Merge branch 'main' of https://github.com/allegroai/clearml-docs

docs/clearml_data/clearml_data.md

@@ -11,10 +11,11 @@ In Machine Learning, you are very likely dealing with a gargantuan amount of dat
 which you then need to be able to share, reproduce, and track.
 
 ClearML Data Management solves two important challenges:
-- Accessibility - Making data easily accessible from every machine,
+- Accessibility - Making data easily accessible from every machine
 - Versioning - Linking data and tasks for better **traceability**.
 
-![Dataset lineage and preview](../img/webapp_dataset_lineage_preview.png)
+![Dataset preview](../img/webapp_dataset_preview.png#light-mode-only)
+![Dataset preview](../img/webapp_dataset_preview_dark.png#dark-mode-only)
 
 **We believe Data is not code**. It should not be stored in a git tree, because progress on datasets is not always linear.
 Moreover, it can be difficult and inefficient to find on a git tree the commit associated with a certain version of a dataset.

docs/clearml_sdk/task_sdk.md

@@ -253,7 +253,8 @@ task's status. If a task failed or was aborted, you can view how much progress i
 
 <div class="max-w-50">
 
-![Task table progress indication](../img/fundamentals_task_progress.png)
+![Task table progress indication](../img/fundamentals_task_progress.png#light-mode-only)
+![Task table progress indication](../img/fundamentals_task_progress_dark.png#dark-mode-only)
 
 </div>
 

docs/guides/clearml_agent/executable_exp_containers.md

@@ -3,7 +3,7 @@ title: Executable Task Containers
 ---
 
 This tutorial demonstrates using [`clearml-agent`](../../clearml_agent.md)'s [`build`](../../clearml_agent/clearml_agent_ref.md#build) 
-command to package a task into an executable container. In this example, you will build a Container image that, when 
+command to package a task into an executable container. In this example, you will build a container image that, when 
 run, will automatically execute the [keras_tensorboard.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py)
 script.
 

docs/webapp/pipelines/webapp_pipeline_viewing.md

@@ -67,9 +67,13 @@ On the pipeline run panel, view the **RUN INFO** which shows:
 * Produced Artifacts 
 * Output Models 
 
+<div class="max-w-25"> 
+
 ![Run info](../../img/webapp_pipeline_run_info.png#light-mode-only)
 ![Run info](../../img/webapp_pipeline_run_info_dark.png#dark-mode-only)
 
+</div>
+
 To view a run's complete information, click **Full details**, which will open the pipeline's controller [task page](../webapp_exp_track_visual.md). 
 View each list's complete details in the pipeline task's corresponding tabs: 
 * **PARAMETERS** list > **CONFIGURATION** tab

docs/webapp/webapp_model_table.md

@@ -44,7 +44,7 @@ The model table contains the following columns:
 | *Metadata* | User defined metadata key column. Available options depend upon the models in the table. | String |
 
 
-## Customizing the Model table
+## Customizing the Model Table
 
 The model table is customizable. Changes are persistent (cached in the browser) and represented in the URL, so customized settings
 can be saved in a browser bookmark and shared with other ClearML users to collaborate.