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32 lines
2.3 KiB
Markdown
32 lines
2.3 KiB
Markdown
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---
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title: Manual Random Parameter Search
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---
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The [manual_random_param_search_example.py](https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py)
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script demonstrates a random parameter search by automating the execution of an experiment multiple times, each time with
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a different set of random hyperparameters.
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This example accomplishes the automated random parameter search by doing the following:
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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)
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script. This experiment must be executed first, so it will be stored in the server, and then it can be accessed, cloned,
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and modified by another Task.
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1. Creating a parameter dictionary, which is connected to the Task by calling [Task.connect](../../references/sdk/task.md#connect)
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so that the parameters are logged by **ClearML**.
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1. Adding the random search hyperparameters and parameters defining the search (e.g., the experiment name, and number of
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times to run the experiment).
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1. Creating a Task object referencing the template experiment, `Keras HP optimization base`. See [Task.get_task](../../references/sdk/task.md#taskget_task).
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1. For each set of parameters:
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1. Cloning the Task object. See [Task.clone](../../references/sdk/task.md#taskclone).
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1. Getting the newly cloned Task's parameters. See [Task.get_parameters](../../references/sdk/task.md#get_parameters)
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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).
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1. Enqueuing the newly cloned Task to execute. See [Task.enqueue](../../references/sdk/task.md#taskenqueue).
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When the example script runs, it creates an experiment named `Random Hyper-Parameter Search Example` which is associated
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with the `examples` project. This starts the parameter search, and creates the experiments:
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* `Keras HP optimization base 0`
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* `Keras HP optimization base 1`
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* `Keras HP optimization base 2`.
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When these experiments are completed, their [results can be compared](../../webapp/webapp_exp_comparing.md).
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