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pollfly
2024-03-07 15:14:11 +02:00
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docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md
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@@ -11,9 +11,9 @@ A search strategy is required for the optimization, as well as a search strategy
The following search strategies can be used:
* Optuna hyperparameter optimization - [automation.optuna.OptimizerOptuna](../../../references/sdk/hpo_optuna_optuna_optimizeroptuna.md).
* Optuna hyperparameter optimization - [`automation.optuna.OptimizerOptuna`](../../../references/sdk/hpo_optuna_optuna_optimizeroptuna.md).
For more information about Optuna, see the [Optuna](https://optuna.org/) documentation.
* BOHB - [automation.hpbandster.OptimizerBOHB](../../../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md).
* BOHB - [`automation.hpbandster.OptimizerBOHB`](../../../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md).
BOHB performs robust and efficient hyperparameter optimization at scale by combining the speed of Hyperband searches
with the guidance and guarantees of convergence of Bayesian Optimization.
@@ -22,11 +22,11 @@ The following search strategies can be used:
For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html)
documentation.
* Random uniform sampling of hyperparameter strategy - [automation.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md)
* Full grid sampling strategy of every hyperparameter combination - [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
* Custom - Use a custom class and inherit from the ClearML automation base strategy class, [SearchStrategy](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L310)
* Random uniform sampling of hyperparameter strategy - [`automation.RandomSearch`](../../../references/sdk/hpo_optimization_randomsearch.md)
* Full grid sampling strategy of every hyperparameter combination - [`automation.GridSearch`](../../../references/sdk/hpo_optimization_gridsearch.md).
* Custom - Use a custom class and inherit from the ClearML automation base strategy class, [`SearchStrategy`](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L310)
The search strategy class that is chosen will be passed to the [automation.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md)
The search strategy class that is chosen will be passed to the [`automation.HyperParameterOptimizer`](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md)
object later.
The example code attempts to import `OptimizerOptuna` for the search strategy. If `clearml.automation.optuna` is not
@@ -113,7 +113,7 @@ if not args['template_task_id']:
## Creating the Optimizer Object
Initialize an [automation.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md)
Initialize an [`automation.HyperParameterOptimizer`](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md)
object, setting the optimization parameters, beginning with the ID of the experiment to optimize.
```python
@@ -122,8 +122,8 @@ an_optimizer = HyperParameterOptimizer(
base_task_id=args['template_task_id'],
```
Set the hyperparameter ranges to sample, instantiating them as ClearML automation objects using [automation.UniformIntegerParameterRange](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md)
and [automation.DiscreteParameterRange](../../../references/sdk/hpo_parameters_discreteparameterrange.md).
Set the hyperparameter ranges to sample, instantiating them as ClearML automation objects using [`automation.UniformIntegerParameterRange`](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md)
and [`automation.DiscreteParameterRange`](../../../references/sdk/hpo_parameters_discreteparameterrange.md).
```python
hyper_parameters=[