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fix: bump mlflow-kubernetes-plugins to 1.3.0 for MLflow 3.13 auth coverage #315
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Jun 19, 2026
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🧩 Analysis chain
🌐 Web query:
What are the changes in mlflow-kubernetes-plugins version 1.3.0 compared to 1.2.1?💡 Result:
The mlflow-kubernetes-plugins package version 1.3.0, released on June 12, 2026 [1][2], is part of the Kubeflow mlflow-integration repository [2]. While official release notes detailing specific code changes between 1.2.1 and 1.3.0 are not explicitly indexed in public summaries, the package maintains its core functionality as a Kubernetes-backed WorkspaceProvider and an optional Kubernetes RBAC authorization plugin [3][2]. Key information regarding this release: - Repository Migration: The project is now hosted under the kubeflow/mlflow-integration repository [2], having moved from its previous location at opendatahub-io/mlflow-kubernetes-plugins [4]. - Versioning: Version 1.3.0 was released shortly after 1.2.1 (which was released on May 8, 2026) [1][5]. - Dependencies: Version 1.3.0 requires MLflow <4.0.0, >=3.10.0, Kubernetes >=29.0.0, and graphql-core >=3.2.0 [3]. It requires Python >=3.12 [3]. - Scope: The plugin continues to provide workspace mapping to Kubernetes namespaces and Kubernetes-based request authorization [3][2]. For a complete list of commits or granular code changes between versions 1.2.1 and 1.3.0, please consult the commit history directly on the kubeflow/mlflow-integration GitHub repository [2].
Citations:
🏁 Script executed:
Repository: opendatahub-io/mlflow
Length of output: 264
🏁 Script executed:
Repository: opendatahub-io/mlflow
Length of output: 2729
🌐 Web query:
mlflow-kubernetes-plugins 1.3.0 release notes changelog kubeflow mlflow-integration💡 Result:
Not enough source evidence was retrieved in this run to provide the actual “release notes/changelog” contents for mlflow-kubernetes-plugins version 1.3.0. What I could verify from authoritative sources - The package exists and is described as providing a Kubernetes-backed workspace provider plus an optional Kubernetes authorization plugin for MLflow workspaces. [1] - In the Kubeflow integration docs, the entry points are: - kubernetes -> MLflow hook mlflow.workspace_provider [2] - kubernetes-auth -> MLflow hook mlflow.app [3] - mlflow-kubernetes-plugins 1.3.0 has dependency constraints for MLflow (>=3.10.0, <4.0.0). [1] What is missing (for 1.3.0 specifically) - I did not find any retrieved page (PyPI, the integration repo, or GitHub release pages indexed in the search results) that contains the text of the 1.3.0 “release notes” or a “what’s changed” changelog section for that exact version. [1] If you want, I can do a second targeted search round specifically for the GitHub release tag “v1.3.0” and/or a compare link “v1.2.x...v1.3.0” to extract the exact changelog entries.
Citations:
🏁 Script executed:
Repository: opendatahub-io/mlflow
Length of output: 47
🌐 Web query:
site:github.com kubeflow/mlflow-integration v1.3.0 release changelog💡 Result:
The kubeflow/mlflow-integration repository currently lists version v1.3.0 as its latest release, published on June 12, 2026 [1]. There is no publicly available changelog text specifically attached to the v1.3.0 release tag in the repository's release section [1]. It is important to note that the Kubeflow ecosystem previously released a major version labeled v1.3.0 in April 2021 [2]; however, this is distinct from the versioning used by the kubeflow/mlflow-integration repository, which was created in March 2026 [1]. Users should not confuse the repository's v1.3.0 release with the historical Kubeflow 1.3.0 platform release [2][3]. For details on the latest changes, users may refer to the repository's commit history or pull request logs, as the official release page for v1.3.0 does not contain descriptive release notes [1].
Citations:
🌐 Web query:
mlflow-kubernetes-plugins 1.3.0 transitive dependencies kubernetes graphql-core requirements💡 Result:
The mlflow-kubernetes-plugins version 1.3.0 package requires the following primary dependencies [1]: - mlflow: >=3.10.0 and <4.0.0 [1] - kubernetes: >=29.0.0 [1] - graphql-core: >=3.2.0 [1] The dependency on graphql-core is listed as a direct dependency of the package [1]. Transitive dependencies are the dependencies of these primary packages, which will vary based on the specific versions of mlflow, kubernetes, and graphql-core installed in your Python environment [1].
Citations:
MLflow 3.13 compatibility is satisfied; shift focus to test validation evidence.
The version constraint for mlflow-kubernetes-plugins 1.3.0 explicitly requires MLflow >=3.10.0, <4.0.0—MLflow 3.13 falls within this range. No compatibility risk exists on that axis.
However, dependency bumps remain a supply-chain surface (CWE-1104: Use of Unmaintained Third Party Components). The valid concern is test validation:
Provide:
Note: GitHub release page for v1.3.0 contains no descriptive release notes; consult commit history if detailed changelog is required.
🤖 Prompt for AI Agents