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@ -28,11 +28,22 @@ The diagram above demonstrates the typical flow of hyperparameter optimization w
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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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summary of the optimization results in tabular and parallel coordinate formats, and in a scalar plot.
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![Optimization results summary chart](../img/fundamentals_hpo_summary.png)
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<details className="cml-expansion-panel screenshot">
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<summary className="cml-expansion-panel-summary">Parallel coordinate and scalar plots</summary>
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<div className="cml-expansion-panel-content">
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![Parallel Coordinates](../img/fundamentals_hpo_parallel_coordinates.png)
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![Scalars](../img/fundamentals_hpo_scalars.png)
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</div>
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</details>
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### Supported Optimizers
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The `HyperParameterOptimizer` class contains **ClearML**’s hyperparameter optimization modules. Its modular design enables
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@ -160,6 +160,8 @@ The "name" argument, is the name of the section that the object will go into. If
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See [here](https://github.com/allegroai/clearml/blob/master/examples/reporting/model_config.py) for a detailed example.
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![Configuration objects](../img/fundamentals_hyperparameters_config_objects.png)
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## Manual Parameter Access
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### Manual Parameter Input
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@ -25,6 +25,7 @@ project name & task name combination or by a unique ID.
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It's possible to copy ([clone](../webapp/webapp_exp_reproducing.md)) a task multiple times and to modify it for re-execution.
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![Task](../img/fundamentals_task.png)
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## Task sections
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