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Add Getting Started admonitions (#648)
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@ -3,6 +3,11 @@ title: Keras Tuner
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displayed_sidebar: mainSidebar
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---
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:::tip
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If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
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instructions.
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:::
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Integrate ClearML into code that uses [Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner). By
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specifying `ClearMLTunerLogger` (see [kerastuner.py](https://github.com/allegroai/clearml/blob/master/clearml/external/kerastuner.py))
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as the Keras Tuner logger, ClearML automatically logs scalars and hyperparameter optimization.
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@ -2,6 +2,11 @@
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title: Click
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---
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:::tip
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If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
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instructions.
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:::
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[`click`](https://click.palletsprojects.com) is a python package for creating command-line interfaces. ClearML integrates
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seamlessly with `click` and automatically logs its command-line parameters.
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@ -2,6 +2,11 @@
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title: Hydra
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---
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:::tip
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If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
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instructions.
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:::
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[Hydra](https://github.com/facebookresearch/hydra) is a Python framework for managing experiment parameters. ClearML integrates seamlessly
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with Hydra and automatically logs the `OmegaConf` which holds all the configuration files, as well as
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@ -2,6 +2,11 @@
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title: PyTorch Ignite
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---
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:::tip
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If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
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instructions.
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:::
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[PyTorch Ignite](https://pytorch.org/ignite/index.html) is a library for training and evaluating neural networks in
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PyTorch. You can integrate ClearML into your code using Ignite’s built-in loggers: [TensorboardLogger](#tensorboardlogger)
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and [ClearMLLogger](#clearmllogger).
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@ -2,6 +2,11 @@
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title: OpenMMLab
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---
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:::tip
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If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
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instructions.
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:::
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[OpenMMLab](https://github.com/open-mmlab) is a computer vision framework. You can integrate ClearML into your
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code using the `mmcv` package's [`ClearMLLoggerHook`](https://mmcv.readthedocs.io/en/master/_modules/mmcv/runner/hooks/logger/clearml.html)
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class. This class is used to create a ClearML Task and to automatically log metrics.
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@ -2,6 +2,11 @@
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title: Seaborn
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---
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:::tip
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If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup
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instructions.
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:::
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[seaborn](https://seaborn.pydata.org/) is a Python library for data visualization.
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ClearML automatically captures plots created using `seaborn`. All you have to do is add two
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lines of code to your script:
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