Fix links (#1053)

docs/apps/clearml_param_search.md

@@ -34,7 +34,7 @@ of the optimization results in table and graph forms.
 |`--objective-metric-sign`| Optimization target, whether to maximize or minimize the value of the objective metric specified. Possible values: "min", "max", "min_global", "max_global". For more information, see [Optimization Objective](#optimization-objective). |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 |`--objective-metric-title`| Objective metric title to maximize/minimize (e.g. 'validation').|<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 |`--optimization-time-limit`|The maximum time (minutes) for the optimization to run. The default is `None`, indicating no time limit.|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|
-|`--optimizer-class`|The optimizer to use. Possible values are: OptimizerOptuna (default), OptimizerBOHB, GridSearch, RandomSearch. For more information, see [Supported Optimizers](../getting_started/hpo.md#supported-optimizers). |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
+|`--optimizer-class`|The optimizer to use. Possible values are: OptimizerOptuna (default), OptimizerBOHB, GridSearch, RandomSearch. For more information, see [Supported Optimizers](../clearml_sdk/hpo_sdk.md#supported-optimizers). |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 |`--params-search`|Parameters space for optimization. See more information in [Specifying the Parameter Space](#specifying-the-parameter-space). |<img src="/docs/latest/icons/ico-optional-yes.svg" alt="Yes" className="icon size-md center-md" />|
 |`--params-override`|Additional parameters of the base task to override for this parameter search. Use the following JSON format for each parameter: `{"name": "param_name", "value": <new_value>}`. Windows users, see [JSON format note](#json_note).|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />| 
 |`--pool-period-min`|The time between two consecutive polls (minutes).|<img src="/docs/latest/icons/ico-optional-no.svg" alt="No" className="icon size-md center-md" />|

docs/best_practices/data_best_practices.md

@@ -25,7 +25,7 @@ version contents ready to be updated.
 Organize the datasets according to use-cases and use tags. This makes managing multiple datasets and 
 accessing the most updated datasets for different use-cases easier. 
 
-Like any ClearML tasks, datasets can be organized into [projects (and subprojects)](../fundamentals/projects.md#creating-subprojects). 
+Like any ClearML tasks, datasets can be organized into [projects (and subprojects)](../fundamentals/projects.md#creating-projects-and-subprojects). 
 Additionally, when creating a dataset, tags can be applied to the dataset, which will make searching for the dataset easier.
 
 Organizing your datasets into projects by use-case makes it easier to access the most recent dataset version for that use-case. 

docs/clearml_agent/clearml_agent_setup.md

@@ -27,7 +27,7 @@ it can't do that when running from a virtual environment.
    clearml-agent init
    ```
 
-    The setup wizard prompts for ClearML credentials (see [here](../webapp/settings/webapp_settings_profile.md#clearml-credentials) about obtaining credentials).
+    The setup wizard prompts for ClearML credentials (see [here](../webapp/settings/webapp_settings_profile.md#clearml-api-credentials) about obtaining credentials).
     ```
     Please create new clearml credentials through the settings page in your `clearml-server` web app, 
     or create a free account at https://app.clear.ml/settings/webapp-configuration

docs/clearml_sdk/clearml_sdk_setup.md

@@ -85,7 +85,7 @@ pip install clearml
    CLEARML setup completed successfully.
    ```
    
-Now you can integrate ClearML into your code! Continue [here](../clearml_sdk/clearml_sdk_setup#auto-log-experiment).
+Now you can integrate ClearML into your code! Continue [here](../getting_started/auto_log_exp.md).
 
 ### Jupyter Notebook
 To use ClearML with Jupyter Notebook, you need to configure ClearML Server access credentials for your notebook.

docs/cloud_autoscaling/autoscaling_overview.md

@@ -71,7 +71,7 @@ execute the tasks in the GPU queue.
 #### Docker
 Every task a cloud instance pulls will be run inside a docker container. When setting up an autoscaler app instance, 
 you can specify a default container to run the tasks inside. If the task has its own container configured, it will 
-override the autoscaler’s default docker image (see [Base Docker Image](../getting_started/clearml_agent_docker_exec.md#base-docker-container)).
+override the autoscaler’s default docker image (see [Base Container](../getting_started/clearml_agent_base_docker.md#base-container)).
 
 #### Git Configuration 
 If your code is saved in a private repository, you can add your Git credentials so the ClearML Agents running on your

docs/getting_started/architecture.md

@@ -2,7 +2,7 @@
 title: ClearML Modules
 ---
 
-- [**ClearML Python Package**](auto_log_exp#install-clearml) (`clearml`) for integrating ClearML into your existing code-base.
+- [**ClearML Python Package**](../clearml_sdk/clearml_sdk_setup.md) (`clearml`) for integrating ClearML into your existing code-base.
 - [**ClearML Server**](../deploying_clearml/clearml_server.md) (`clearml-server`) for storing task, model, and workflow data, and supporting the Web UI experiment manager. It is also the control plane for the MLOps.
 - [**ClearML Agent**](../clearml_agent.md) (`clearml-agent`), the MLOps orchestration agent. Enabling task and workflow reproducibility, and scalability.
 - [**ClearML Data**](../clearml_data/clearml_data.md) (`clearml-data`) data management and versioning on top of file-systems/object-storage.

docs/integrations/pytorch_lightning.md

@@ -144,6 +144,6 @@ task.execute_remotely(queue_name='default', exit_process=True)
 
 ## Hyperparameter Optimization
 Use ClearML's [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find 
-the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../getting_started/hpo.md) 
 for more information.
 

docs/webapp/webapp_model_comparing.md

@@ -46,8 +46,7 @@ models update. The Enterprise Plan and Hosted Service support embedding resource
 The comparison tabs provides the following views:
 * [Side-by-side textual comparison](#side-by-side-textual-comparison)
 * [Tabular scalar comparison](#tabular-scalar-comparison)
-* [Merged plot comparison](#plot-comparison) 
+* [Plot comparison](#plot-comparison) 
-* [Side-by-side graphic comparison](#graphic-comparison) 
 
 
 ### Side-by-side Textual Comparison