Add Getting Started admonitions (#648)

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@ -3,6 +3,11 @@ title: Keras Tuner
displayed_sidebar: mainSidebar
---
:::tip
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
instructions.
:::
Integrate ClearML into code that uses [Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner). By
specifying `ClearMLTunerLogger` (see [kerastuner.py](https://github.com/allegroai/clearml/blob/master/clearml/external/kerastuner.py))
as the Keras Tuner logger, ClearML automatically logs scalars and hyperparameter optimization.

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@ -2,6 +2,11 @@
title: Click
---
:::tip
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
instructions.
:::
[`click`](https://click.palletsprojects.com) is a python package for creating command-line interfaces. ClearML integrates
seamlessly with `click` and automatically logs its command-line parameters.

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@ -2,6 +2,11 @@
title: Hydra
---
:::tip
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
instructions.
:::
[Hydra](https://github.com/facebookresearch/hydra) is a Python framework for managing experiment parameters. ClearML integrates seamlessly
with Hydra and automatically logs the `OmegaConf` which holds all the configuration files, as well as

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@ -2,6 +2,11 @@
title: PyTorch Ignite
---
:::tip
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
instructions.
:::
[PyTorch Ignite](https://pytorch.org/ignite/index.html) is a library for training and evaluating neural networks in
PyTorch. You can integrate ClearML into your code using Ignites built-in loggers: [TensorboardLogger](#tensorboardlogger)
and [ClearMLLogger](#clearmllogger).

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title: OpenMMLab
---
:::tip
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
instructions.
:::
[OpenMMLab](https://github.com/open-mmlab) is a computer vision framework. You can integrate ClearML into your
code using the `mmcv` package's [`ClearMLLoggerHook`](https://mmcv.readthedocs.io/en/master/_modules/mmcv/runner/hooks/logger/clearml.html)
class. This class is used to create a ClearML Task and to automatically log metrics.

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title: Seaborn
---
:::tip
If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
instructions.
:::
[seaborn](https://seaborn.pydata.org/) is a Python library for data visualization.
ClearML automatically captures plots created using `seaborn`. All you have to do is add two
lines of code to your script: