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Copy file name to clipboardexpand all lines: modules/config-fields-modelcard-rendering.adoc
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|===
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| Field | Type | Description
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|*FEATURE_UI_MODELCARD* |Boolean | Enables *Modelcard* image tab in UI. Defaults to `true`.
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|*UI_MODELCARD_ARTIFACT_TYPE* | String | Defines the modelcard artifact type.
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|*FEATURE_UI_MODELCARD* |Boolean | Enables *Model Card* image tab in UI. Defaults to `true`.
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|*UI_MODELCARD_ARTIFACT_TYPE* | String | Defines the model card artifact type.
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|*UI_MODELCARD_ANNOTATION* |Object | This optional field defines the layer annotation of the model card stored in an OCI image.
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|*UI_MODELCARD_LAYER_ANNOTATION* |Object | This optional field defines the layer annotation of the model card stored in an OCI image.
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|===
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UI_MODELCARD_LAYER_ANNOTATION: <4>
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org.opencontainers.image.title: README.md
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----
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<1> Enables the ModelCard image tab in the UI.
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<1> Enables the *Model Card* image tab in the UI.
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<2> Defines the model card artifact type. In this example, the artifact type is `application/x-mlmodel`.
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<3> Optional. If an image does not have an `artifactType` defined, this field is checked at the manifest level. If a matching annotation is found, the system then searches for a layer with an annotation matching `UI_MODELCARD_LAYER_ANNOTATION`.
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<4> Optional. If an image has an `artifactType` defined and multiple layers, this field is used to locate the specific layer containing the model card.
Copy file name to clipboardexpand all lines: modules/config-updates-314.adoc
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|===
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| Field | Type | Description
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|*FEATURE_UI_MODELCARD* |Boolean | Enables *Modelcard* image tab in UI. Defaults to `true`.
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|*UI_MODELCARD_ARTIFACT_TYPE* | String | Defines the modelcard artifact type.
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|*FEATURE_UI_MODELCARD* |Boolean | Enables *Model card* image tab in UI. Defaults to `true`.
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|*UI_MODELCARD_ARTIFACT_TYPE* | String | Defines the model card artifact type.
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|*UI_MODELCARD_ANNOTATION* |Object | This optional field defines the layer annotation of the model card stored in an OCI image.
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|*UI_MODELCARD_LAYER_ANNOTATION* |Object | This optional field defines the layer annotation of the model card stored in an OCI image.
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|===
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UI_MODELCARD_LAYER_ANNOTATION: <4>
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org.opencontainers.image.title: README.md
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----
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<1> Enables the ModelCard image tab in the UI.
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<1> Enables the *Model Card* image tab in the UI.
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<2> Defines the model card artifact type. In this example, the artifact type is `application/x-mlmodel`.
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<3> Optional. If an image does not have an `artifactType` defined, this field is checked at the manifest level. If a matching annotation is found, the system then searches for a layer with an annotation matching `UI_MODELCARD_LAYER_ANNOTATION`.
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<4> Optional. If an image has an `artifactType` defined and multiple layers, this field is used to locate the specific layer containing the model card.
In some cases, the `podman` CLI tool is unable to pull certain artifact types, for example, `application/x-mlmodel`. Attempting to use `podman pull` with this artifact type results in the following error:
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[source,terminal]
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----
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Error: parsing image configuration: unsupported image-specific operation on artifact with type "application/x-mlmodel"
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----
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As an alternative, you can use `skopeo copy` to copy an artifact from one location to your {productname} repository.
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.Prerequisites
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* You have installed the `skopeo` CLI.
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* You have logged in to a source registry (in this example, `\registry.redhat.io`) and have a valid authentication file (`~/.docker/config.json`). Alternatively, you can provide credentials by using the `--src-username` and `--src-password` parameters when running a command with the `skopeo` CLI.
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* You have logged in to your {productname} repository.
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.Procedure
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* Use the `skopeo copy` command on an artifact to copy the artifact to your {productname} repository. For example:
<1> Optional. `--dest-tls-verify=false` disables SSL/TLS verification for the destination registry.
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<2> Optional. The `--all` flag optionally copies all image manifests, including multi-architecture images.
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<3> Optional. If you are not logged into a registry, you can pass in the source registry credentials with these parameters.
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<4> Optional. The path to your Docker authentication file. Typically located at `~/.docker/config.json`.
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<5> Your {productname} registry username and password.
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<6> The source image or artifact from the Red{nbsp}Hat container registry. Ensure that you are logged in to the registry and that you can pull the image.
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<7> The URL of your {productname} repository appended with a namespace and the name of the image.
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.Example output
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[source,terminal]
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----
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Getting image source signatures
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Checking if image destination supports signatures
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Copying blob 9538fa2b8ad9 done |
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Copying blob 491ae95f59a2 done |
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Copying blob 01196d075d77 done |
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Copying blob e53a4633c992 done |
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Copying blob c266e9cfa731 done |
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Copying blob dae0e701d9b2 done |
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Copying blob 1e227a2c78d8 done |
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Copying blob 94ff9338861b done |
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Copying blob 2f2bba45146f done |
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Copying blob d3b4df07a0ce done |
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Copying blob f533a8dbb852 done |
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Copying config 44136fa355 done |
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Writing manifest to image destination
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Storing signatures
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----
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.Next steps
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* After you have pushed a machine learning artifact to your {productname} repository, you can link:https://docs.redhat.com/en/documentation/red_hat_quay/{producty}/html-single/use_red_hat_quay/index#viewing-and-modifying-tags[View tag information by using the UI] or link:https://docs.redhat.com/en/documentation/red_hat_quay/{producty}/html-single/use_red_hat_quay/index#viewing-model-card-information[View model card information by using the UI].
There are two ways to create a repository in {quayio}: by pushing an image with the relevant `podman` command, or by using the {quayio} UI. You can also use the UI to delete a repository.
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endif::[]
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ifeval::["{context}" == "use-quay"]
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There are three ways to create a repository in {productname}: by pushing an image with the relevant `podman` command, by using the {productname} UI, or by using the {productname} API. Similarly, repositories can be deleted by using the UI or the proper API endpoint.
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There are multiple ways to create a repository in {productname}. The following options are available depending on your use case:
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* You can push an image with the relevant `podman` or `docker` command.
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* You can use the {productname} UI.
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* You can use the {productname} API.
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* For OCI artifacts, for example, a large-language model (LLM) or machine learning application, you can use `skopeo` or `oras` to copy the artifact to your repository.
Copy file name to clipboardexpand all lines: modules/rn_3_14_0.adoc
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The following updates have been made to {productname}.
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[id="model-card-rendering"]
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=== User-interface modelcard rendering
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=== Model card rendering on the v2 UI
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With the release of {productname} 3.14, modelcard rendering has been introduced to the v2 UI for machine learning models. When a manifest has a certain annotation and a modelcard stored as its layer, a *ModelCard* tab is displayed on the tag's information page. The information provided on the *ModelCard* page provides users with comprehensive insights into each model, and can help enhance a user's understanding of models stored within your registry.
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With the release of {productname} 3.14, the v2 UI now renders model card information for machine learning models that include a model card. When a manifest has a certain annotation (for example, `application/x-mlmodel`) and a model card stored as a layer in the manifest, a *Model Card* tab is displayed on the tag's information page. The information on the *Model Card* page provides users with comprehensive insights into each model, and can help enhance a user's understanding of models stored within their registry.
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[NOTE]
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====
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Modelcard rendering is only available on the {productname} v2 UI.
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The *Model Card* rendering page is only available on the {productname} v2 UI.
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====
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For more information, see. . .
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To view model card information, {productname} users or administrators must push an artifact to a repository. The artifact must have have an accompanying model card. This information renders under *Repository* -> *<tag_name>* -> *Model Card*.
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For more information, see link:https://docs.redhat.com/en/documentation/red_hat_quay/3/html-single/use_red_hat_quay/index#viewing-model-card-information[Viewing model card information by using the v2 UI].
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[id="tag-expiration-enhancement"]
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=== Tag expiration enhancement
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Previously, when configuring tag expiration for {productname}, the `yearly` option was unavailable on the {productname} v2 UI. With this update, users can now configure default tag expiration to occur yearly on the {productname} v2 UI. This can be set by using the {productname} UI or in your `config.yaml` file. For example:
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[source,yaml]
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DEFAULT_TAG_EXPIRATION: 1y
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[id="new-quay-config-fields-314"]
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== {productname} configuration fields updates and changes
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The following configuration fields have been added to {productname} {producty}.
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The following configuration fields have been added to {productname} 3.14.
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[id="model-card-rendering-configuration-field"]
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=== Model card rendering configuration fields
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|*UI_MODELCARD_LAYER_ANNOTATION* |Object | This optional field defines the layer annotation of the model card stored in an OCI image.
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|===
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To enable model card rendering on the {productname} v2 UI, you must set `FEATURE_UI_MODELCARD: true` and `UI_MODELCARD_ARTIFACT_TYPE` in your `config.yaml` file. For example:
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These configuration fields are enabled and set by default in your `config.yaml` file:
For more information, see link:https://docs.redhat.com/en/documentation/red_hat_quay/3/html-single/use_red_hat_quay/index#viewing-model-card-information[Viewing model card information by using the v2 UI].
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[id="ignore-unknown-mediatype-removal"]
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=== IGNORE_UNKNOWN_MEDIATYPES configuration field removal
Copy file name to clipboardexpand all lines: modules/viewing-model-card-information.adoc
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:_content-type: PROCEDURE
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[id="viewing-model-card-information"]
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= Viewing model card information by using the UI
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Model card information can be viewed on the v2 UI. Model cards are essentially markdown (`.md`) files with additional metadata that provide information about a machine learning application. To view model card information, a manifest must have an annotation that is defined in your `config.yaml` file (for example, `application/x-mlmodel`) and include a model card stored as a layer in the manifest. When these conditions are met, a *ModelCard* tab appears on the *Details* page of a tag.
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Model card information can be viewed on the v2 UI. Model cards are essentially markdown (`.md`) files with additional metadata that provide information about a machine learning application. To view model card information, a manifest must have an annotation that is defined in your `config.yaml` file (for example, `application/x-mlmodel`) and include a model card stored as a layer in the manifest. When these conditions are met, a *Model Card* tab appears on the *Details* page of a tag.
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* You have pushed an artifact of that annotation type, and it includes a model card (`.md`) file.
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UI_MODELCARD_LAYER_ANNOTATION: <4>
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org.opencontainers.image.title: README.md
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----
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<1> Enables the ModelCard image tab in the UI.
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<1> Enables the Model Card image tab in the UI.
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<2> Defines the model card artifact type. In this example, the artifact type is `application/x-mlmodel`.
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<3> Optional. If an image does not have an `artifactType` defined, this field is checked at the manifest level. If a matching annotation is found, the system then searches for a layer with an annotation matching `UI_MODELCARD_LAYER_ANNOTATION`.
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<4> Optional. If an image has an `artifactType` defined and multiple layers, this field is used to locate the specific layer containing the model card.
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