---
title: Manual Random Parameter Search
---

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) 
   so that the parameters are logged by **ClearML**.
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:

* `Keras HP optimization base 0`
* `Keras HP optimization base 1`
* `Keras HP optimization base 2`.

When these experiments are completed, their [results can be compared](../../webapp/webapp_exp_comparing.md).