Manual Random Parameter Search¶
The manual_random_param_search_example.py example scripts demonstrate a random parameter search by automating the execution of an experiment, multiple times, each time with a different set of random hyperparameters. That experiment must run first. It is named
Keras HP optimization base, and is created by running another ClearML example script, base_template_keras_simple.py
This example accomplishes the automated random parameter search by doing the following:
Creating a parameter dictionary, which we connect to the Task by calling Task.connect so that the parameters are logged by ClearML.
Adding the random search hyperparameters and parameters defining the search (e.g., the experiment name, and number of times to run the experiment).
Creating a Task object referencing the experiment.
For each set of parameters:
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 they complete, you can compare the experiment results.