kubeflow/katib

• mediumreadme#1

  Refine README H1 and opening sentence for stronger positioning

  Why:

  CURRENT

  # Kubeflow Katib
  
  Kubeflow Katib is a Kubernetes-native project for automated machine learning (AutoML).

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  # Kubeflow Katib: Kubernetes-Native Hyperparameter Tuning and Neural Architecture Search
  
  Kubeflow Katib is the Kubernetes-native project for automated machine learning (AutoML), specifically designed for scalable Hyperparameter Tuning, Early Stopping, and Neural Architecture Search directly on your Kubernetes clusters.

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• mediumcomparison#2

  Add a 'Comparison with other HPO/NAS tools' section to README

  Why:

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  ## Comparison with other HPO/NAS tools
  
  While many tools offer Hyperparameter Optimization (HPO) and Neural Architecture Search (NAS), Katib's core differentiator is its deep integration with Kubernetes. Unlike general-purpose libraries or frameworks, Katib leverages Kubernetes Custom Resources and controllers to manage experiments, trials, and metrics natively on your cluster, providing robust scalability, resilience, and seamless integration with the Kubeflow ecosystem.

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• lowexamples#3

  Add a concise quickstart example to the README

  Why:

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  ## Quickstart Example
  
  To run a simple hyperparameter tuning experiment with Katib, apply the following YAML:
  
  ```yaml
  apiVersion: "kubeflow.org/v1beta1"
  kind: Experiment
  metadata:
    name: example-hpo
  spec:
    objective:
      type: maximize
      goal: 0.99
      objectiveMetricName: accuracy
    algorithm:
      algorithmName: random
    parameters:
      - name: lr
        parameterType: double
        feasibleSpace:
          min: "0.01"
          max: "0.05"
      - name: num-layers
        parameterType: int
        feasibleSpace:
          min: "1"
          max: "5"
    trialTemplate:
      primaryContainer: training-container
      trialParameters:
        - name: learningRate
          description: Learning Rate for the model
          reference: lr
        - name: numberLayers
          description: Number of layers for the model
          reference: num-layers
      trialSpec:
        apiVersion: batch/v1
        kind: Job
        spec:
          template:
            spec:
              containers:
                - name: training-container
                  image: docker.io/kubeflow/katib-pytorch-mnist:v1.8.0
                  command:
                    - "python3"
                    - "/app/pytorch-mnist.py"
                    - "--lr=${trialParameters.learningRate}"
                    - "--num-layers=${trialParameters.numberLayers}"
              restartPolicy: Never
  ```

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  What are the best tools for automated hyperparameter tuning on Kubernetes?

  you: #1
  AI recommended (in order):

  1. Kubeflow Katib (kubeflow/katib) ← you
  2. Optuna (optuna/optuna)
  3. Ray Tune (ray-project/ray)
  4. Hyperopt (hyperopt/hyperopt)
  5. SigOpt
  6. Weights & Biases (W&B) Sweeps (wandb/wandb)
  Show full AI answer
• CATEGORY QUERY

  Seeking a Kubernetes-native platform for neural architecture search and model optimization.

  you: #2
  AI recommended (in order):

  1. Kubeflow (kubeflow/kubeflow)
  2. Katib (kubeflow/katib) ← you
  3. Pipelines (kubeflow/pipelines)
  4. KFServing (kserve/kserve)
  5. Argo Workflows (argoproj/argo-workflows)
  6. NNI (Neural Network Intelligence) (microsoft/nni)
  7. Ray Tune (ray-project/ray)
  8. KubeRay (ray-project/kuberay)
  9. Polyaxon (polyaxon/polyaxon)
  10. Pytorch Lightning (Lightning-AI/lightning)
  11. Hydra (facebookresearch/hydra)
  Show full AI answer

• Metadata completeness

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• README presence

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• Compared to common alternatives in this category, what is the core differentiator of kubeflow/katib?

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• If a team adopts kubeflow/katib in production, what risks or prerequisites should they evaluate first?

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• In one sentence, what problem does the repo kubeflow/katib solve, and who is the primary audience?

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