Small edits (#888)

docs/deploying_clearml/clearml_server_config.md

@@ -35,7 +35,7 @@ Single IP (domain) with the following open ports:
 
 ### Subdomain Configuration
     
-Subdomain configuration with default http/s ports (`80` or `443`):
+Subdomain configuration with default HTTP/S ports (`80` or `443`):
 
 * Web application on subdomain: `app.*.*`
 * API service on subdomain: `api.*.*`

docs/fundamentals/agents_and_queues.md

@@ -107,7 +107,7 @@ in parallel (each task will register itself as a sub-agent, visible in the [Orch
 This mode is intended for running maintenance tasks. Some suitable tasks include:
 
 - [Pipeline controller](../guides/pipeline/pipeline_controller.md) - Implementing the pipeline scheduling and logic
-- [Hyper-Parameter Optimization](../guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md) - Implementing an active selection of experiments
+- [Hyperparameter Optimization](../guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md) - Implementing an active selection of experiments
 - [Control Service](../guides/services/aws_autoscaler.md) - AWS Autoscaler for example
 - [External services](../guides/services/slack_alerts.md) - Such as Slack integration alert service
 

docs/fundamentals/artifacts.md

@@ -45,8 +45,8 @@ through a `Task` or `OutputModel` object.
 
 ClearML provides flexibility for explicitly connecting input models and experimentation, including:
 
-* Importing pre-trained models from external sources such as Amazon AWS, GIT repositories, PyTorch, and TensorFlow.
+* Importing pre-trained models from external sources such as Amazon AWS, GIT repositories, PyTorch, and TensorFlow
-* Using standalone models already registered in ClearML by previously run experiments. 
+* Using standalone models already registered in ClearML by previously run experiments
 * Defining your own input models in scripts
   
 ## Setting Upload Destination

docs/fundamentals/hpo.md

@@ -57,7 +57,7 @@ optimization.
   documentation and a [code example](../guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md).
 * **Random** uniform sampling of hyperparameters - [`automation.RandomSearch`](../references/sdk/hpo_optimization_randomsearch.md).
 * **Full grid** sampling strategy of every hyperparameter combination - [`automation.GridSearch`](../references/sdk/hpo_optimization_gridsearch.md).
-* **Custom** - [`automation.optimization.SearchStrategy`](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L268) - Use a custom class and inherit from the ClearML automation base strategy class 
+* **Custom** - [`automation.optimization.SearchStrategy`](https://github.com/allegroai/clearml/blob/master/clearml/automation/optimization.py#L268) - Use a custom class and inherit from the ClearML automation base strategy class. 
 
 
 ## Defining a Hyperparameter Optimization Search Example

docs/fundamentals/hyperparameters.md

@@ -22,10 +22,10 @@ and tracks hyperparameters of various types, supporting automatic logging and ex
 Once a ClearML Task has been [initialized](../references/sdk/task.md#taskinit) in a script, ClearML automatically captures and tracks 
 the following types of parameters:
 * Command line parsing - command line parameters passed when invoking code that uses standard python packages, including:
-  * [click](../integrations/click.md).
+  * [click](../integrations/click.md)
-  * [argparse](../guides/reporting/hyper_parameters.md#argparse-command-line-options).
+  * [argparse](../guides/reporting/hyper_parameters.md#argparse-command-line-options)
-  * [Python Fire](../integrations/python_fire.md).
+  * [Python Fire](../integrations/python_fire.md)
-  * [LightningCLI](../integrations/pytorch_lightning.md).
+  * [LightningCLI](../integrations/pytorch_lightning.md)
 * TensorFlow Definitions (`absl-py`). See examples of ClearML's automatic logging of TF Defines:
     * [TensorFlow MNIST](../guides/frameworks/tensorflow/tensorflow_mnist.md)
     * [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md)
@@ -118,9 +118,9 @@ including their subsections (see [WebApp sections](#webapp-interface) below).
 Configurations can be viewed in web UI experiment pages, in the **CONFIGURATION** tab. 
 
 The configuration panel is split into three sections according to type:
-- **User Properties** - Modifiable section that can be edited post-execution.
+- **User Properties** - Modifiable section that can be edited post-execution
 - **Hyperparameters** - Individual parameters for configuration
-- **Configuration Objects** - Usually configuration files (JSON / YAML) or Python objects. 
+- **Configuration Objects** - Usually configuration files (JSON / YAML) or Python objects
   
 These sections are further broken down into subsections based on how the parameters were logged (General / Args / TF_Define / Environment).
 

docs/fundamentals/logger.md

@@ -72,7 +72,7 @@ This section is mostly used for debugging. It's recommended to use [artifacts](t
 outputs that would be used later on.
 
 Only the last X results of each title / series are saved to prevent overloading the server.
-See details in [Logger.report_media](../references/sdk/logger.md#report_media). 
+See details in [`Logger.report_media`](../references/sdk/logger.md#report_media). 
 
 ![image](../img/fundamentals_logger_reported_images.png)
 

docs/fundamentals/projects.md

@@ -18,14 +18,14 @@ the project are executed, the model checkpoints (snapshots) and artifacts are st
 
 ## WebApp 
 
-Users can create and modify projects, and see project details in the WebApp (see [WebApp Home](../webapp/webapp_home.md)). 
+Users can create and modify projects, and see project details in the [WebApp](../webapp/webapp_home.md). 
-The project's description can be edited in the [overview](../webapp/webapp_overview.md) page. Each project's experiments,
+A project's description can be edited in its [overview](../webapp/webapp_project_overview.md) page. Each project's experiments,
 models, and dataviews, can be viewed in the project's [experiments table](../webapp/webapp_exp_table.md),
  [models table](../webapp/webapp_model_table.md), and [dataviews table](../hyperdatasets/webapp/webapp_dataviews.md). 
 
 ## Usage
 
-### Creating Subprojects
+### Creating Projects and Subprojects
 
 When [initializing a task](../clearml_sdk/task_sdk.md#task-creation), its project needs to be specified. If the project entered does not exist, it will be created. 
 Projects can contain subprojects, just like folders can contain subfolders. Input into the `project_name` 
@@ -40,7 +40,7 @@ from clearml import Task
 Task.init(project_name='main_project/sub_project', task_name='test')
 ```
 
-Nesting projects works on multiple levels. For example: `project_name=main_project/sub_project/sub_sub_project` 
+Nesting projects works on multiple levels. For example: `project_name=main_project/sub_project/sub_sub_project`. 
 
 Projects can also be created using the [`projects.create`](../references/api/projects.md#post-projectscreate) REST API call. 
 
@@ -56,7 +56,8 @@ This returns a list of projects sorted by last update time.
 
 ### More Actions
 
-For additional ways to work with projects, use the REST API `projects` resource. Some of the available actions include:
+For additional ways to work with projects, use the REST API [`projects`](../references/api/projects.md) resource. Some 
+of the available actions include:
 * [`projects.create`](../references/api/projects.md#post-projectscreate) and [`projects.delete`](../references/api/projects.md#post-projectsdelete) - create and delete projects
 * [`projects.get_hyper_parameters`](../references/api/projects.md#post-projectsget_hyper_parameters) - get a list of all hyperparameter sections and names used in a project
 * [`projects.merge_projects`](../references/api/projects.md#post-projectsmerge) - merge projects into a single project

docs/fundamentals/task.md

@@ -31,10 +31,10 @@ You can create copies of a task ([clone](../webapp/webapp_exp_reproducing.md)) t
 * Set hyperparameter and run-time configuration values
 
 Modifying a task clone's configuration will have the executing ClearML agent override the original values:
-* Modified package requirements will have the experiment script run with updated packages.
+* Modified package requirements will have the experiment script run with updated packages
 * Modified recorded command line arguments will have the ClearML agent inject the new values in their stead
 * Code-level configuration instrumented with [`Task.connect`](../references/sdk/task.md#connect) will be overridden by 
-modified hyperparameters.
+modified hyperparameters
  
 
 
@@ -149,7 +149,7 @@ The above diagram describes how execution information is recorded when running c
    including:
    * Source code
    * Python environment 
-   * Configuration parameters.
+   * Configuration parameters
 1. As the execution progresses, any outputs produced are recorded including:
    * Console logs
    * Metrics and graphs 

docs/guides/storage/examples_storagehelper.md

@@ -10,7 +10,7 @@ class. The storage examples include:
 * [Setting cache limits](#setting-cache-limits) - Set the maximum number of objects.
 
 :::note 
-`StorageManager` supports http(s), S3, Google Cloud Storage, Azure, and file system folders.
+`StorageManager` supports HTTP(S), S3, Google Cloud Storage, Azure, and file system folders.
 :::
 
 ## StorageManager