Small edits (#630)

docs/clearml_serving/clearml_serving_cli.md

@@ -5,7 +5,7 @@ title: CLI
 The `clearml-serving` utility is a CLI tool for model deployment and orchestration. 
 
 The following page provides a reference for `clearml-serving`'s CLI commands:
-* [list](#list) -  List running Serving Services
+* [list](#list) - List running Serving Services
 * [create](#create) - Create a new Serving Service
 * [metrics](#metrics) - Configure inference metrics Service
 * [config](#config) - Configure a new Serving Service

docs/cloud_autoscaling/autoscaling_overview.md

@@ -22,7 +22,7 @@ workload grows, not to mention avoiding paying for running machines that aren’
 This is where autoscaling comes into the picture. 
 
 ClearML provides the following options to automate your resource scaling, while optimizing machine usage:
-* [ClearML autoscaler applications](#autoscaler-applications) -  Use the apps to define your compute resource budget, 
+* [ClearML autoscaler applications](#autoscaler-applications) - Use the apps to define your compute resource budget, 
 and have the apps automatically manage your resource consumption as needed–with no code!
 * [Kubernetes integration](#kubernetes) - Deploy agents through Kubernetes, which handles resource management and scaling 
 

docs/getting_started/ds/best_practices.md

@@ -24,7 +24,7 @@ During early stages of model development, while code is still being modified hea
 The abovementioned setups might be folded into each other and that's great! If you have a GPU machine for each researcher, that's awesome! 
 The goal of this phase is to get a code, dataset, and environment setup, so you can start digging to find the best model!
 
--  [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out our [getting started](ds_first_steps.md)). 
+- [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out our [getting started](ds_first_steps.md)). 
   This helps visualizing the results and tracking progress.
 - [ClearML Agent](../../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time, 
   while also creating an easy queue interface that easily lets you just drop your experiments to be executed one by one

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/pipelines/pipelines_sdk_function_decorators.md

@@ -39,7 +39,7 @@ def main(pickle_url, mock_parameter='mock'):
 
 * `name` - The name for the pipeline controller task
 * `project` - The ClearML project where the pipeline controller task is stored
-* `version` -  Numbered version string (e.g. 1.2.3). If `auto_version_bump` is set to `True`, the version number is 
+* `version` - Numbered version string (e.g. 1.2.3). If `auto_version_bump` is set to `True`, the version number is 
   automatically bumped if the same version already exists and the pipeline code has changed
 * `default_queue` - The default [ClearML Queue](../fundamentals/agents_and_queues.md#what-is-a-queue) in which to enqueue all pipeline steps (unless otherwise specified in the pipeline step).
 * `args_map` - Map arguments to their [configuration section](../fundamentals/hyperparameters.md#webapp-interface) in 

docs/pipelines/pipelines_sdk_tasks.md

@@ -15,7 +15,7 @@ pipe = PipelineController(
 
 * `name` - The name for the pipeline controller task
 * `project` - The ClearML project where the pipeline tasks will be created.
-* `version` -  Numbered version string (`e.g. 1.2.3`). When `auto_version_bump` is set to `True`, the version number will 
+* `version` - Numbered version string (`e.g. 1.2.3`). When `auto_version_bump` is set to `True`, the version number will 
   be automatically bumped if the same version already exists and the code has changed
 
 See [PipelineController](../references/sdk/automation_controller_pipelinecontroller.md) for all arguments. 
@@ -159,7 +159,7 @@ pipe.add_function_step(
 * `function_kwargs` (optional) - A dictionary of function arguments and default values which are translated into task 
   hyperparameters. If not provided, all function arguments are translated into hyperparameters.
 * `function_return` - The names for storing the pipeline step’s returned objects as artifacts in its ClearML task.
-* `cache_executed_step` -  If `True`, the controller will check if an identical task with the same code 
+* `cache_executed_step` - If `True`, the controller will check if an identical task with the same code 
   (including setup, see task [Execution](../webapp/webapp_exp_track_visual.md#execution) 
   section) and input arguments was already executed. If found, the cached step's 
   outputs are used instead of launching a new task.

docs/release_notes/ver_1_0.md

@@ -278,7 +278,7 @@ Arguments order changed in `Logger.report_line_plot()`, `Logger.report_plotly()`
 * Fix UI custom columns choice does not persist per project - [ClearML GitHub issue 314](https://github.com/allegroai/clearml/issues/314)
 * Fix API plot_str not returned for compressed plots
 * Fix UI plots color picker consistency
-* Fix API ```Tasks.reset``` marking parent id as  'deleted' in its children
+* Fix API ```Tasks.reset``` marking parent id as 'deleted' in its children
 * Fix UI missing queue selection on queue delete
 * Fix UI debug image history slider not shown when there's only a single iteration
 * Fix UI X-axis labels are being cut in plots - [ClearML GitHub issue 264](https://github.com/allegroai/clearml/issues/264)

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 |

docs/webapp/webapp_profile.md

@@ -241,7 +241,7 @@ The system includes three pre-configured groups that can't be removed:
 * `Users` - All users. Can't be modified
 * `Admins` - Have RW access to all resources (except queue modification), and can grant users / user groups access 
   permissions to workspace resources
-* `Queue admins` -  Can create / delete / rename queues
+* `Queue admins` - Can create / delete / rename queues
 
 The user group table lists all the active user groups. Each row includes a group's name, description, member list, and ID. 
 

docs/webapp/webapp_workers_queues.md

@@ -72,7 +72,7 @@ The worker’s details panel includes the following two tabs:
     * Current Experiment - The experiment currently being executed by the worker
     * Experiment Runtime - How long the currently executing experiment has been running
     * Experiment iteration - The last reported training iteration for the experiment
-* **QUEUES** -  Information about the queues that the worker is assigned to:
+* **QUEUES** - Information about the queues that the worker is assigned to:
     * Queue - The name of the Queue
     * Next experiment - The next experiment available in this queue
     * In Queue - The number of experiments currently enqueued
@@ -117,7 +117,7 @@ Clicking on a queue will open the queue’s details panel and replace the graphs
 The queue’s details panel includes the following two tabs: 
 * **EXPERIMENTS** - A list of experiments in the queue. You can reorder and remove enqueued experiments. See 
   [Controlling Queue Contents](#controlling-queue-contents).
-* **WORKERS**  - Information about the workers assigned to the queue:
+* **WORKERS** - Information about the workers assigned to the queue:
   * Name - Worker name
   * IP - Worker’s IP
   * Currently Executing - The experiment currently being executed by the worker

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']}