The [manual_random_param_search_example.py](https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py)
script demonstrates a random parameter search by automating the execution of an experiment multiple times, each time with
a different set of random hyperparameters.
This example accomplishes the automated random parameter search by doing the following:
1. Creating a template Task named `Keras HP optimization base`. To create it, run the [base_template_keras_simple.py](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py)
script. This experiment must be executed first, so it will be stored in the server, and then it can be accessed, cloned,
and modified by another Task.
1. Creating a parameter dictionary, which is connected to the Task by calling [Task.connect](../../references/sdk/task.md#connect)
1. Adding the random search hyperparameters and parameters defining the search (e.g., the experiment name, and number of
times to run the experiment).
1. Creating a Task object referencing the template experiment, `Keras HP optimization base`. See [Task.get_task](../../references/sdk/task.md#taskget_task).
1. For each set of parameters:
1. Cloning the Task object. See [Task.clone](../../references/sdk/task.md#taskclone).
1. Getting the newly cloned Task's parameters. See [Task.get_parameters](../../references/sdk/task.md#get_parameters)
1. Setting the newly cloned Task's parameters to the search values in the parameter dictionary (Step 1). See [Task.set_parameters](../../references/sdk/task.md#set_parameters).
1. Enqueuing the newly cloned Task to execute. See [Task.enqueue](../../references/sdk/task.md#taskenqueue).
When the example script runs, it creates an experiment named `Random Hyper-Parameter Search Example` which is associated
with the `examples` project. This starts the parameter search, and creates the experiments:
