mlrun/mlrun with the addition of KFP 1.8 already installed. Use it for compiling KFP workflows, and for remote workflows. This image includes KFP.

  • mlrun/mlrun-gpu: The same as mlrun/mlrun but for GPUs, including Open MPI.

  • mlrun/ml-base: Image for file acquisition, compression, Dask jobs, simple training jobs and other utilities.

  • 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 server's backend image.

  • mlrun/log-collector: A service that collects the logs for all runs and stores them in a persistent storage

  • mlrun/mlrun-ui: The server's frontend image

Notes

  • When using the mlrun or mlrun-gpu image, use PyTorch versions up to and including than 2.0.1, but not higher. You can build your own images with newer CUDA for later releases of PyTorch.

  • If you are using a zipped source, use mlrun/mlrun images or install unzip in the provided function base image.

  • When creating an image with MLRun methods using one of the mlrun/mlrun base images, MLRun by default runs pip install mlrun to ensure its dependencies are aligned, along with any other specified Python packages.

When to use an image with KFP#

MLRun supports KFP SDK 1.8. In general, if your workflow will be compiled locally, meaning you are not working with a remote source, then you need to use the image mlrun/mlrun-kfp.

If your workflow will be compiled remotely (engine=remote), then the workflow-runner pod automatically uses the mlrun/mlrun-kfp image, unless you specify otherwise, for example, if you have specific python package requirements.

Unless you are using KFP-specific code inside the MLRun job, you do not need to use the mlrun-kfp image. Generally speaking, all MLRun code works without KFP except, of course, for creating and running pipelines.

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 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-kfp:x.y.z

  • mlrun/mlrun-gpu:x.y.z

  • mlrun/jupyter:x.y.z

  • mlrun/ml-base:x.y.z

You can build specific images. The typical images to build are the common client-side images that you actually use to run your code: mlrun, mlrun-gpu and mlrun-kfp.

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

Building a docker image using a Dockerfile and then using it#

This flow describes how to build the image externally, put it your private repo, and use it in MLRun.

  1. Build an image using Dockerfile:

    1. Create a Dockerfile

    FROM mlrun/mlrun:X.X
RUN pip install package1
RUN pip install package2

    1. Build the image:

    docker build -t your_docker_registry/your_image_name:tag

    1. Push the image:

    docker push your_docker_registry/your_image_name:tag

  2. Create a secret on K8s level for accessing your registry:

    kubectl --namespace default-tenant create secret docker-registry registry-credentials \
    --docker-server your-docker-registry \
    --docker-username <    > \
    --docker-password <    > \
    --docker-email <    >

  3. In the code, use the image you created and provide the secret for pulling it:

    func = prj.set_function(name="func",...)
func.set_image_pull_configuration(image_pull_secret_name="registry-credentials")

    Now when you run the function, the image is used.

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:1.7.0. 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 data node registry. This pulls the image more quickly, and also supports air-gapped sites. When you specify an MLRun image, for example mlrun/mlrun:1.7.0, the actual image used is similar to datanode-registry.iguazio-platform.app.vm/mlrun/mlrun:1.7.0.

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#

Warning

For production, create your own images to ensure that the image is fixed.

  • Pin the image tag, e.g. image="mlrun/mlrun:1.7.0". 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.)