Improved tagging

[bcdurak]

Describe changes

With this PR, we improve how we handle tags in ZenML.

New Feature: Rolling tags

Our users can now use rolling tags. These tags have special rules regarding how they can be attached to various models. A rolling tag can be associated with only one:

• pipeline run per pipeline
• artifact version per artifact
• run template within a project

You can create and use these tags in various ways:

Recommended approach: the new Tag object

For instance, if you want to add a tag to your pipeline run, you can use the new Tag object:

@pipeline(tags=["not_a_rolling_tag", Tag("a_rolling_tag", rolling=True)])
def my_pipeline():
    ...

Alternative methods

You can also create a tag as a rolling tag and use it down the line. Or, update it later on.

# 1. Create a rolling tag and then use it
from zenml.client import Client
Client().create_tag("a_rolling_tag"], rolling=True)

@pipeline(tags=["rolling_tag"])
def my_pipeline():
    ...

# 2. Update an existing tag
@pipeline(tags=["existing_tag"])
def my_pipeline():
    ...

# This only works if the singleton tag is used once or less
from zenml.client import Client
Client().update_tag(tag_name_or_id="existing_tag", rolling=True)

We, in fact, store the information regarding whether a tag is rolling in our DB.

New Feature: Runtime configuration for hierarchical tags

With this PR, we also enable the hierarchical association of tags on the pipeline run level. In other words, if someone explicitly defines it, a tag on the pipeline decorator can be associated with all the artifact versions that are created during its execution.

@pipeline(tags=["normal_tag", Tag("hierarchical_tag", hierarchical=True)])
def my_pipeline():
    ...

Keep in mind, that this is different than the rolling attribute and it is a one-time runtime setting. We do not save whether a tag is hierarchical or not in the DB.

New Feature: Filtering by multiple tags

With this PR, we add the ability to filter taggable objects with more than one tag.

# Let's say I have an object, which has the following tags: "One", "Two", "Three"

my_filter_1 = TaggableFilter(
    tags=["contains:wo", "startswith:Thr", "equals:Three"]
) #  -> Doesn't return

my_filter_2 = TaggableFilter(
    tags=["contains:wo", "startswith:Thr", "equals:Four"]
) #  -> Returns

Keep in mind, that this is a generalized change and affects all the taggable entities, namely pipelines, pipeline runs, run templates, artifacts, artifact versions, models, and model versions.

New Feature: Utility functions to handle tags

There are new utility functions to manage tags: add_tags and remove_tags. Much like the log_metadatafunctions, these functions can be used in and from different contexts.

# Automatic tagging to a pipeline run (within a step)
add_tags(tags=[...])
                
# Manual tagging to a pipeline run
add_tags(tags=[...], run=...)

# Manual tagging to a pipeline
add_tags(tags=[...], pipeline=...)

# Manual tagging to a run template
add_tags(tags=[...], run_template=...)

# Automatic tagging to a model (within a step)
add_tags(tags=[...], infer_model=True)

# Manual tagging to a model
add_tags(tags=[...], model_name=..., model_version=...)
add_tags(tags=[...], model_version_id=...)

# Automatic tagging to an artifact (within a step)
add_tags(tags=[...], infer_artifact=True)  # step with single output
add_tags(tags=[...], artifact_name=..., infer_artifact=True)  # specific output of a step

# Manual tagging to an artifact
add_tags(tags=[...], artifact_name=..., artifact_version=...)
add_tags(tags=[...], artifact_version_id=...)

The same methodology applies to the remove_tags functions as well.

New Feature: Batch endpoints for tag management

There are two new endpoints for managing the creation and deletion of tags in a batched manner.

Improvement: Refactored client methods

Our previous client methods required our users to use the corresponding Request, Update, and Filter models. This has been changed to keep them consistent with the other entities and make the UX smoother.

# Old version
def create_tag(
    self,
    tag: TagRequest,
) -> TagResponse:

# New version
def create_tag(
    self,
    name: str,
    singleton: Optional[bool] = False,
    color: Optional[ColorVariants] = None,
) -> TagResponse:

Remaining TODOs

• Docs
• Tests
• Fix the deletion endpoints

Pre-requisites

Please ensure you have done the following:

• I have read the CONTRIBUTING.md document.
• I have added tests to cover my changes.
• I have based my new branch on develop and the open PR is targeting develop. If your branch wasn't based on develop read Contribution guide on rebasing branch to develop.
• IMPORTANT: I made sure that my changes are reflected properly in the following resources:
  • ZenML Docs
  • Dashboard: Needs to be communicated to the frontend team.
  • Templates: Might need adjustments (that are not reflected in the template tests) in case of non-breaking changes and deprecations.
  • Projects: Depending on the version dependencies, different projects might get affected.

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Other (add details above)

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