Fix links (#167)

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#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
 
@@ -73,7 +73,7 @@ Install ClearML Agent as a system Python package and not in a Python virtual env
     If the setup wizard's response indicates that a configuration file already exists, follow the instructions [here](#adding-clearml-agent-to-a-configuration-file). 
    The wizard does not edit or overwrite existing configuration files.
 
-1. At the command prompt `Paste copied configuration here:`, copy and paste the **ClearML** credentials and press **Enter**. 
+1. At the command prompt `Paste copied configuration here:`, copy and paste the ClearML credentials and press **Enter**. 
    The setup wizard confirms the credentials. 
         
         Detected credentials key="********************" secret="*******"

docs/deploying_clearml/clearml_server.md

@@ -15,7 +15,7 @@ ClearML Server is composed of the following:
 
 * File server which stores media and models making them easily accessible using the ClearML Web UI.
 
-The [**ClearML Hosted Service**](https://app.community.clear.ml) is essentially the ClearML Server maintained for you.
+The [**ClearML Hosted Service**](https://app.clear.ml) is essentially the ClearML Server maintained for you.
 
 ![image](../img/ClearML_Server_Diagram.png)
 

docs/getting_started/ds/ds_first_steps.md

@@ -6,14 +6,14 @@ title: First Steps
 ## Install ClearML
 
 
-First, [sign up for free](https://app.community.clear.ml)
+First, [sign up for free](https://app.clear.ml)
 
 Install the clearml python package:
 ```bash
 pip install clearml
 ```
 
-Connect your computer to the server by [creating credentials](https://app.community.clear.ml/profile), then run the below and follow the setup instructions:
+Connect your computer to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration), then run the below and follow the setup instructions:
 ```bash
 clearml-init
 ```

docs/getting_started/mlops/mlops_first_steps.md

@@ -3,7 +3,7 @@ title: First Steps
 ---
 
 :::note
-This tutorial assumes that you've already [signed up](https://app.community.clear.ml) to ClearML
+This tutorial assumes that you've already [signed up](https://app.clear.ml) to ClearML
 :::
 
 ClearML provides tools for **automation**, **orchestration**, and **tracking**, all key in performing effective MLOps. 
@@ -33,7 +33,7 @@ required python packages, and execute & monitor the process.
     pip install clearml-agent
     ```
 
-1. Connect the agent to the server by [creating credentials](https://app.community.clear.ml/profile), then run this:
+1. Connect the agent to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration), then run this:
 
     ```bash
     clearml-agent init

docs/guides/ide/google_colab.md

@@ -30,7 +30,7 @@ and running, users can send Tasks to be executed on the Google Colab's HW.
    This environment variable makes Matplotlib work in headless mode, so it won't output graphs to the screen.
    
 1. Create new credentials.   
-   Go to your **profile** in the [ClearML WebApp](https://app.community.clear.ml). Under the **WORKSPACES** section, 
+   Go to your **profile** in the [ClearML WebApp](https://app.clear.ml). Under the **WORKSPACES** section, 
    go to **App Credentials**, click **+ Create new credentials**, and copy the information that pops up. 
    
 1. Set the credentials.  

docs/release_notes/ver_0_17.md

@@ -254,7 +254,7 @@ title: Version 0.17
 
 ### ClearML Hosted Service Only
 
-* Launched free [ClearML Hosted Service](https://app.community.clear.ml/dashboard).
+* Launched free [ClearML Hosted Service](https://app.clear.ml/dashboard).
 * Multiple workspaces.
 * Workspace invitation.
 * Add publicly sharing experiments.