+
+- Project-specific mandatory tags apply only to a single project.
+ They are configured by an administrator in the `Cluster settings` > `Projects` section, by selecting the project.
+
+ 
+
+For each mandatory tag you select the artifact types it applies to.
+A tag schema can be mandatory for any combination of feature groups, feature views, training datasets, models and deployments.
+For example, a `data_owner` schema can be marked mandatory for models and deployments only, leaving feature groups, feature views and training datasets unaffected.
+
+## Enforcement per artifact type
+
+All five artifact types, feature groups, feature views, training datasets, models and deployments, enforce mandatory tags the same way.
+
+The create request is validated against the configured mandatory tags.
+If any mandatory tag is missing from the tags provided at creation, the artifact is not created and the request is rejected with an HTTP 400 error that lists the missing tag names.
+The bulk tag endpoint applies the same validation.
+
+An artifact created before a tag was marked mandatory stays valid and is not deleted.
+The missing tag is surfaced on read through the `missing_mandatory_tags` property, described in [Missing mandatory tags on pre-existing artifacts](#missing-mandatory-tags-on-pre-existing-artifacts), and on the artifact page in the UI.
+
+## Attach mandatory tags at creation
+
+Pass the mandatory tag values in the `tags` argument of the create call so the create request carries them and passes validation.
+The `tags` argument takes the same shape as feature group tags: a `{"name": ..., "value": ...}` dictionary, a list of such dictionaries, or `Tag` objects, where the value is a single primitive or a dictionary matching the tag schema.
+
+=== "Feature Group (Python)"
+
+ ```python
+ fs = project.get_feature_store()
+
+ # data_owner is mandatory for feature groups; pass it at creation
+ fg = fs.create_feature_group(
+ name="transactions",
+ version=1,
+ primary_key=["id"],
+ tags=[{"name": "data_owner", "value": "email@hopsworks.ai"}],
+ )
+ fg.insert(df)
+ ```
+
+=== "Feature View (Python)"
+
+ ```python
+ fs = project.get_feature_store()
+ fg = fs.get_feature_group("transactions", version=1)
+
+ # data_owner is mandatory for feature views; pass it at creation
+ fv = fs.create_feature_view(
+ name="transactions_fv",
+ version=1,
+ query=fg.select_all(),
+ tags=[{"name": "data_owner", "value": "email@hopsworks.ai"}],
+ )
+ ```
+
+=== "Training Dataset (Python)"
+
+ ```python
+ fs = project.get_feature_store()
+ fv = fs.get_feature_view("transactions_fv", version=1)
+
+ # data_owner is mandatory for training datasets; pass it at creation
+ td_version, td_job = fv.create_training_data(
+ tags=[{"name": "data_owner", "value": "email@hopsworks.ai"}],
+ )
+ ```
+
+=== "Model (Python)"
+
+ ```python
+ mr = project.get_model_registry()
+
+ # data_owner is mandatory for models; pass it at creation
+ model = mr.python.create_model(
+ name="fraud_model",
+ metrics={"accuracy": 0.94},
+ tags=[{"name": "data_owner", "value": "email@hopsworks.ai"}],
+ )
+ model.save("/path/to/model_artifacts")
+
+ # Deploy the model; pass the mandatory deployment tag on the deploy call
+ deployment = model.deploy(
+ name="fraudmodeldeployment",
+ tags=[{"name": "data_owner", "value": "email@hopsworks.ai"}],
+ )
+ ```
+
+=== "Deployment (Python)"
+
+ ```python
+ ms = project.get_model_serving()
+
+ # Build a predictor for an already-saved model
+ predictor = ms.create_predictor(model)
+
+ # data_owner is mandatory for deployments; pass it at creation
+ deployment = ms.create_deployment(
+ predictor,
+ name="fraudmodeldeployment",
+ tags=[{"name": "data_owner", "value": "email@hopsworks.ai"}],
+ )
+ deployment.save()
+ ```
+
+Omitting a mandatory tag from the `tags` argument rejects the create request with an HTTP 400 error that lists the missing tag names.
+
+## Missing mandatory tags on pre-existing artifacts
+
+Marking a tag mandatory does not retroactively reject artifacts that already exist without it.
+An artifact created before the tag became mandatory stays valid, and Hopsworks surfaces the gap on read rather than deleting the artifact.
+
+Fetching such an artifact emits a Python `UserWarning` listing the missing tag names.
+The same list is available on the fetched object through the `missing_mandatory_tags` property and is shown on the artifact page in the UI.
+The property is populated when the object is fetched from the backend and reflects the state at fetch time.
+Adding a tag does not update the property on the object in hand, so fetch the artifact again to see the updated list.
+
+=== "Model (Python)"
+
+ ```python
+ mr = project.get_model_registry()
+
+ # If data_owner is mandatory but was never set on this model,
+ # the fetch emits: UserWarning: Missing mandatory tags: ['data_owner']
+ model = mr.get_model("fraud_model", version=1)
+
+ # Names of mandatory tags that are required for this model but not yet set
+ missing = [tag["name"] for tag in model.missing_mandatory_tags]
+
+ # Set the missing tag
+ if "data_owner" in missing:
+ model.add_tag("data_owner", "email@hopsworks.ai")
+
+ # missing_mandatory_tags reflects the state at fetch time,
+ # so fetch the model again to see the updated list (no warning now)
+ model = mr.get_model("fraud_model", version=1)
+ print(model.missing_mandatory_tags) # []
+ ```
+
+=== "Deployment (Python)"
+
+ ```python
+ ms = project.get_model_serving()
+
+ # If data_owner is mandatory but was never set on this deployment,
+ # the fetch emits: UserWarning: Missing mandatory tags: ['data_owner']
+ deployment = ms.get_deployment("fraudmodeldeployment")
+
+ missing = [tag["name"] for tag in deployment.missing_mandatory_tags]
+
+ # Set the missing tag
+ if "data_owner" in missing:
+ deployment.add_tag("data_owner", "email@hopsworks.ai")
+
+ # Fetch the deployment again to see the updated list (no warning now)
+ deployment = ms.get_deployment("fraudmodeldeployment")
+ print(deployment.missing_mandatory_tags) # []
+ ```
+
+After the tag is set, it no longer appears in `missing_mandatory_tags` on the next fetch, and the fetch no longer warns.
diff --git a/docs/user_guides/fs/tags/tags.md b/docs/user_guides/fs/tags/tags.md
index f51ccceee4..6fc582829c 100644
--- a/docs/user_guides/fs/tags/tags.md
+++ b/docs/user_guides/fs/tags/tags.md
@@ -2,7 +2,7 @@
## Introduction
-Hopsworks feature store enables users to attach tags to artifacts, such as feature groups, feature views or training datasets.
+Hopsworks feature store enables users to attach tags to artifacts, such as feature groups, feature views, training datasets, models or deployments.
A tag is a `{key: value}` pair which provides additional information about the data managed by Hopsworks.
Tags allow you to design custom metadata for your artifacts.
@@ -73,7 +73,7 @@ Where the type is a valid primitive type: `string`, `boolean`, `integer`, `numbe
## Step 2: Attach a tag to an artifact
-Once the tag schema has been created, you can attach a tag with that schema to a feature group, feature view or training datasets either using the feature store APIs, or by using the UI.
+Once the tag schema has been created, you can attach a tag with that schema to a feature group, feature view, training dataset, model or deployment either using the APIs, or by using the UI.
### Using the API
@@ -120,7 +120,7 @@ Finally you can remove a tag from a given artifact by calling the `delete_tag()`
fg.delete_tag("data_privacy")
```
-The same APIs work for feature views and training dataset alike.
+The same APIs work for feature views, training datasets, models and deployments alike.
### Using the UI
@@ -139,6 +139,7 @@ From there you can select the tag schema of the tag you want to attach and popul
Hopsworks indexes the tags attached to feature groups, feature views and training datasets.
The tags will then be searchable using the free text search box located at the top of the UI.
+Tags attached to models and deployments are stored and retrievable through the APIs and the UI, but they are not indexed for free text search.