Universal Python SDK to run AI workloads on Kubernetes
C-Rank Grade: B (Solid) - 52/100
External PR Merge Rate: 4%
Response Time: 5h
First Timer Success: -
kubeflow/sdk has a recorded first-timer success rate of 0.0%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in kubeflow/sdk respond to incoming external pull requests in approximately 5.2 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 52.1 places kubeflow/sdk in the B tier.
The external contributor pull request merge rate for kubeflow/sdk is 4.0%, based on public PR activity from non-core contributors.
kubeflow/sdk currently has 11 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Active maintainer team in kubeflow/sdk welcoming community pull requests and bug fixes.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
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Historical health & responsiveness evolution
External PR acceptance
First PR success rate
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90% receive response in
Maintainer review activity heatmap & prime window prediction
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Maintainers are most responsive around 16:00 UTC (16:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 14:00 to 18:00 UTC (14:00 - 18:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (5% approval). Large PRs (>500 lines) take ~25 hours (15% approval). Keep your diff compact for rapid review.
Summary The Spark and Trainer SDKs currently maintain duplicate test utilities, including TestCase, SUCCESS, FAILED, and related helper functionality. To reduce duplication and keep testing utilities consistent across SDK components, these common helpers should be moved to a shared location under kubeflow.common. Motivation Currently both components provide nearly identical implementations: kubeflow.spark.test.common kubeflow.trainer.test.common Maintaining duplicate implementations increases maintenance overhead and can lead to inconsistencies over time. Proposed Changes Create a shared test helper module under kubeflow.common. Move the common testing utilities into the shared module. Update Spark tests to import the shared implementation. Update Trainer tests to import the shared implementation. Remove duplicate implementations where appropriate. Acceptance Criteria Shared test helpers are available under kubeflow.common. Spark and Trainer tests use the shared implementation. Ex
What would you like to be added? The goal of this issue is to improve the Spark documentation by restructuring it into multiple sections, similar to the Trainer and Optimizer documentation. Potential improvements include: Add a Example section. Split the documentation into dedicated guides instead of a single page. Improve the current documentation This issue can be implemented through multiple documentation PRs. Why is this needed? The Spark documentation currently consists of a single navigation entry, making it difficult to discover features and navigate as the SDK grows. Trainer and Optimizer already provide a structured documentation experience with dedicated sections and guides. Aligning the Spark documentation with that structure will improve discoverability, onboarding, and overall consistency across the Kubeflow SDK documentation.
What you would like to be added? The Spark SDK currently defaults the service account for the SparkApplication driver to spark-operator-spark. This is required because the native K8s scheduler in Spark requires that driver pod is attached to a ServiceAccount with pod creation permission (so that the driver can create executors). In turn, the SparkApplication controller requires that the driver spec contain a ServiceAccount with the same permission (which it then passes to Spark via spark-submit). The default spark-operator Helm installation creates a per-namespace ServiceAccount called spark-operator-spark with this permission and all the examples CRs in the repo showcase a driver spec with this ServiceAccount included. An issue has been created in the spark-operator repo to enable a fallback mechanism when a default ServiceAccount is not passed in the driver spec for SparkApplication: kubeflow/spark-operator#3049. Once that PR lands the SDK can remove the hardcoded spark-operator-spa
What you would like to be added? The goal of this issue is to make the Spark E2E tests more robust and align closely with upstream artifacts to enable better detection of drifts. The following is a consolidated list of target end states: Remove the vendored SparkConnect CRD and pull all the latest upstream artifacts to test Spark SDK against the latest version. Remove the default service account from the RBAC. Enable "cluster watcher" support for both SparkConnect and SparkApplication. Why is this needed? Test robustness ensures the Spark package works in realistic production settings. Love this feature? Give it a 👍 We prioritize the features with most 👍
What you would like to be added? The goal of this issue is two-fold. Align test patterns for Spark package to match Trainer package unit tests. Improve coverage of test cases for Spark package unit tests. Why is this needed? Consistency between the SDK components is necessary for maintainability. Love this feature? Give it a 👍 We prioritize the features with most 👍
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When evaluating whether to contribute to kubeflow/sdk, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of kubeflow/sdk acknowledge new external contributions in approximately 5 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 4.0% were successfully merged into the primary branch.
kubeflow/sdk has a recorded first-timer success rate of 0.0%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~5 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 52.1 places kubeflow/sdk in the Solid tier.
The external pull request merge rate is 4.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, kubeflow/sdk currently has 11 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for kubeflow/sdk are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.