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2
docs/guides/ide/integration_pycharm.md
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@ -11,7 +11,7 @@ remote machine. The ClearML PyCharm plugin detects the git details on the local
machine, and passes that information to the remote machine to be registered to a [task](../../fundamentals/task.md).
* Pass user credentials to a remote machine - Multiple users can use the same resource for execution without compromising
private credentials (assuming the entire code base, including `.git` already exists on the remote machine)
private credentials (assuming the entire code base, including `.git` already exists on the remote machine).
* Run the [ClearML Agent](../../clearml_agent.md) on default VMs/Containers.

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docs/integrations/ignite.md
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@ -21,7 +21,7 @@ task = Task.init(task_name="<task_name>", project_name="<project_name>")
This will create a [ClearML Task](../fundamentals/task.md) that captures your script's information, including Git details,
uncommitted code, python environment, all information logged through `TensorboardLogger`, and more.
Visualize all the captured information in the experiment's page in ClearML's [WebApp](#webapp)
Visualize all the captured information in the experiment's page in ClearML's [WebApp](#webapp).
See a code example [here](https://github.com/allegroai/clearml/blob/master/examples/frameworks/ignite/cifar_ignite.py).

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docs/integrations/tensorflow.md
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@ -44,7 +44,7 @@ For example:
```python
auto_connect_frameworks={
'matplotlib': True, 'tensorflow': False, 'tensorboard': False, 'pytorch': True,
'tensorflow': False, 'matplotlib': True, 'tensorboard': False, 'pytorch': True,
'xgboost': False, 'scikit': True, 'fastai': True, 'lightgbm': False,
'hydra': True, 'detect_repository': True, 'tfdefines': True, 'joblib': True,
'megengine': True, 'jsonargparse': True, 'catboost': True

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docs/integrations/xgboost.md
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@ -96,7 +96,7 @@ See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
## Examples
Take a look at ClearML's XGBoost examples. The examples use XGBOost and ClearML in different configurations with
Take a look at ClearML's XGBoost examples. The examples use XGBoost and ClearML in different configurations with
additional tools, like Matplotlib and scikit-learn:
* [XGBoost Metric](../guides/frameworks/xgboost/xgboost_metrics.md) - Demonstrates ClearML automatic logging of XGBoost models and plots
* [XGBoost and scikit-learn](../guides/frameworks/xgboost/xgboost_sample.md) - Demonstrates ClearML automatic logging of XGBoost scalars and models

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sidebars.js
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@ -172,7 +172,7 @@ module.exports = {
{'Scikit-Learn': ['guides/frameworks/scikit-learn/sklearn_joblib_example', 'guides/frameworks/scikit-learn/sklearn_matplotlib_example']},
{'TensorBoardX': ['guides/frameworks/tensorboardx/tensorboardx', "guides/frameworks/tensorboardx/video_tensorboardx"]},
{
'Tensorflow': ['guides/frameworks/tensorflow/tensorboard_pr_curve', 'guides/frameworks/tensorflow/tensorboard_toy',
'TensorFlow': ['guides/frameworks/tensorflow/tensorboard_pr_curve', 'guides/frameworks/tensorflow/tensorboard_toy',
'guides/frameworks/tensorflow/tensorflow_mnist', 'guides/frameworks/tensorflow/integration_keras_tuner']
},
{'XGBoost': ['guides/frameworks/xgboost/xgboost_sample', 'guides/frameworks/xgboost/xgboost_metrics']}