Small edits (#701)

docs/clearml_data/clearml_data_sdk.md

@@ -28,8 +28,8 @@ ClearML Data supports multiple ways to create datasets programmatically, which p
   will inherit its data
 * [`Dataset.squash()`](#datasetsquash) - Generate a new dataset from by squashing together a set of related datasets
 
-You can add metadata to your datasets using the `Dataset.set_metadata` method, and access the metadata using the
+You can add metadata to your datasets using [`Dataset.set_metadata()`](../references/sdk/dataset.md#set_metadata), 
-`Dataset.get_metadata` method. See [`set_metadata`](../references/sdk/dataset.md#set_metadata) and [`get_metadata`](../references/sdk/dataset.md#get_metadata).
+and access the metadata using [`Dataset.get_metadata()`](../references/sdk/dataset.md#get_metadata).
 
 ### Dataset.create()
 

docs/clearml_data/data_management_examples/data_man_cifar_classification.md

@@ -102,7 +102,7 @@ hyperparameters. Passing `alias=<dataset_alias_string>` stores the dataset's ID
 `dataset_alias_string` parameter in the experiment's **CONFIGURATION > HYPERPARAMETERS > Datasets** section. This way 
 you can easily track which dataset the task is using. 
 
-The Dataset's [`get_local_copy`](../../references/sdk/dataset.md#get_local_copy) method returns a path to the cached, 
+[`Dataset.get_local_copy`](../../references/sdk/dataset.md#get_local_copy) returns a path to the cached, 
 downloaded dataset. Then the dataset path is input to PyTorch's `datasets` object.
 
 The script then trains a neural network to classify images using the dataset created above.

docs/clearml_serving/clearml_serving_tutorial.md

@@ -241,8 +241,8 @@ You can also specify per-endpoint log frequency with the `clearml-serving` CLI.
 
 See examples of ClearML Serving with other supported frameworks:
 
-* [Scikit-Learn](https://github.com/allegroai/clearml-serving/blob/main/examples/sklearn/readme.md) - random data
+* [scikit-learn](https://github.com/allegroai/clearml-serving/blob/main/examples/sklearn/readme.md) - random data
-* [Scikit-Learn Model Ensemble](https://github.com/allegroai/clearml-serving/blob/main/examples/ensemble/readme.md) - random data
+* [scikit-learn Model Ensemble](https://github.com/allegroai/clearml-serving/blob/main/examples/ensemble/readme.md) - random data
 * [XGBoost](https://github.com/allegroai/clearml-serving/blob/main/examples/xgboost/readme.md) - iris dataset
 * [LightGBM](https://github.com/allegroai/clearml-serving/blob/main/examples/lightgbm/readme.md) - iris dataset
 * [PyTorch](https://github.com/allegroai/clearml-serving/blob/main/examples/pytorch/readme.md) - mnist dataset

docs/faq.md

@@ -128,7 +128,7 @@ When a new ClearML Server version is available, the notification is:
 
 #### How do I find out ClearML version information?   <a id="versions"></a>
 
-ClearML server version information is available in the ClearML webapp Settings page. On the bottom right of the page, 
+ClearML server version information is available in the ClearML WebApp **Settings** page. On the bottom right of the page, 
 it says **Version**, followed by three numbers: the web application version, the API server version, and the API version.
 
 ![Server version information](img/faq_server_versions.png)

docs/getting_started/ds/ds_second_steps.md

@@ -115,7 +115,7 @@ under the "Input Models" section.
 Check out model snapshots examples for [TensorFlow](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py),
 [PyTorch](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_mnist.py),
 [Keras](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py),
-[Scikit-Learn](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py).
+[scikit-learn](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py).
 
 #### Loading Models
 Loading a previously trained model is quite similar to loading artifacts.

docs/guides/frameworks/scikit-learn/sklearn_joblib_example.md

@@ -1,5 +1,5 @@
 ---
-title: Scikit-Learn with Joblib 
+title: scikit-learn with Joblib 
 ---
 
 The [sklearn_joblib_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/scikit-learn/sklearn_joblib_example.py) 

docs/guides/pipeline/pipeline_controller.md

@@ -50,7 +50,7 @@ The sections below describe in more detail what happens in the controller task a
 1. Build the pipeline (see [PipelineController.add_step](../../references/sdk/automation_controller_pipelinecontroller.md#add_step) 
    method for complete reference):
 
-   The pipeline's [first step](#step-1---downloading-the-datae) uses the pre-existing task 
+   The pipeline's [first step](#step-1---downloading-the-data) uses the pre-existing task 
    `pipeline step 1 dataset artifact` in the `examples` project. The step uploads local data and stores it as an artifact.
 
    ```python

docs/guides/reporting/3d_plots_reporting.md

@@ -27,7 +27,7 @@ Logger.current_logger().report_surface(
     zaxis="title Z",
 )
 ```
-Visualize the reported surface plot in **PLOTS**.
+View the reported surface plot in **PLOTS**.
 
 ![Surface plot](../../img/examples_reporting_02.png)
 
@@ -49,5 +49,5 @@ Logger.current_logger().report_scatter3d(
 )
 ```
 
-Visualize the reported 3D scatter plot in **PLOTS**.
+View the reported 3D scatter plot in **PLOTS**.
 ![3d scatter plot](../../img/examples_reporting_01.png)

docs/guides/reporting/clearml_logging_example.md

@@ -17,8 +17,8 @@ In the ``clearml`` GitHub repository, this example includes a clickable icon to
 
 ## Scalars
 
-To reports scalars, call the [Logger.report_scalar](../../references/sdk/logger.md#report_scalar) 
+To reports scalars, call [Logger.report_scalar()](../../references/sdk/logger.md#report_scalar). 
-method. The scalar plots appear in the **web UI** in **SCALARS**.
+The scalar plots appear in the **web UI** in **SCALARS**.
 
 ```python
 # report two scalar series on two different graphs
@@ -44,7 +44,7 @@ Plots appear in **PLOTS**.
 
 ### 2D Plots
 
-Report 2D scatter plots by calling the [Logger.report_scatter2d](../../references/sdk/logger.md#report_scatter2d) method. 
+Report 2D scatter plots by calling [Logger.report_scatter2d()](../../references/sdk/logger.md#report_scatter2d). 
 Use the `mode` parameter to plot data points as markers, or both lines and markers.
 
 ```python
@@ -67,7 +67,7 @@ logger.report_scatter2d(
 
 ### 3D Plots
 
-To plot a series as a 3D scatter plot, use the [Logger.report_scatter3d](../../references/sdk/logger.md#report_scatter3d) method.
+To plot a series as a 3D scatter plot, use [Logger.report_scatter3d()](../../references/sdk/logger.md#report_scatter3d).
 
 ```python
 # report 3d scatter plot
@@ -85,8 +85,7 @@ logger.report_scatter3d(
 
 ![3d scatter plot](../../img/colab_explicit_reporting_05.png)
 
-To plot a series as a surface plot, use the [Logger.report_surface](../../references/sdk/logger.md#report_surface) 
+To plot a series as a surface plot, use [Logger.report_surface()](../../references/sdk/logger.md#report_surface).
-method.
 
 ```python
 # report 3d surface

docs/guides/reporting/model_config.md

@@ -25,8 +25,7 @@ output_model = OutputModel(task=task)
 
 ## Label Enumeration
 
-Set the model's label enumeration using the [`OutputModel.update_labels`](../../references/sdk/model_outputmodel.md#update_labels) 
+Set the model's label enumeration using [`OutputModel.update_labels()`](../../references/sdk/model_outputmodel.md#update_labels).
-method.
 
 ```python
 labels = {"background": 0, "cat": 1, "dog": 2}
@@ -34,8 +33,8 @@ output_model.update_labels(labels)
 ```
 
 ## Registering Models
-Register a previously trained model using the [`OutputModel.update_weights`](../../references/sdk/model_outputmodel.md#update_weights) 
+Register a previously trained model using [`OutputModel.update_weights()`](../../references/sdk/model_outputmodel.md#update_weights). 
-method. The example code uses a model stored in S3.
+The example code uses a model stored in S3.
 
 ```python
 # Manually log a model file, which will have the labels connected above

docs/guides/ui/building_leader_board.md

@@ -51,7 +51,7 @@ The experiments table allows filtering experiments by experiment name, type, and
     * **Aborted** - The experiment ran and was manually or programmatically terminated.
     * **Published** - The experiment is not running, it is preserved as read-only.
 
-## Step 3: Hide the Defaults Column
+## Step 3: Hide the Default Columns
 
 Customize the columns on the tracking leaderboard by hiding any of the default columns shown below.
 

docs/integrations/scikit_learn.md

@@ -1,5 +1,5 @@
 ---
-title: Scikit-Learn
+title: scikit-learn
 ---
 
 :::tip
@@ -7,7 +7,7 @@ If you are not already using ClearML, see [Getting Started](../getting_started/d
 instructions.
 :::
 
-ClearML integrates seamlessly with [Scikit-Learn](https://scikit-learn.org/stable/), automatically logging models created
+ClearML integrates seamlessly with [scikit-learn](https://scikit-learn.org/stable/), automatically logging models created
 with `joblib`.
 
 All you have to do is simply add two lines of code to your scikit-learn script:
@@ -73,8 +73,8 @@ See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
 
 Take a look at ClearML's scikit-learn examples. The examples use scikit-learn and ClearML in different configurations with 
 additional tools, like Matplotlib: 
-* [Scikit-Learn with Joblib](../guides/frameworks/scikit-learn/sklearn_joblib_example.md) - Demonstrates ClearML automatically logging the models created with joblib and a scatter plot created by Matplotlib.
+* [scikit-learn with Joblib](../guides/frameworks/scikit-learn/sklearn_joblib_example.md) - Demonstrates ClearML automatically logging the models created with joblib and a scatter plot created by Matplotlib.
-* [Scikit-Learn with Matplotlib](../guides/frameworks/scikit-learn/sklearn_matplotlib_example.md) - Demonstrates ClearML automatically logging scatter diagrams created with Matplotlib.
+* [scikit-learn with Matplotlib](../guides/frameworks/scikit-learn/sklearn_matplotlib_example.md) - Demonstrates ClearML automatically logging scatter diagrams created with Matplotlib.
 
 
 ## Remote Execution

docs/integrations/transformers.md

@@ -28,7 +28,7 @@ All you have to do is install and set up ClearML:
 That’s it! In every training run from now on, the ClearML experiment 
 manager will capture:
 * Source code and uncommitted changes
-* Hyperparameters - PyTorch trainer [parameters](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer#transformers.TrainingArguments),
+* Hyperparameters - PyTorch trainer [parameters](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/trainer#transformers.TrainingArguments)
 and TensorFlow definitions
 * Installed packages
 * Model files (make sure the `CLEARML_LOG_MODEL` environment variable is set to `True`)

docs/model_registry.md

@@ -35,7 +35,7 @@ continue to train and test new model versions.
 ClearML supports automatic and manual registration of models to the model catalog. 
 
 ### Automatic Logging
-ClearML automatically logs models created/loaded through popular frameworks like TensorFlow or Scikit-Learn; all you 
+ClearML automatically logs models created/loaded through popular frameworks like TensorFlow or scikit-learn; all you 
 need to do is [instantiate a ClearML Task](clearml_sdk/task_sdk.md#task-creation) in your code. ClearML stores the 
 framework's training results as output models.
 

docs/pipelines/pipelines_sdk_tasks.md

@@ -52,7 +52,10 @@ For files, call `connect_configuration()` before reading the configuration file.
 path.
 
 ```python
-config_file = pipe.connect_configuration(configuration=config_file_path, name="My Configuration", description="configuration for pipeline")
+config_file = pipe.connect_configuration(
+    configuration=config_file_path, 
+    name="My Configuration", description="configuration for pipeline"
+)
 my_params = json.load(open(config_file,'rt'))
 ```
 

docs/webapp/datasets/webapp_dataset_viewing.md

@@ -116,7 +116,7 @@ clicking the checkbox in the top left corner of the list.
 Click the checkbox in the top left corner of the list to select all items currently visible.
 
 An extended bulk selection tool is available through the down arrow next to the checkbox in the top left corner, enabling selecting items beyond the items currently on-screen:
-* All - Select all versions in the dataset
+* **All** - Select all versions in the dataset
-* None - Clear selection
+* **None** - Clear selection
-* Filtered - Select all versions in the dataset that match the current active filters
+* **Filtered** - Select all versions in the dataset that match the current active filters