mlflow-builder: fix OOM failures during build with bigger images

[stefannica]

If the k8s node where the MLFlow builder step is running doesn't
have a lot of memory, the builder step will fail if it has to build
larger images. For example, building the trainer image for the keras
CIFAR10 codeset example resulted in an OOM failure on a node where
only 8GB of memory were available.

This is a known kaniko issue [1] and there's a fix available [2] with
more recent (>=1.7.0) kaniko versions: disabling the compressed
caching via the --compressed-caching command line argument.

This commit models a workflow input parameter mapped to this
new command line argument. To avoid OOM errors with bigger
images, the user may set it in the workflow like so:

  - name: builder
    image: ghcr.io/stefannica/mlflow-builder:latest
    inputs:
      - name: mlflow-codeset
        codeset:
          name: '{{ inputs.mlflow-codeset }}'
          path: /project
      - name: compressed_caching
        # Disable compressed caching to avoid running into OOM errors on cluster nodes with lower memory
        value: false

[1] GoogleContainerTools/kaniko#909
[2] GoogleContainerTools/kaniko#1722