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name: keras-tuner description: "Routes KerasTuner workflows for defining hyperparameter spaces, running search algorithms, tuning image and scikit-learn models, and coordinating distributed chief/worker search state." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

KerasTuner

Use this skill when the task mentions keras_tuner, KerasTuner, HyperParameters, HyperModel, Oracle, Tuner, RandomSearch, GridSearch, Hyperband, BayesianOptimization, SklearnTuner, HyperResNet, HyperXception, HyperEfficientNet, HyperImageAugment, or distributed KERASTUNER_* search state.

What this skill covers

• Defining search spaces with HyperParameters and HyperModel.
• Running the built-in search algorithms and reading back best trials.
• Tuning scikit-learn estimators with SklearnTuner.
• Using the built-in image hypermodels and augmentation search helpers.
• Debugging distributed chief/worker oracle coordination.

Install and quick check

For a normal CPU-backed KerasTuner install:

python -m pip install "keras-tuner[tensorflow-cpu]" tensorboard

For Bayesian optimization or scikit-learn tuning, add the optional backend package set:

python -m pip install "keras-tuner[tensorflow-cpu,bayesian]" tensorboard

If you are using a GPU TensorFlow environment, swap tensorflow-cpu for tensorflow.

For editable development from a source checkout, run this from the checkout root that contains setup.py; the generated skill directory is documentation and is not itself an installable checkout:

python -m pip install -e ".[tensorflow-cpu,bayesian]" tensorboard

Minimal check:

python -c "import keras_tuner; from keras_tuner.backend import config; print(keras_tuner.__version__, config.backend())"

Run scripts/check_env.py when you want a slightly richer import and backend sanity check.

Route map

• Read sub-skills/tuning/SKILL.md for generic hyperparameter search, custom HyperModel classes, Oracle/Tuner subclassing, trial inspection, and reload/resume workflows.
• Read sub-skills/image-hypermodels/SKILL.md for HyperResNet, HyperXception, HyperEfficientNet, and HyperImageAugment.
• Read sub-skills/sklearn-tuning/SKILL.md for SklearnTuner, estimator factories, cross-validation, sample weights, and pickle-backed model saving.
• Read sub-skills/distributed-tuning/SKILL.md for KERASTUNER_ORACLE_IP, KERASTUNER_ORACLE_PORT, KERASTUNER_TUNER_ID, and local chief/worker coordination.

What to read next

• references/api-reference.md for verified signatures and class responsibilities.
• references/workflows.md for end-to-end tuning patterns.
• references/troubleshooting.md for install/import, optional dependency, and search failures.
• references/repo-provenance.md before deciding whether this skill matches the current checkout.

Operational notes

• This package is Python-first; there is no separate CLI to route here.
• Use keras_tuner.backend.config.backend() to check the active backend, not keras_tuner.backend.backend.
• Bayesian optimization and scikit-learn tuning need the optional bayesian dependency set.
• In this 1.4.8 TensorFlow path, Keras model trials also import TensorBoard's HParams API; install tensorboard even when you do not pass a TensorBoard callback.
• The built-in image models may be slow to build; HyperEfficientNet can also trigger a first-run Keras Applications weight download.
• Distributed tuning needs the chief/worker environment variables to be set together. Unless a command says otherwise, run the shell snippets from this skill's root directory (the directory containing this SKILL.md); from another cwd, use the corresponding absolute path.

Fast path

If you only need to confirm the install, backend, and public exports, run:

python scripts/check_env.py

If you need a workflow-specific smoke check, run the helper from the skill root, using its full bundle-relative path, such as python sub-skills/tuning/scripts/smoke_search.py.