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35 lines
2.0 KiB
Markdown
35 lines
2.0 KiB
Markdown
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---
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title: Hyperparameter Optimization
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---
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## What is Hyperparameter Optimization?
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Hyperparameters are variables that directly control the behaviors of training algorithms, and have a significant effect on
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the performance of the resulting machine learning models. Hyperparameter optimization (HPO) is crucial for improving
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model performance and generalization.
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Finding the hyperparameter values that yield the best performing models can be complicated. Manually adjusting
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hyperparameters over the course of many training trials can be slow and tedious. Luckily, ClearML offers automated
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solutions to boost hyperparameter optimization efficiency.
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## Workflow
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The preceding diagram demonstrates the typical flow of hyperparameter optimization where the parameters of a base task are optimized:
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1. Configure an Optimization Task with a base task whose parameters will be optimized, optimization targets, and a set of parameter values to
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test
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1. Clone the base task. Each clone's parameter is overridden with a value from the optimization task
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1. Enqueue each clone for execution by a ClearML Agent
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1. The Optimization Task records and monitors the cloned tasks' configuration and execution details, and returns a
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summary of the optimization results.
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## ClearML Solutions
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ClearML offers three solutions for hyperparameter optimization:
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* [GUI application](webapp/applications/apps_hpo.md): The Hyperparameter Optimization app allows you to run and manage the optimization tasks
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directly from the web interface--no code necessary (available under the ClearML Pro plan).
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* [Command-Line Interface (CLI)](apps/clearml_param_search.md): The `clearml-param-search` CLI tool enables you to configure and launch the optimization process from your terminal.
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* [Python Interface](clearml_sdk/hpo_sdk.md): The `HyperParameterOptimizer` class within the ClearML SDK allows you to
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configure and launch optimization tasks, and seamlessly integrate them in your existing model training tasks.
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