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gsoc: OptimizationJob CRD for Hyperparameter Optimization #4291

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32 changes: 32 additions & 0 deletions content/en/events/upcoming-events/gsoc-2026.md
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Expand Up @@ -104,3 +104,35 @@ To participate in GSoC with Kubeflow, you **must** meet the GSoC [eligibility re

---

## Project 3: OptimizationJob CRD for Hyperparameter Optimization

**Components:** [kubeflow/katib](https://www.github.com/kubeflow/katib), [kubeflow/sdk](https://www.github.com/kubeflow/sdk), [kubeflow/trainer](https://www.github.com/kubeflow/trainer)

**Mentors:** [@akshaychitneni](https://github.com/akshaychitneni), [@andreyvelich](https://github.com/andreyvelich)

**Contributor:**

**Details:**

Hyperparameter optimization (HPO) is critical for maximizing model performance in machine learning workflows. While Katib currently provides HPO capabilities through the `Experiment` CRD, it was designed for broad use cases including Neural Architecture Search (NAS) and arbitrary workloads.

This project aims to design and implement a new **OptimizationJob CRD** (`optimizer.kubeflow.org/v1alpha1`) specifically focused on hyperparameter optimization for TrainJobs. The new CRD will provide:

- **Tighter TrainJob Integration**: Replace unstructured trial specifications with typed TrainJob templates, enabling strong validation
- **Shared Initialization**: Implement a common initializer pattern that runs once and shares model/dataset artifacts across all trials reducing trial startup time and storage costs
- **Simplified API**: Focus exclusively on HPO use cases
- **Modern Metrics Collection**: Support push-based metrics reporting via the Kubeflow SDK
- **SDK Alignment**: Integrate with `OptimizerClient` API from [KEP-46: Hyperparameter Optimization in Kubeflow SDK](https://github.com/kubeflow/sdk/blob/main/docs/proposals/46-hyperparameter-optimization/README.md)

Tracking issue: [kubeflow/katib#2605](https://github.com/kubeflow/katib/issues/2605)

**Difficulty:** Hard

**Size:** 350 hours (Large)

**Skills Required/Preferred:**
* Go
* Python
* Familiarity with Kubernetes controllers, CRDs
* Basic understanding of machine learning training workflows
* Experience with HPO frameworks
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