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Add hyperparameter info in fundamentals/task (#375)

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@ -29,7 +29,7 @@ It's possible to copy ([clone](../webapp/webapp_exp_reproducing.md)) a task mult
## Task Sections ## Task Sections
The sections of **ClearML Task** are made up of the information that a task captures and stores, which consists of code The sections of ClearML Task are made up of the information that a task captures and stores, which consists of code
execution details and execution outputs. This information is used for tracking execution details and execution outputs. This information is used for tracking
and visualizing results, reproducing, tuning, and comparing experiments, and executing workflows. and visualizing results, reproducing, tuning, and comparing experiments, and executing workflows.
@ -37,7 +37,7 @@ The captured [code execution information](../webapp/webapp_exp_track_visual.md#e
* Git information * Git information
* Uncommitted code modifications * Uncommitted code modifications
* Python environment * Python environment
* Execution [configuration](../webapp/webapp_exp_track_visual.md#configuration) * [Execution configuration](#execution-configuration) and hyperparameters
The captured [execution output](../webapp/webapp_exp_track_visual.md#experiment-results) includes: The captured [execution output](../webapp/webapp_exp_track_visual.md#experiment-results) includes:
* [Console output](../webapp/webapp_exp_track_visual.md#console) * [Console output](../webapp/webapp_exp_track_visual.md#console)
@ -48,6 +48,21 @@ The captured [execution output](../webapp/webapp_exp_track_visual.md#experiment-
To view a more in depth description of each task section, see [Tracking Experiments and Visualizing Results](../webapp/webapp_exp_track_visual.md). To view a more in depth description of each task section, see [Tracking Experiments and Visualizing Results](../webapp/webapp_exp_track_visual.md).
### Execution Configuration
ClearML logs a task’s hyperparameters specified as command line arguments, environment or code level variables. This
allows experiments to be reproduced, and their hyperparameters and results can be saved and compared, which is key to
understanding model behavior.
Hyperparameters can be added from anywhere in your code, and ClearML provides multiple ways to log them. If you specify
your parameters using popular python packages, such as [argparse](https://docs.python.org/3/library/argparse.html) and
[click](https://click.palletsprojects.com/), all you need to do is [initialize](../references/sdk/task#taskinit) a task, and
ClearML will automatically log the parameters. ClearML also provides methods to explicitly report parameters.
When executing a task through a ClearML agent, the ClearML instrumentation of your code allows for using the ClearML UI
to override the original specified values of your parameters.
See [Hyperparameters](hyperparameters.md) for more information.
### Artifacts ### Artifacts
ClearML allows easy storage of experiments' output products as artifacts that can later be accessed easily and used, ClearML allows easy storage of experiments' output products as artifacts that can later be accessed easily and used,