Small edits (#630)

docs/guides/ide/integration_pycharm.md

@@ -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.
 

docs/hyperdatasets/masks.md

@@ -139,7 +139,7 @@ The relevant label is applied to all masks in the version according to the versi
 Frames can contain multiple masks. To add multiple masks, use the SingleFrame’s `masks_source` property. Input one of 
 the following:
 * A dictionary with mask string ID keys and mask URI values
-* A list of mask URIs. Number IDs are automatically assigned to the masks ( "00", "01", etc.)   
+* A list of mask URIs. Number IDs are automatically assigned to the masks ("00", "01", etc.)   
 
 ```python
 frame = SingleFrame(source='https://s3.amazonaws.com/allegro-datasets/cityscapes/leftImg8bit_trainvaltest/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png',)

docs/integrations/ignite.md

@@ -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).
 

docs/integrations/tensorflow.md

@@ -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

docs/integrations/xgboost.md

@@ -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 

docs/webapp/pipelines/webapp_pipeline_table.md

@@ -32,7 +32,7 @@ The models table contains the following columns:
 | Column | Description | Type |
 |---|---|---|
 | **RUN** | Pipeline run identifier  |  String |
-| **VERSION** | The pipeline version number. Corresponds to the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md#class-pipelinecontroller) ’s and [PipelineDecorator](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)’s `version` parameter | Version string |
+| **VERSION** | The pipeline version number. Corresponds to the [PipelineController](../../references/sdk/automation_controller_pipelinecontroller.md#class-pipelinecontroller)’s and [PipelineDecorator](../../references/sdk/automation_controller_pipelinecontroller.md#class-automationcontrollerpipelinedecorator)’s `version` parameter | Version string |
 | **TAGS** | Descriptive, user-defined, color-coded tags assigned to run. | Tag |
 | **STATUS** | Pipeline run's status. See a list of the [task states and state transitions](../../fundamentals/task.md#task-states). For Running, Failed, and Aborted runs, you will also see a progress indicator next to the status. See [here](../../pipelines/pipelines.md#tracking-pipeline-progress). |  String |
 | **USER** | User who created the run.  |  String |

sidebars.js

@@ -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']}