Build function image#

As discussed in Images and their usage in MLRun, MLRun provides pre-built images which contain the components necessary to execute an MLRun runtime. In some cases, however, custom images need to be created. This page details this process and the available options.

When is a build required?#

In many cases an MLRun runtime can be executed without having to build an image. This will be true when the basic MLRun images fulfill all the requirements for the code to execute. It is required to build an image if one of the following is true:

The code uses additional Python packages, OS packages, scripts or other configurations that need to be applied
The code uses different base-images or different versions of MLRun images than provided by default
Executed source code has changed, and the image has the code packaged in it - see here for more details on source code, and using with_code() to avoid re-building the image when the code has changed
The code runs nuclio functions, which are packaged as images (the build is triggered by MLRun and executed by nuclio)

The build process in MLRun is based on Kaniko and automated by MLRun - MLRun generates the dockerfile for the build process, and configures Kaniko with parameters needed for the build.

Building images is done through functions provided by the MlrunProject class. By using project functions, the same process is used to build and deploy a stand-alone function or functions serving as steps in a pipeline.

Automatically building images#

MLRun has the capability to auto-detect when a function image needs to first be built. Following is an example that will require building of the image:

project = mlrun.new_project(project_name, "./proj")

project.set_function(
   "train_code.py", 
   name="trainer",
   kind="job",
   image="mlrun/mlrun",
   handler="train_func",
   requirements=["pandas"]
)

# auto_build will trigger building the image before running, 
# due to the additional requirements.
project.run_function("trainer", auto_build=True)

Using the auto_build option is only suitable when the build configuration does not change between runs of the runtime. For example, if during the development process new requirements were added, the auto_build parameter should not be used, and manual build is needed to re-trigger a build of the image.

In the example above, the requirements parameter was used to specify a list of additional Python packages required by the code. This option directly affects the image build process - each requirement is installed using pip as part of the docker-build process. The requirements parameter can also contain a path to a requirements file, making it easier to reuse an existing configuration rather than specify a list of packages.

Manually building an image#

To manually build an image, use the build_function() function, which provides multiple options that control and configure the build process.

Specifying base image#

To use an existing image as the base image for building the image, set the image name in the base_image parameter. Note that this image serves as the base (dockerfile FROM property), and should not to be confused with the resulting image name, as specified in the image parameter.

project.build_function(
   "trainer",
   base_image="myrepo/my_base_image:latest",
)

Running commands#

To run arbitrary commands during the image build, pass them in the commands parameter of build_function(). For example:

github_repo = "myusername/myrepo.git@mybranch"

project.build_function(
   "trainer",
   base_image="myrepo/base_image:latest",
   commands= [
        "pip install git+https://github.com/" + github_repo,
        "mkdir -p /some/path && chmod 0777 /some/path",    
   ]
)

These commands are added as RUN operations to the dockerfile generating the image.

MLRun package deployment#

The with_mlrun and mlrun_version_specifier parameters allow control over the inclusion of the MLRun package in the build process. Depending on the base-image used for the build, the MLRun package may already be available in which case use with_mlrun=False. If not specified, MLRun will attempt to detect this situation - if the image used is one of the default MLRun images released with MLRun, with_mlrun is automatically set to False. If the code execution requires a different version of MLRun than the one used to deploy the function, set the mlrun_version_specifier to point at the specific version needed. This uses the published MLRun images of the specified version instead. For example:

project.build_function(
   "trainer",
   with_mlrun=True,
   mlrun_version_specifier="1.0.0"
)

Working with code repository#

As the code matures and evolves, the code will usually be stored in a git code repository. When the MLRun project is associated with a git repo (see Create, save, and use projects for details), functions can be added by calling set_function() and setting with_repo=True. This indicates that the code of the function should be retrieved from the project code repository.

In this case, the entire code repository will be retrieved from git as part of the image-building process, and cloned into the built image. This is recommended when the function relies on code spread across multiple files and also is usually preferred for production code, since it means that the code of the function is stable, and further modifications to the code will not cause instability in deployed images.

During the development phase it may be desired to retrieve the code in runtime, rather than re-build the function image every time the code changes. To enable this, use set_source() which gets a path to the source (can be a git repository or a tar or zip file) and set pull_at_runtime=True.

Using a private Docker registry#

By default, images are pushed to the registry configured during MLRun deployment, using the configured registry credentials.

To push resulting images to a different registry, specify the registry URL in the image parameter. If the registry requires credentials, create a k8s secret containing these credentials, and pass its name in the secret_name parameter.

When using ECR as registry, MLRun uses Kaniko’s ECR credentials helper, in which case the secret provided should contain AWS credentials needed to create ECR repositories, as described here. MLRun detects automatically that the registry is an ECR registry based on its URL and configures Kaniko to use the ECR helper. For example:

# AWS credentials stored in a k8s secret -
# kubectl create secret generic ecr-credentials --from-file=<path to .aws/credentials>

project.build_function(
    "trainer",
    image="<aws_account_id>.dkr.ecr.us-east-2.amazonaws.com/myrepo/image:v1",
    secret_name="ecr-credentials",
)

Build environment variables#

It is possible to pass environment variables that will be set in the Kaniko pod that executes the build. This may be useful to pass important information needed for the build process. The variables are passed as a dictionary in the builder_env parameter, for example:

project.build_function(
   ...
   builder_env={"GIT_TOKEN": token},
)

Deploying nuclio functions#

When using nuclio functions, the image build process is done by nuclio as part of the deployment of the function. Most of the configurations mentioned in this page are available for nuclio functions as well. To deploy a nuclio function, use deploy_function() instead of using build_function() and run_function().

Creating default Spark runtime images#

When using Spark to execute code, either using a Spark service (remote-spark) or the Spark operator, an image is required that contains both Spark binaries and dependencies, and MLRun code and dependencies. This image is used in the following scenarios:

For remote-spark, the image is used to run the initial MLRun code which will submit the Spark job using the remote Spark service
For Spark operator, the image is used for both the driver and the executor pods used to execute the Spark job

This image needs to be created any time a new version of Spark or MLRun is being used, to ensure that jobs are executed with the correct versions of both products.

To prepare this image, MLRun provides the following facilities:

# For remote Spark
from mlrun.runtimes import RemoteSparkRuntime
RemoteSparkRuntime.deploy_default_image()

# For Spark operator
from mlrun.runtimes import Spark3Runtime
Spark3Runtime.deploy_default_image()

Build function image

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