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docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md
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@@ -5,17 +5,15 @@ title: Hyperparameter Optimization
The [hyper_parameter_optimizer.py](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py)
example script demonstrates hyperparameter optimization, which is automated by using **ClearML**
<a class="tr_top_negative" name="strategy"></a>
## Set the Search Strategy for Optimization
A search strategy is required for the optimization, as well as a search strategy optimizer class to implement that strategy.
The following search strategies can be used:
* Optuna hyperparameter optimization - [automation.optuna.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.bandster.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.
@@ -24,11 +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.optimization.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md)
* Full grid sampling strategy of every hyperparameter combination - Grid search [automation.optimization.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
* Random uniform sampling of hyperparameter strategy - [automation.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md)
* Full grid sampling strategy of every hyperparameter combination - Grid search [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md).
* Custom - Use a custom class and inherit from the **ClearML** automation base strategy class, automation.optimization.SearchStrategy.
The search strategy class that is chosen will be passed to the [automation.optimization.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
@@ -40,14 +38,14 @@ the `RandomSearch` for the search strategy.
if not aSearchStrategy:
try:
from clearml.automation.optuna import OptimizerOptuna
from clearml.optuna import OptimizerOptuna
aSearchStrategy = OptimizerOptuna
except ImportError as ex:
pass
if not aSearchStrategy:
try:
from clearml.automation.hpbandster import OptimizerBOHB
from clearml.automation.hpbandster import OptimizerBOHB
aSearchStrategy = OptimizerBOHB
except ImportError as ex:
pass
@@ -104,7 +102,7 @@ In this example, an experiment named **Keras HP optimization base** is being opt
least once so that it is stored in **ClearML Server**, and, therefore, can be cloned.
Since the arguments dictionary is connected to the Task, after the code runs once, the `template_task_id` can be changed
to optimize a different experiment, see [tuning experiments](../../../webapp/webapp_exp_tuning.md).
to optimize a different experiment.
```python
# experiment template to optimize in the hyper-parameter optimization
@@ -122,7 +120,7 @@ to optimize a different experiment, see [tuning experiments](../../../webapp/web
## Creating the Optimizer Object
Initialize an [automation.optimization.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
@@ -131,8 +129,8 @@ object, setting the optimization parameters, beginning with the ID of the experi
base_task_id=args['template_task_id'],
```
Set the hyperparameter ranges to sample, instantiating them as **ClearML** automation objects using [automation.parameters.UniformIntegerParameterRange](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md)
and [automation.parameters.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=[
@@ -186,8 +184,6 @@ Specify the remaining parameters, including the time limit per Task (minutes), p
```
<a class="tr_top_negative" name="service"></a>
## Running as a Service
The optimization can run as a service, if the `run_as_service` argument is set to `true`. For more information about