Small edits (#161)

docs/clearml_agent.md

@@ -61,7 +61,7 @@ Install ClearML Agent as a system Python package and not in a Python virtual env
    clearml-agent init
    ```
 
-    The setup wizard prompts for **ClearML** credentials (see [here](webapp/webapp_profile.md#creating-clearml-credentials) about obtaining credentials).
+    The setup wizard prompts for **ClearML** credentials (see [here](webapp/webapp_profile.md#clearml-credentials) about obtaining credentials).
     
         CLEARML-AGENT setup process
 

docs/deploying_clearml/clearml_config_for_clearml_server.md

@@ -6,13 +6,13 @@ title: Configuring ClearML for Your ClearML Server
 This documentation page applies to deploying your own open source ClearML Server. It does not apply to ClearML Hosted Service users.
 :::
 
-The **ClearML** configuration file that will be initialized will contain the host URLs of the **ClearML Server**, and 
+The ClearML **configuration file** that will be initialized will contain the host URLs of the **ClearML Server**, and 
-**ClearML** credentials, allowing the code to integrate with the server. Later, **ClearML** can be tailored to fit requirements
+ClearML credentials, allowing the code to integrate with the server. Later, ClearML can be tailored to fit requirements
 by setting [configuration options](../configs/clearml_conf.md).
 
 **To configure ClearML for your ClearML Server:**
 1. If not installed already, install `clearml` (see [install](../getting_started/ds/ds_first_steps.md))
-1. In a terminal session, run the **ClearML** setup wizard. 
+1. In a terminal session, run the ClearML setup wizard. 
    ```
    clearml-init 
    ```
@@ -21,7 +21,7 @@ by setting [configuration options](../configs/clearml_conf.md).
    <summary className="cml-expansion-panel-summary">Learn about creating multiple ClearML configuration files</summary>
    <div className="cml-expansion-panel-content">
 
-   Additional **ClearML** configuration files can be created, for example, to use inside Docker containers when executing 
+   Additional ClearML configuration files can be created, for example, to use inside Docker containers when executing 
    a Task.
    
    Use the `--file` option for `clearml-init`.
@@ -42,7 +42,7 @@ by setting [configuration options](../configs/clearml_conf.md).
    If the setup wizard's response indicates that a configuration file already exists, follow the instructions in 
    [here](#add-clearml-to-a-configuration-file). The wizard does not edit or overwrite existing configuration files.
 
-   1. The setup wizard prompts for **ClearML** credentials.
+   1. The setup wizard prompts for ClearML credentials.
     
     
             ClearML SDK setup process
@@ -53,7 +53,7 @@ by setting [configuration options](../configs/clearml_conf.md).
             
             Paste copied configuration here:
 
-   1. Get **ClearML** credentials. Open the **ClearML Web UI** in a browser. On the **PROFILE** page, click 
+   1. Get ClearML credentials. Open the ClearML Web UI in a browser. On the **SETTINGS > WORKSPACE** page, click 
       **Create new credentials** **>** **Copy to clipboard**.
     
    1. At the command prompt `Paste copied configuration here:`, copy and paste the **ClearML** credentials.

docs/deploying_clearml/clearml_server_config.md

@@ -9,7 +9,7 @@ This documentation page applies to deploying your own open source ClearML Server
 This page describes the **ClearML Server** [deployment](#clearml-server-deployment-configuration) and [feature](#clearml-server-feature-configurations) configurations. Namely, it contains instructions on how to configure **ClearML Server** for:
 
 * [Sub-domains and load balancers](#sub-domains-and-load-balancers) - An AWS load balancing example
-* [Opening Elasticsearch, MongoDB, and Redis for External Access](#opening-elasticsearch-mongodb-and-redis-for-external-access).
+* [Opening Elasticsearch, MongoDB, and Redis for External Access](#opening-elasticsearch-mongodb-and-redis-for-external-access)
 * [Web login authentication](#web-login-authentication) - Create and manage users and passwords
 * [Using hashed passwords](#using-hashed-passwords) - Option to use hashed passwords instead of plain-text passwords
 * [Non-responsive Task watchdog](#non-responsive-task-watchdog) - For inactive experiments

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 profile 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)
@@ -162,7 +162,7 @@ that metric column.
 
 **Can I store more information on the models?** <a id="store-more-model-info"></a>
 
-Yes! For example, you can use the [Task.set_model_label_enumeration](references/sdk/task.md#set_model_label_enumerationenumerationnone) 
+Yes! For example, you can use the [Task.set_model_label_enumeration](references/sdk/task.md#set_model_label_enumeration) 
 method to store label enumeration:
 
 ```python
@@ -176,7 +176,7 @@ For more information about `Task` class methods, see the [Task Class](fundamenta
 
 **Can I store the model configuration file as well?**  <a id="store-model-configuration"></a>
 
-Yes! Use the [Task.set_model_config](references/sdk/task.md#set_model_configconfig_textnone-config_dictnone) 
+Yes! Use the [Task.set_model_config](references/sdk/task.md#set_model_config) 
 method:
 
 ```python
@@ -708,7 +708,7 @@ Yes! You can run ClearML in Jupyter Notebooks using either of the following:
         
 **Option 2: Install ClearML in your Jupyter Notebook**
 
-1. In the ClearML Web UI, Profile page, create credentials and copy your access key and secret key. These are required in the Step 3.
+1. In the ClearML Web UI > **Settings > Workspace** page, create credentials and copy your access key and secret key. These are required in the Step 3.
 
 1. Install the ClearML Python Package.
 

docs/guides/frameworks/pytorch/pytorch_mnist.md

@@ -33,13 +33,14 @@ Logger.current_logger().report_scalar(
 )
 ```    
 
-These scalars can be visualized in plots, which appear in the **ClearML web UI**, in the experiment's
+These scalars can be visualized in plots, which appear in the ClearML [web UI](../../../webapp/webapp_overview.md), 
-page **>** **RESULTS** **>** **SCALARS**. 
+in the experiment's page **>** **RESULTS** **>** **SCALARS**. 
+
 ![image](../../../img/examples_pytorch_mnist_07.png)
 
 ## Hyperparameters
 
-**ClearML** automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
+ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**.
 
 ![image](../../../img/examples_pytorch_mnist_01.png)
 

docs/guides/frameworks/pytorch/pytorch_tensorboard.md

@@ -15,7 +15,8 @@ The example does the following:
 ## Scalars
 
 In the example script, the `train` and `test` functions call the TensorBoard `SummaryWriter.add_scalar` method to log loss. 
-These scalars, along with the resource utilization plots, which are titled **:monitor: machine**, appear in the experiment's page in the **ClearML web UI** under **RESULTS** **>** **SCALARS**, 
+These scalars, along with the resource utilization plots, which are titled **:monitor: machine**, appear in the experiment's 
+page in the [ClearML web UI](../../../webapp/webapp_overview.md) under **RESULTS** **>** **SCALARS**. 
 
 ![image](../../../img/examples_pytorch_tensorboard_07.png)
 

docs/guides/frameworks/tensorboardx/tensorboardx.md

@@ -6,10 +6,10 @@ The [pytorch_tensorboardX.py](https://github.com/allegroai/clearml/blob/master/e
 example demonstrates the integration of ClearML into code that uses PyTorch and TensorBoardX. 
 
 The script does the following:
-* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) dataset.
+* Trains a simple deep neural network on the PyTorch built-in [MNIST](https://pytorch.org/vision/stable/datasets.html#mnist) dataset
 * Creates an experiment named `pytorch with tensorboardX` which is associated with the `examples` project
 * ClearML automatically captures scalars and text logged using the TensorBoardX `SummaryWriter` object, and 
-  the model created by PyTorch. 
+  the model created by PyTorch 
 
 ## Scalars
 

docs/hyperdatasets/webapp/webapp_dataviews.md

@@ -1,5 +1,5 @@
 ---
-title: Dataviews Table
+title: The Dataviews Table
 ---
 
 [Dataviews](../dataviews.md) appear in the same Project as the experiment that stored the Dataview in the ClearML Enterprise platform, 

docs/webapp/webapp_exp_comparing.md

@@ -9,7 +9,7 @@ The **ClearML Web UI** provides a deep experiment comparison, allowing to locate
     - [Artifacts](#artifacts) - Input model, output model, and model design.
     - [Execution](#execution-details) - Installed packages and source code.
     - [Configuration](#configuration) - Configuration objects used by the experiment.
-* [Hyper Parameters](#hyperparameters)
+* [Hyperparameters](#hyperparameters)
     - [Values (table) view](#values-mode) - Key/value of all the arguments used by the experiments.
     - [Parallel coordinates view](#parallel-coordinates-mode) - Impact of each argument on a selected metric
       the experiments reported (see [task.connect_configuration](../references/sdk/task.md#connect_configuration)).