change titles to title case for consistency (#60)

docs/deploying_clearml/clearml_config_for_clearml_server.md

@@ -95,7 +95,7 @@ The configuration file's location depends upon the operating system:
 * Mac - `$HOME/clearml.conf`
 * Windows - `\User\<username>\clearml.conf`
 
-## Add ClearML to a configuration file
+## Add ClearML to a Configuration File
 
 The setup wizard may indicate that a configuration file already exists. For example, if a **ClearML Agent** was previously 
 configured, then a configuration file was created. The wizard does not edit or overwrite existing configuration files. 

docs/fundamentals/agents_and_queues.md

@@ -8,7 +8,7 @@ coupled with execution queues, addresses both these needs.
 The ClearML Agent is the base for **Automation** in ClearML and can be leveraged to build automated pipelines, launch custom services 
 (e.g. a [monitor and alert service](https://github.com/allegroai/clearml/tree/master/examples/services/monitoring)) and more.
 
-## What does a ClearML Agent do?
+## What Does a ClearML Agent Do?
 An agent (also referred to as a Worker) allows users to execute code on any machine it's installed on, thus facilitating the 
 scaling of data science work beyond one's own machine.  
 The agent takes care of deploying the code to the target machine as well as setting up the entire execution environment: 
@@ -33,7 +33,7 @@ A ClearML Agent can service multiple queues in either of the following modes:
 * Strict priority: The agent services the higher priority queue before servicing lower priority ones.
 * Round robin: The agent pulls a single task from a queue then moves to service the next queue.
 
-## Agent and Queue workflow 
+## Agent and Queue Workflow 
 
 ![image](../img/clearml_agent_flow_diagram.png)
 
@@ -52,7 +52,7 @@ The diagram above demonstrates a typical flow where an agent executes a task:
 
 While the agent is running, it continuously reports system metrics to the ClearML Server (These can be monitored in the **Workers and Queues** page).  
 
-## Resource management
+## Resource Management
 Installing an Agent on machines allows it to monitor all the machine's status (GPU \ CPU \ Memory \ Network \ Disk IO). 
 When managing multiple machines, this allows users to have an overview of their entire HW resources. What is the status of each machine, what is the expected workload
 on each machine and so on.
@@ -70,7 +70,7 @@ Task to one of your queues, according to the amount of resources you want to all
 With queues and ClearML Agent, you can easily add and remove machines from the cluster, and you can 
 reuse machines without the need for any dedicated containers or images.
 
-## Additional features
+## Additional Features
 
 Agents can be deployed bare-metal, with multiple instances allocating 
 specific GPUs to the agents. They can also be deployed as dockers in a Kubernetes cluster.

docs/fundamentals/hpo.md

@@ -63,7 +63,7 @@ optimization.
 * **Custom** - `automation.optimization.SearchStrategy` - Use a custom class and inherit from the ClearML automation base strategy class 
 
 
-## Defining a hyperparameter optimization search example
+## Defining a Hyperparameter Optimization Search Example
 
 1. Import ClearML's automation modules: 
 

docs/fundamentals/pipelines.md

@@ -7,7 +7,7 @@ Tasks in a pipeline can leverage other tasks' work products such as artifacts an
 
 Pipelines are controlled by a *Controller Task* that holds the logic of the pipeline execution steps. 
 
-## How do pipelines work? 
+## How Do Pipelines Work? 
 
 Before running a pipeline, we need to configure a Controller Task, in which the pipeline is defined. Pipelines are made 
 up of steps. Each step consists of a task that already exists in the ClearML Server and is used as a template. The 
@@ -25,7 +25,7 @@ create customized, step-specific callbacks.
 ![Pipeline chart](../img/fundamentals_pipeline.png)
 
 
-## Simple DAG pipelines
+## Simple DAG Pipelines
 
 For a simple, DAG based logic, use the off-the-shelf [`PipelineController`](../references/sdk/automation_controller_pipelinecontroller.md) class to define the DAG (see an example 
 [here](../guides/pipeline/pipeline_controller)). Once the `PipelineController` object is populated and configured, 
@@ -44,7 +44,7 @@ method. Alternatively, step-specific callback functions can be specified with th
 `post_execute_callback` parameters of the [`add_step`](../references/sdk/automation_controller_pipelinecontroller.md#add_step) 
 method. 
 
-## Advanced pipelines
+## Advanced Pipelines
 
 Since a pipeline *Controller Task* is itself a ClearML Task, it can be used as a pipeline step and can be used to create 
 more complicated workflows, such as pipelines running other pipelines, or a pipeline running multiple tasks concurrently.
@@ -56,7 +56,7 @@ It could also be useful to run a pipeline that runs tasks concurrently, training
 values simultaneously. See the [Tabular training pipeline](../guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline.md) 
 example of a pipeline with concurrent steps. 
 
-## Custom pipelines
+## Custom Pipelines
 
 In cases where a DAG is insufficient (for example, when needing to launch one pipeline, then, if performance is inadequate, 
 rerun pipeline again), users can apply custom logic, using generic methods to enqueue tasks, implemented in python code.

docs/guides/frameworks/keras/jupyter.md

@@ -31,7 +31,7 @@ They appear in **RESULTS** **>** **PLOTS**.
 
 ![image](../../../img/examples_keras_jupyter_03b.png)
 
-## Debug samples
+## Debug Samples
 
 The example calls Matplotlib methods to log debug sample images. They appear in **RESULTS** **>** **DEBUG SAMPLES**.
 
@@ -63,7 +63,7 @@ The TensorFlow Definitions appear in the **TF_DEFINE** subsection.
 
 ![image](../../../img/examples_keras_jupyter_21.png)
 
-## CONSOLE
+## Console
 
 Text printed to the console for training appears in **RESULTS** **>** **CONSOLE**.
 

docs/guides/frameworks/keras/keras_tensorboard.md

@@ -46,7 +46,7 @@ Text printed to the console for training progress, as well as all other console
 
 ![image](../../../img/examples_keras_03.png)
 
-## Configuration objects
+## Configuration Objects
 
 In the experiment code, a configuration dictionary is connected to the Task by calling the [Task.connect](../../../references/sdk/task.md#connect) 
 method. 

docs/guides/frameworks/pytorch/tensorboard_toy_pytorch.md

@@ -7,7 +7,7 @@ example demonstrates the integration of **ClearML** into code, which creates a T
 debug sample images. When the script runs, it creates an experiment named `pytorch tensorboard toy example`, which is 
 associated with the `examples` project.
 
-## Debug samples
+## Debug Samples
 
 The debug sample images appear according to metric, in the experiment page in the **ClearML web UI** under **RESULTS** 
 **>** **DEBUG SAMPLES**.

docs/guides/frameworks/pytorch_lightning/pytorch_lightning_example.md

@@ -1,5 +1,5 @@
 ---
-title: Pytorch Lightning
+title: PyTorch Lightning
 ---
 
 The [pytorch-lightning](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch-lightning/pytorch_lightning_example.py) 

docs/hyperdatasets/annotations.md

@@ -10,7 +10,7 @@ Annotation Tasks can be used to efficiently organize the annotation of frames in
 
 For information about how to view, create, and manage annotations using the WebApp, see [Annotating Images and Videos](webapp/webapp_annotator.md#annotating-images-and-video).
 
-## Frame objects
+## Frame Objects
 
 Frame objects are labeled Regions of Interest (ROIs), which can be bounded by polygons (including rectangles), ellipses, 
 or key points. These ROIs are useful for object detection, classification, or semantic segmentation. 
@@ -18,14 +18,14 @@ or key points. These ROIs are useful for object detection, classification, or se
 Frame objects can include ROI labels, confidence levels, and masks for semantic segmentation. In ClearML Enterprise, 
 one or more labels and sources dictionaries can be associated with an ROI (although multiple source ROIs are not frequently used).
 
-## Frame labels
+## Frame Labels
 
 Frame labels are applied to an entire frame, not a region in a frame.
 
 
 ## Usage
 
-### Adding a frame object
+### Adding a Frame Object
 
 To add a frame object annotation to a SingleFrame, use the `SingleFrame.add_annotation` method.  
 
@@ -43,7 +43,7 @@ When adding an annotation there are a few options for entering the annotation's
 * `ellipse2d_xyrrt` - A List consisting of cx, cy, rx, ry, and theta for an ellipse 
 * And more! See `SingleFrame.add_annotation` for further options. 
 
-### Adding a Frame label
+### Adding a Frame Label
 
 Adding a frame label is similar to creating a frame objects, except that coordinates don't need to be specified, since 
 the whole frame is being referenced. 

docs/hyperdatasets/dataset.md

@@ -17,7 +17,7 @@ metadata and data.
 These parent-child version relationships can be represented as version trees with a root-level parent. A Dataset 
 can contain one or more trees.
 
-## Dataset version state
+## Dataset Version State
 
 Dataset versions can have either **Draft** or **Published** status. 
 

docs/hyperdatasets/single_frames.md

@@ -52,7 +52,7 @@ For more information, see [Previews](previews.md).
 
 For more information, see [Custom Metadata](custom_metadata.md).
 
-## Frame structure
+## Frame Structure
 
 The panel below describes the details contained within a `frame`:
 
@@ -294,7 +294,7 @@ myDatasetVersion.update_frames(frames)
 ```
 
 
-### Deleting frames
+### Deleting Frames
 
 To delete a SingleFrame, use the `DatasetVersion.delete_frames` method.
 

docs/webapp/webapp_exp_reproducing.md

@@ -1,5 +1,5 @@
 ---
-title: Reproducing experiments
+title: Reproducing Experiments
 ---
 
 Reproduce experiments on local or remote machines, in one of the following ways: 

docs/webapp/webapp_exp_table.md

@@ -14,7 +14,7 @@ in the active experiments and models tables. See [Archiving](webapp_archiving).
 
 ![Experiment table](../img/webapp_exp_table_01.png)
 
-## Experiments table columns
+## Experiments Table Columns
 
 The experiments table default and customizable columns are described in the following table.
 
@@ -36,7 +36,7 @@ The experiments table default and customizable columns are described in the foll
 
 
 
-## Customizing the experiments table
+## Customizing the Experiments Table
 
 The experiments table can be customized by:
 * Showing / hiding default columns
@@ -69,24 +69,24 @@ all the experiments in the project. The customizations of these two views are sa
 
 
 
-### Adding metrics and / or hyperparameters
+### Adding Metrics and / or Hyperparameters
 
 ![Experiment table customization gif](../img/gif/webapp_exp_table_cust.gif)
 
 Add metrics and / or hyperparameters columns to the experiments table. The metrics and hyperparameters depend upon the
 experiments in the table.
 
-#### To add metrics:
+#### To Add Metrics:
 
 * Click <img src="/docs/latest/icons/ico-settings.svg" alt="Setting Gear" className="icon size-md" /> **>** **+ METRICS** **>** Expand a metric **>** Select the **LAST** (value),
   **MIN** (minimal value), and / or **MAX** (maximal value) checkboxes.
 
-#### To add hyperparameters:
+#### To Add Hyperparameters:
 
 * Click <img src="/docs/latest/icons/ico-settings.svg" alt="Setting Gear" className="icon size-md" /> **>** **+ HYPER PARAMETERS** **>** Expand a section **>** Select the
   hyperparameter checkboxes.
 
-### Filtering experiments
+### Filtering Experiments
 
 ![Filtering table gif](../img/gif/filter_screenshots.gif)
 
@@ -106,7 +106,7 @@ Once a filter is applied to a column, its filter icon will appear with a highlig
 
 
 
-### Using other customization features
+### Using Other Customization Features
 
 **To use other customization features:**
 
@@ -116,7 +116,7 @@ Once a filter is applied to a column, its filter icon will appear with a highlig
 * Column resizing - In the column heading, drag to a new size.
 * Column autofit - In the column heading, double click a column separator.
 
-## Experiment actions
+## Experiment Actions
 
 The following table describes the actions that can be done from the experiments table, including the [states](../fundamentals/task.md#task-states-and-state-transitions)
 that allow each operation.  
@@ -151,7 +151,7 @@ appears when hovering over an action icon.
 ![Experiment table batch operations](../img/webapp_exp_table_batch_operations.png)
 
 
-## Creating an experiment leaderboard
+## Creating an Experiment Leaderboard
 
 Filter & sort the experiments of any project to create a leaderboard that can be shared and stored. This leaderboard
 updates in real time with experiment performance and outputs.

docs/webapp/webapp_model_modifying.md

@@ -5,7 +5,7 @@ title: Modifying Models
 In the models table, modify models that have a status of *Draft* (status *Published* is read-only). Modify the model
 configuration and label enumeration.
 
-## Model configuration
+## Model Configuration
 
 **To edit the model configuration:**
 
@@ -14,7 +14,7 @@ configuration and label enumeration.
 
 ![image](../img/webapp_models_05a.png)
 
-### Label enumeration
+### Label Enumeration
 
 For each class, label enumeration contains the class name (key) and value.
 

docs/webapp/webapp_model_viewing.md

@@ -4,7 +4,7 @@ title: Viewing Model Details
 
 In the models table, click on a model to view its general information, configuration, and label enumeration.
     
-## General model information
+## General Model Information
 
 General information includes: 
 * Model URL
@@ -16,11 +16,11 @@ If a model is a local file, it is downloadable. If a model is in another type of
 
 ![image](../img/webapp_models_02.png)
 
-## Model configuration 
+## Model Configuration 
 
 ![image](../img/webapp_models_05.png)
 
-## Label enumeration
+## Label Enumeration
 
 For each class, label enumeration contains the class name (key) and value.
 

docs/webapp/webapp_profile.md

@@ -10,7 +10,7 @@ Use the Profile page to manage a **ClearML** user account, including:
 * [Switch workspaces](#switching-workspaces) - If using multiple workspaces (are a member of more than one **ClearML Hosted Service** team), switch workspaces.
 * [Invite new teammates](#inviting-new-teammates) - Collaborate with new users by inviting them to a **ClearML Hosted Service** workspace.
 
-## Setting user preferences
+## Setting User Preferences
 
 The **HiDPI browser scale override** adjusts scaling on High-DPI monitors to improve the Web UI experience. It is enabled
 by default, but can be disabled.
@@ -29,7 +29,7 @@ Users that use their own **ClearML Server** can choose whether to send anonymous
     * **Region** - The region for AWS S3.
     * **Host (Endpoint)** - The host for non-AWS S3 servers.
 
-## Creating ClearML credentials
+## Creating ClearML Credentials
 
 **ClearML** credentials include:
 * Access key
@@ -53,7 +53,7 @@ switch to it.
 
 1. In **App Credentials** **>** **+ Create new credentials**.
 
-## Switching workspaces
+## Switching Workspaces
 
 :::note
 Switching workspaces does not apply to users of a self-hosted **ClearML Server**
@@ -67,7 +67,7 @@ Switching workspaces does not apply to users of a self-hosted **ClearML Server**
   Click the workspace to switch to.
 * Profile page - In the **WORKSPACES** section, click **SWITCH TO WORKSPACE** **>** Click the workspace to switch to.
 
-## Inviting new teammates
+## Inviting New Teammates
 
 :::note
 Inviting new teammates does not apply to users of a self-hosted **ClearML Server**.
@@ -88,7 +88,7 @@ section shows the current members of the team, and whether the team has reached
 
 1. Send the invitation hyperlink to an invitee.
 
-## Leaving a workspace
+## Leaving a Workspace
 
 A member of a workspace can leave the workspace and no longer be a member of that team.
 

docs/webapp/webapp_workers_queues.md

@@ -12,7 +12,7 @@ With the **Workers and Queues** page, users can:
 
 
 
-## Resources utilization
+## Resources Utilization
 
 **To monitor resource utilization:**
 
@@ -34,7 +34,7 @@ With the **Workers and Queues** page, users can:
 
 
 
-## Worker utilization
+## Worker Utilization
 
 Optimize worker use by monitoring worker utilization in the **Workers** tab.
 
@@ -45,7 +45,7 @@ Optimize worker use by monitoring worker utilization in the **Workers** tab.
 
 
 
-## Queue utilization
+## Queue Utilization
 
 **To monitor all queues:**
 
@@ -64,7 +64,7 @@ Optimize worker use by monitoring worker utilization in the **Workers** tab.
 
 
 
-## Queue management
+## Queue Management
 
 In the **Queues** tab, do any of the following: