---
title: Hyperparameter Optimization
---
## What is HyperParameter Optimization?
Hyperparameters are variables that directly control the behaviors of training algorithms, and have a significant effect on
the performance of the resulting machine learning models. Finding the hyperparameter values that yield the best
performing models can be complicated. Manually adjusting hyperparameters over the course of many training trials can be
slow and tedious. Luckily, you can automate and boost hyperparameter optimization with ClearML's
[**`HyperParameterOptimizer`**](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class.
## ClearML's HyperParameter Optimization
ClearML provides the `HyperParameterOptimizer` class, which takes care of the entire optimization process for users
with a simple interface.
ClearML's approach to hyperparameter optimization is scalable, easy to set up and to manage, and it makes it easy to
compare results.
### Workflow
The diagram above demonstrates the typical flow of hyperparameter optimization where the parameters of a base task are optimized:
1. Configure an Optimization Task with a base task whose parameters will be optimized, and a set of parameter values to
test
1. Clone the base task. Each clone's parameter is overridden with a value from the optimization task
1. Enqueue each clone for execution by a ClearML Agent
1. The Optimization Task records and monitors the cloned tasks' configuration and execution details, and returns a
summary of the optimization results in tabular and parallel coordinate formats, and in a scalar plot.
Parallel coordinate and scalar plots
### Supported Optimizers
The `HyperParameterOptimizer` class contains ClearML’s hyperparameter optimization modules. Its modular design enables
using different optimizers, including existing software frameworks, enabling simple, accurate, and fast hyperparameter
optimization.
* **Optuna** - [`automation.optuna.OptimizerOptuna`](../references/sdk/hpo_optuna_optuna_optimizeroptuna.md). Optuna is the default optimizer in ClearML. It makes use of
different samplers such as grid search, random, bayesian, and evolutionary algorithms.
For more information, see the [Optuna](https://optuna.readthedocs.io/en/latest/)
documentation.
* **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.
For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html)
documentation and a [code example](../guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md).
* **Random** uniform sampling of hyperparameters - [`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** - [`automation.optimization.SearchStrategy`](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L268) - Use a custom class and inherit from the ClearML automation base strategy class
## Defining a Hyperparameter Optimization Search Example
1. Import ClearML's automation modules:
```python
from clearml.automation import UniformParameterRange, UniformIntegerParameterRange
from clearml.automation import HyperParameterOptimizer
from clearml.automation.optuna import OptimizerOptuna
```
1. Initialize the Task, which will be stored in ClearML Server when the code runs. After the code runs at least once,
it can be reproduced, and the parameters can be tuned:
```python
from clearml import Task
task = Task.init(
project_name='Hyper-Parameter Optimization',
task_name='Automatic Hyper-Parameter Optimization',
task_type=Task.TaskTypes.optimizer,
reuse_last_task_id=False
)
```
1. Define the optimization configuration and resources budget:
```python
optimizer = HyperParameterOptimizer(
# specifying the task to be optimized, task must be in system already so it can be cloned
base_task_id=TEMPLATE_TASK_ID,
# setting the hyper-parameters to optimize
hyper_parameters=[
UniformIntegerParameterRange('number_of_epochs', min_value=2, max_value=12, step_size=2),
UniformIntegerParameterRange('batch_size', min_value=2, max_value=16, step_size=2),
UniformParameterRange('dropout', min_value=0, max_value=0.5, step_size=0.05),
UniformParameterRange('base_lr', min_value=0.00025, max_value=0.01, step_size=0.00025),
],
# setting the objective metric we want to maximize/minimize
objective_metric_title='accuracy',
objective_metric_series='total',
objective_metric_sign='max',
# setting optimizer
optimizer_class=OptimizerOptuna,
# configuring optimization parameters
execution_queue='default',
max_number_of_concurrent_tasks=2,
optimization_time_limit=60.,
compute_time_limit=120,
total_max_jobs=20,
min_iteration_per_job=15000,
max_iteration_per_job=150000,
)
```
:::tip Locating Task ID
To locate the base task's ID, go to the task's info panel in the [WebApp](../webapp/webapp_overview.md). In the top of the panel,
to the right of the task name, click `ID` and the task ID appears
:::
For more information about `HyperParameterOptimizer` and supported optimization modules, see the [HyperParameterOptimizer class reference](../references/sdk/hpo_optimization_hyperparameteroptimizer.md).
## Tutorial
Check out the [Hyperparameter Optimization tutorial](../guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md) for a step-by-step guide.