Principle:Kubeflow Kubeflow Tune Hyperparameters
| Knowledge Sources | |
|---|---|
| Domains | MLOps, Hyperparameter Optimization, AutoML |
| Last Updated | 2026-02-13 00:00 GMT |
Overview
Tune Hyperparameters is the principle of systematically searching the hyperparameter space of a model training process to identify the configuration that optimizes a target objective metric.
Description
Hyperparameters are configuration values set before training begins that control the learning process itself: learning rate, batch size, number of layers, regularization strength, optimizer choice, and many more. Unlike model parameters (weights), hyperparameters are not learned from data and must be specified by the practitioner or discovered through search.
Manual hyperparameter tuning is time-consuming, error-prone, and does not scale. This principle establishes the practice of automated hyperparameter optimization (HPO), where a search algorithm systematically proposes hyperparameter configurations, evaluates them through training trials, and converges toward the optimal setting according to a defined objective.
Within the Kubeflow ecosystem, Katib provides the HPO platform. Katib supports a range of search algorithms (random search, grid search, Bayesian optimization, Tree-structured Parzen Estimators, CMA-ES, HyperBand, ENAS for neural architecture search), early stopping strategies to terminate unpromising trials, and integration with the Kubeflow Training Operator for executing each trial as a distributed training job. Katib 0.19 (targeted for Kubeflow v1.11) continues to evolve the experiment API and algorithm support.
Usage
Apply this principle when:
- Model performance is sensitive to hyperparameter choices and manual tuning is insufficient.
- The hyperparameter search space has multiple dimensions, making exhaustive manual exploration impractical.
- The team needs to systematically compare dozens or hundreds of training configurations.
- A rigorous, reproducible record of all tried configurations and their results is required.
- Early stopping can provide significant compute savings by terminating poor trials early.
- Neural architecture search is desired to discover optimal network structures automatically.
Theoretical Basis
Automated hyperparameter tuning follows a structured optimization loop:
Step 1: Define the Search Space
- Enumerate the hyperparameters to tune and their valid ranges or value sets.
- For continuous parameters (e.g., learning rate), define min, max, and optional step or distribution.
- For discrete parameters (e.g., number of layers), define the set of allowed values.
- For categorical parameters (e.g., optimizer type), define the list of choices.
Step 2: Select the Search Algorithm
- Choose an algorithm appropriate to the search space and budget.
- Random search is effective for initial broad exploration.
- Bayesian optimization (e.g., TPE, Gaussian Process) is efficient for smaller budgets by modeling the objective function.
- HyperBand combines early stopping with random search for cost-effective multi-fidelity optimization.
- Neural architecture search algorithms (ENAS, DARTS) are appropriate when the architecture itself is a hyperparameter.
Step 3: Define the Objective
- Specify the metric to optimize (e.g., validation accuracy, F1 score, loss).
- Define the optimization direction (maximize or minimize).
- Optionally define early stopping rules based on metric convergence or budget constraints.
Step 4: Execute Trials
- The search algorithm proposes a hyperparameter configuration.
- A training trial is launched with those hyperparameters.
- The trial reports the objective metric upon completion or at intermediate steps.
- The algorithm incorporates the result and proposes the next configuration.
Step 5: Analyze Results
- After all trials complete or the budget is exhausted, rank trials by their objective metric.
- Identify the best hyperparameter configuration.
- Analyze the sensitivity of the objective to each hyperparameter for insight.
- The best configuration is used for the final production training run.