merge

.github/workflows/main.yml

@@ -28,4 +28,4 @@ jobs:
       # Runs a single command using the runners shell
       - name: Run a one-line script
         run: |
-          grep -r -Eoh "(https?://github.com/[a-zA-Z0-9./?=_%:-]*)" $GITHUB_WORKSPACE | sort -u | grep -v "://github.com/allegroai/clearml-docs" | xargs -n 1 sh -c 'curl --output /dev/null --silent --head --fail $0 || curl --output /dev/null --silent --head --fail --write-out "%{url_effective}: %{http_code}\n" $0'
+          grep -r -Eoh "(https?://github.com/[a-zA-Z0-9./?=_%:-]*)" $GITHUB_WORKSPACE | sort -u | grep -v "://github.com/clearml/clearml-docs" | xargs -n 1 sh -c 'curl --output /dev/null --silent --head --fail $0 || curl --output /dev/null --silent --head --fail --write-out "%{url_effective}: %{http_code}\n" $0'

.github/workflows/test.yml

@@ -25,7 +25,7 @@ jobs:
         env:
           INCOMING_WEBHOOK_URL: ${{ secrets.SLACK_WEBHOOK }}
         with:
-          text: Link Checker failure in github.com/allegroai/clearl-docs
+          text: Link Checker failure in github.com/clearml/clearl-docs
           blocks: |
             [
               {"type": "section", "text": {"type": "mrkdwn", "text": "Testing!"}}

docs/apps/clearml_task.md

@@ -11,7 +11,7 @@ line arguments, Python module dependencies, and a requirements.txt file!
 
 ## What Is ClearML Task For?
 * Launching off-the-shelf code on a remote machine with dedicated resources (e.g. GPU)
-* Running [hyperparameter optimization](../hpo.md) on a codebase that is still not in ClearML
+* Running [hyperparameter optimization](../getting_started/hpo.md) on a codebase that is still not in ClearML
 * Creating a pipeline from an assortment of scripts, that you need to turn into ClearML tasks
 * Running some code on a remote machine, either using an on-prem cluster or on the cloud
 

docs/best_practices/data_best_practices.md

@@ -55,5 +55,5 @@ serves as a dataset's single point of truth, you can schedule a script which use
 will update the dataset based on the modifications made to the folder. This way, there is no need to manually modify a dataset. 
 This functionality will also track the modifications made to a folder.
 
-See the sync function with the [CLI](clearml_data_cli.md#sync) or [SDK](clearml_data_sdk.md#syncing-local-storage)
+See the sync function with the [CLI](../clearml_data/clearml_data_cli.md#sync) or [SDK](../clearml_data/clearml_data_sdk.md#syncing-local-storage)
 interface. 

docs/best_practices/data_scientist_best_practices.md

@@ -24,12 +24,12 @@ During early stages of model development, while code is still being modified hea
 These setups can be folded into each other and that's great! If you have a GPU machine for each researcher, that's awesome! 
 The goal of this phase is to get a code, dataset, and environment set up, so you can start digging to find the best model!
 
-- [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out [ClearML Setup](../../clearml_sdk/clearml_sdk_setup.md)). 
+- [ClearML SDK](../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out [ClearML Setup](../clearml_sdk/clearml_sdk_setup.md)). 
   This helps visualizing the results and tracking progress.
-- [ClearML Agent](../../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time, 
+- [ClearML Agent](../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time, 
   while also creating an easy queue interface that easily lets you drop your tasks to be executed one by one
   (great for ensuring that the GPUs are churning during the weekend).
-- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, in the same way that you'd develop on your local laptop!
+- [ClearML Session](../apps/clearml_session.md) helps with developing on remote machines, in the same way that you'd develop on your local laptop!
 
 ## Train Remotely
 
@@ -43,12 +43,12 @@ yields the best performing model for your task!
 
 Visualization and comparison dashboards keep your sanity at bay! At this stage you usually have a docker container with all the binaries 
 that you need. 
-- [ClearML SDK](../../clearml_sdk/clearml_sdk.md) ensures that all the metrics, parameters and Models are automatically logged and can later be 
-  accessed, [compared](../../webapp/webapp_exp_comparing.md) and [tracked](../../webapp/webapp_exp_track_visual.md).
-- [ClearML Agent](../../clearml_agent.md) does the heavy lifting. It reproduces the execution environment, clones your code, 
+- [ClearML SDK](../clearml_sdk/clearml_sdk.md) ensures that all the metrics, parameters and Models are automatically logged and can later be 
+  accessed, [compared](../webapp/webapp_exp_comparing.md) and [tracked](../webapp/webapp_exp_track_visual.md).
+- [ClearML Agent](../clearml_agent.md) does the heavy lifting. It reproduces the execution environment, clones your code, 
   applies code patches, manages parameters (including overriding them on the fly), executes the code, and queues multiple tasks.
   It can even [build](../../clearml_agent/clearml_agent_docker_exec#exporting-a-task-into-a-standalone-docker-container) the docker container for you!  
-- [ClearML Pipelines](../../pipelines/pipelines.md) ensure that steps run in the same order, 
+- [ClearML Pipelines](../pipelines/pipelines.md) ensure that steps run in the same order, 
   programmatically chaining tasks together, while giving an overview of the execution pipeline's status.
 
 **Your entire environment should magically be able to run on any machine, without you working hard.** 

docs/best_practices/mlops_best_practices.md

@@ -7,10 +7,10 @@ From training models to data processing to deploying to production.
 
 ## Development - Preparing for Automation
 Basically, track everything. There is nothing that is not worth having visibility to.
-If you are afraid of clutter, use the archive option, and set up your own [cleanup service](../../guides/services/cleanup_service.md).
+If you are afraid of clutter, use the archive option, and set up your own [cleanup service](../guides/services/cleanup_service.md).
 
 - Track the code base. There is no reason not to add metrics to any process in your workflow, even if it is not directly ML. Visibility is key to iterative improvement of your code / workflow.
-- Create per-project [leaderboards](../../guides/ui/building_leader_board.md) based on custom columns 
+- Create per-project [leaderboards](../guides/ui/building_leader_board.md) based on custom columns 
   (hyperparameters and performance accuracy), and bookmark them (full URL will always reproduce the same view and table).
 - Share tasks with your colleagues and team-leaders. 
   Invite more people to see how your project is progressing, and suggest they add metric reporting for their own.
@@ -19,22 +19,22 @@ If you are afraid of clutter, use the archive option, and set up your own [clean
 ## Clone Tasks
 Define a ClearML Task with one of the following options:
 - Run the actual code with the `Task.init()` call. This will create and auto-populate the Task in CleaML (including Git Repo / Python Packages / Command line etc.).
-- Register local / remote code repository with `clearml-task`. See [details](../../apps/clearml_task.md).
+- Register local / remote code repository with `clearml-task`. See [details](../apps/clearml_task.md).
 
-Once you have a Task in ClearML, you can clone and edit its definitions in the UI, then launch it on one of your nodes with [ClearML Agent](../../clearml_agent.md).
+Once you have a Task in ClearML, you can clone and edit its definitions in the UI, then launch it on one of your nodes with [ClearML Agent](../clearml_agent.md).
 
 ## Advanced Automation
 - Create daily / weekly cron jobs for retraining best performing models on.
 - Create data monitoring & scheduling and launch inference jobs to test performance on any new coming dataset.
-- Once there are two or more tasks that run after another, group them together into a [pipeline](../../pipelines/pipelines.md).
+- Once there are two or more tasks that run after another, group them together into a [pipeline](../pipelines/pipelines.md).
 
 ## Manage Your Data
-Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running tasks for easy reproduction.
+Use [ClearML Data](../clearml_data/clearml_data.md) to version your data, then link it to running tasks for easy reproduction.
 Make datasets machine agnostic (i.e. store original dataset in a shared storage location, e.g. shared-folder / S3 / Gs / Azure).
 ClearML Data supports efficient Dataset storage and caching, differentiable and compressed.
 
 ## Scale Your Work
-Use [ClearML Agent](../../clearml_agent.md) to scale work. Install the agent machines (remote or local) and manage
+Use [ClearML Agent](../clearml_agent.md) to scale work. Install the agent machines (remote or local) and manage
 training workload with it. 
 
 Improve team collaboration by transparent resource monitoring, always know what is running where.

docs/clearml_data/clearml_data.md

@@ -46,7 +46,7 @@ ClearML Data supports two interfaces:
 - `clearml-data` - A CLI utility for creating, uploading, and managing datasets. See [CLI](clearml_data_cli.md) for a reference of `clearml-data` commands.
 - `clearml.Dataset` - A Python interface for creating, retrieving, managing, and using datasets. See [SDK](clearml_data_sdk.md) for an overview of the basic methods of the `Dataset` module.
 
-For an overview of recommendations for ClearML Data workflows and practices, see [Best Practices](best_practices.md).
+For an overview of recommendations for ClearML Data workflows and practices, see [Best Practices](../best_practices/data_best_practices.md).
 
 ## Dataset Version States
 The following table displays the possible states for a dataset version. 

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_base_docker.md)).
+override the autoscaler’s default docker image (see [Base Docker Image](../getting_started/clearml_agent_docker_exec.md#base-docker-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/fundamentals/agents_and_queues.md

@@ -17,7 +17,7 @@ from installing required packages to setting environment variables,
 all leading to executing the code (supporting both virtual environment or flexible docker container configurations).
 
 The agent also supports overriding parameter values on-the-fly without code modification, thus enabling no-code experimentation (this is also the foundation on which 
-ClearML [Hyperparameter Optimization](../hpo.md) is implemented).  
+ClearML [Hyperparameter Optimization](../getting_started/hpo.md) is implemented).  
 
 An agent can be associated with specific GPUs, enabling workload distribution. For example, on a machine with 8 GPUs you 
 can allocate several GPUs to an agent and use the rest for a different workload, even through another agent (see [Dynamic GPU Allocation](../clearml_agent/clearml_agent_dynamic_gpus.md)).   

docs/fundamentals/hyperparameters.md

@@ -6,7 +6,7 @@ Hyperparameters are a script's configuration options. Since hyperparameters can
 model performance, it is crucial to efficiently track and manage them.
 
 ClearML supports tracking and managing hyperparameters in each task and provides a dedicated [hyperparameter 
-optimization module](../hpo.md). With ClearML's logging and tracking capabilities, tasks can be reproduced, and their 
+optimization module](../getting_started/hpo.md). With ClearML's logging and tracking capabilities, tasks can be reproduced, and their 
 hyperparameters and results can be saved and compared, which is key to understanding model behavior.
 
 ClearML lets you easily try out different hyperparameter values without changing your original code. ClearML's [execution

docs/fundamentals/task.md

@@ -124,7 +124,7 @@ Available task types are:
 * *inference* - Model inference job (e.g. offline / batch model execution)
 * *controller* - A task that lays out the logic for other tasks' interactions, manual or automatic (e.g. a pipeline 
   controller) 
-* *optimizer* - A specific type of controller for optimization tasks (e.g. [hyperparameter optimization](../hpo.md))
+* *optimizer* - A specific type of controller for optimization tasks (e.g. [hyperparameter optimization](../getting_started/hpo.md))
 * *service* - Long lasting or recurring service (e.g. server cleanup, auto ingress, sync services etc.)
 * *monitor* - A specific type of service for monitoring
 * *application* - A task implementing custom applicative logic, like [autoscaler](../guides/services/aws_autoscaler.md) 

docs/getting_started/hpo.md

@@ -13,7 +13,7 @@ solutions to boost hyperparameter optimization efficiency.
 
 ## Workflow
 
-![Hyperparameter optimization diagram](img/hpo_diagram.png)
+![Hyperparameter optimization diagram](../img/hpo_diagram.png)
 
 The preceding diagram demonstrates the typical flow of hyperparameter optimization where the parameters of a base task are optimized: 
 
@@ -27,8 +27,8 @@ The preceding diagram demonstrates the typical flow of hyperparameter optimizati
 ## ClearML Solutions 
 
 ClearML offers three solutions for hyperparameter optimization:
-* [GUI application](webapp/applications/apps_hpo.md): The Hyperparameter Optimization app allows you to run and manage the optimization tasks 
+* [GUI application](../webapp/applications/apps_hpo.md): The Hyperparameter Optimization app allows you to run and manage the optimization tasks 
   directly from the web interface--no code necessary (available under the ClearML Pro plan).
-* [Command-Line Interface (CLI)](apps/clearml_param_search.md): The `clearml-param-search` CLI tool enables you to configure and launch the optimization process from your terminal.
-* [Python Interface](clearml_sdk/hpo_sdk.md): The `HyperParameterOptimizer` class within the ClearML SDK allows you to 
+* [Command-Line Interface (CLI)](../apps/clearml_param_search.md): The `clearml-param-search` CLI tool enables you to configure and launch the optimization process from your terminal.
+* [Python Interface](../clearml_sdk/hpo_sdk.md): The `HyperParameterOptimizer` class within the ClearML SDK allows you to 
   configure and launch optimization tasks, and seamlessly integrate them in your existing model training tasks. 

docs/getting_started/main.md

@@ -112,7 +112,7 @@ alert you whenever your model improves in accuracy)
 - Automatically scale cloud instances according to your resource needs with ClearML's 
 [AWS Autoscaler](../webapp/applications/apps_aws_autoscaler.md) and [GCP Autoscaler](../webapp/applications/apps_gcp_autoscaler.md)
 GUI applications
-- Run [hyperparameter optimization](../hpo.md) 
+- Run [hyperparameter optimization](hpo.md) 
 - Build [pipelines](../pipelines/pipelines.md) from code 
 - Much more!
 

docs/integrations/catboost.md

@@ -117,5 +117,5 @@ 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/integrations/keras.md

@@ -129,5 +129,5 @@ 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/integrations/lightgbm.md

@@ -118,5 +118,5 @@ 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/integrations/megengine.md

@@ -114,5 +114,5 @@ 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/integrations/optuna.md

@@ -2,7 +2,7 @@
 title: Optuna
 ---
 
-[Optuna](https://optuna.readthedocs.io/en/latest) is a [hyperparameter optimization](../hpo.md) framework, 
+[Optuna](https://optuna.readthedocs.io/en/latest) is a [hyperparameter optimization](../getting_started/hpo.md) framework, 
 which makes use of different samplers such as grid search, random, bayesian, and evolutionary algorithms. You can integrate
 Optuna into ClearML's automated hyperparameter optimization. 
 

docs/integrations/tensorflow.md

@@ -131,5 +131,5 @@ 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/integrations/transformers.md

@@ -90,5 +90,5 @@ The ClearML Agent executing the task will use the new values to [override any ha
 
 ## 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/integrations/xgboost.md

@@ -144,5 +144,5 @@ 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/integrations/yolov5.md

@@ -7,7 +7,7 @@ built in logger:
 * Track every YOLOv5 training run in ClearML
 * Version and easily access your custom training data with [ClearML Data](../clearml_data/clearml_data.md) 
 * Remotely train and monitor your YOLOv5 training runs using [ClearML Agent](../clearml_agent.md)
-* Get the very best mAP using ClearML [Hyperparameter Optimization](../hpo.md)
+* Get the very best mAP using ClearML [Hyperparameter Optimization](../getting_started/hpo.md)
 * Turn your newly trained YOLOv5 model into an API with just a few commands using [ClearML Serving](../clearml_serving/clearml_serving.md)
 
 ## Setup

docs/webapp/webapp_exp_track_visual.md

@@ -93,7 +93,7 @@ using to set up an environment (`pip` or `conda`) are available. Select which re
 
 ### Container
 The Container section list the following information:
-* Image - a pre-configured container that ClearML Agent will use to remotely execute this task (see [Building Docker containers](../clearml_agent/clearml_agent_docker_exec))
+* Image - a pre-configured container that ClearML Agent will use to remotely execute this task (see [Building Docker containers](../getting_started/clearml_agent_docker_exec.md))
 * Arguments - add container arguments
 * Setup shell script - a bash script to be executed inside the container before setting up the task's environment
 

docs/webapp/webapp_exp_tuning.md

@@ -72,7 +72,7 @@ and/or Reset functions.
 
 
 #### Default Container
-Select a pre-configured container that the [ClearML Agent](../clearml_agent.md) will use to remotely execute this task (see [Building Docker containers](../clearml_agent/clearml_agent_docker_exec)).
+Select a pre-configured container that the [ClearML Agent](../clearml_agent.md) will use to remotely execute this task (see [Building Docker containers](../getting_started/clearml_agent_docker_exec.md)).
 
 **To add, change, or delete a default container:**
 

docusaurus.config.js

@@ -68,7 +68,7 @@ module.exports = {
     },
     announcementBar: {
       id: 'supportus',
-      content: 'If you ❤️ ️<b>ClearML</b>, ⭐️ us on <a target="_blank" rel="noopener noreferrer" href="https://github.com/allegroai/clearml">GitHub</a>!',
+      content: 'If you ❤️ ️<b>ClearML</b>, ⭐️ us on <a target="_blank" rel="noopener noreferrer" href="https://github.com/clearml/clearml">GitHub</a>!',
       isCloseable: true,
     },
     navbar: {
@@ -85,48 +85,61 @@ module.exports = {
           to: '/docs/',
           label: 'Overview',
           position: 'left',
+          activeBaseRegex: '^/docs/latest/docs/(fundamentals/agents_and_queues|hyper_datasets|clearml_agent(/(clearml_agent_dynamic_gpus|clearml_agent_fractional_gpus)?|)?|cloud_autoscaling/autoscaling_overview|remote_session|model_registry|deploying_clearml/enterprise_deploy/appgw|build_interactive_models|deploying_models|custom_apps)?$',
         },
         {
-          to: '/docs/deploying_clearml/clearml_server',
+          to: '/docs/clearml_sdk/clearml_sdk_setup',
           label: 'Setup',
-          position: 'left'
+          position: 'left',
+          activeBaseRegex: '^/docs/latest/docs/(deploying_clearml(?!/enterprise_deploy/appgw(/.*)?$)(/.*)?$|clearml_sdk/clearml_sdk_setup|user_management(/.*)?|clearml_agent/(clearml_agent_setup|clearml_agent_deployment_bare_metal|clearml_agent_deployment_k8s|clearml_agent_deployment_slurm|clearml_agent_execution_env|clearml_agent_env_caching|clearml_agent_services_mode)|integrations/storage)/?$',
         },
         {
           to: '/docs/getting_started/auto_log_exp',
           label: 'Using ClearML',
-          position: 'left'
+          position: 'left',
+          activeBaseRegex: '^/docs/latest/docs/(getting_started(?!/video_tutorials(/.*)?)|clearml_serving|apps/clearml_session)(/.*)?$',
         },
         {
           label: 'Developer Center',
           position: 'left', // or 'right'
+          to: '/docs/fundamentals/projects',
+          activeBaseRegex: '^/docs/latest/docs/(fundamentals(?!/agents_and_queues)(/.*)?|configs/configuring_clearml|getting_started/video_tutorials(/.*)?|clearml_sdk(?!/clearml_sdk_setup)(/.*)?|pipelines(/.*)?|hyperdatasets(/.*)?|clearml_data(/.*)?|hyperdatasets(/webapp)(/.*)?|references(/.*)?|webapp(/.*)?|clearml_agent/(clearml_agent_ref|clearml_agent_env_var)(/.*)?|configs/(clearml_conf|env_vars)(/.*)?|apps/(clearml_task|clearml_param_search)(/.*)?|best_practices(/.*)?|guides(/.*)?|integrations(/.*)?|faq|release_notes(/.*)?)$',
+          activeClassName: 'navbar__link--active',
           items: [
             {
               label: 'ClearML Basics',
               to: '/docs/fundamentals/projects',
+              activeBaseRegex: '^/docs/latest/docs/(fundamentals|getting_started/video_tutorials|clearml_sdk(/(?!clearml_sdk_setup).*|(?=/))?|pipelines|clearml_data|hyperdatasets/(?!webapp/).*)(/.*)?$',
             },
             {
               label: 'References',
               to: '/docs/references/sdk/task',
+              activeBaseRegex: '^/docs/latest/docs/(references/|webapp/.*|hyperdatasets/webapp/.*|clearml_agent/(clearml_agent_ref|clearml_agent_env_var)|configs/(clearml_conf|env_vars)|apps/(clearml_task|clearml_param_search))(/.*)?$',
             },
             {
               label: 'Best Practices',
-              to: '/docs/getting_started/ds/best_practices'
+              to: 'docs/best_practices/data_scientist_best_practices',
+              activeBaseRegex: '^/docs/latest/docs/best_practices/'
             },
             {
               label: 'Tutorials',
               to: '/docs/guides',
+              activeBaseRegex: '^/docs/latest/docs/guides',
             },
             {
               label: 'Integrations',
-              to: '/docs/integrations'
+              to: '/docs/integrations',
+              activeBaseRegex: '^/docs/latest/docs/integrations(?!/storage)',
             },
             {
               label: 'FAQ',
               to: '/docs/faq',
+              activeBaseRegex: '^/docs/latest/docs/faq$',
             },
             {
               label: 'Release Notes',
               to: '/docs/release_notes/clearml_server/open_source/ver_2_0',
+              activeBaseRegex: '^/docs/latest/docs/release_notes/',
             },
 
           ],
@@ -134,7 +147,7 @@ module.exports = {
         {
           label: 'Community Resources',
           position: 'left', // or 'right'
-          to: '/docs/community',
+          to: '/docs/latest/docs/community',
         },
         {
           href: 'https://joinslack.clear.ml',
@@ -155,7 +168,7 @@ module.exports = {
           'aria-label': 'Twitter',
         },
         {
-          href: 'https://github.com/allegroai/clearml',
+          href: 'https://github.com/clearml/clearml',
           position: 'right',
           className: 'header-ico header-ico--github',
           'aria-label': 'GitHub repository',
@@ -202,7 +215,7 @@ module.exports = {
             },
             {
               label: 'GitHub',
-              href: 'https://github.com/allegroai/clearml',
+              href: 'https://github.com/clearml/clearml',
             },
           ],
         },
@@ -220,13 +233,13 @@ module.exports = {
           // Please change this to your repo.
           breadcrumbs: false,
           editUrl:
-            'https://github.com/allegroai/clearml-docs/edit/main/',
+            'https://github.com/clearml/clearml-docs/edit/main/',
         },
         // API: {
           // sidebarPath: require.resolve('./sidebars.js'),
           // // Please change this to your repo.
           // editUrl:
-            // 'https://github.com/allegroai/clearml-docs/edit/main/',
+            // 'https://github.com/clearml/clearml-docs/edit/main/',
         // },
         blog: {
           blogTitle: 'ClearML Tutorials',
@@ -236,7 +249,7 @@ module.exports = {
           showReadingTime: true,
           // Please change this to your repo.
           editUrl:
-            'https://github.com/allegroai/clearml-docs/edit/main/tutorials/',
+            'https://github.com/clearml/clearml-docs/edit/main/tutorials/',
         },
         theme: {
           customCss: require.resolve('./src/css/custom.css'),

sidebars.js

@@ -60,10 +60,10 @@ module.exports = {
         'getting_started/data_management',
         'getting_started/remote_execution',
         'getting_started/building_pipelines',
-        'hpo',
-        'clearml_agent/clearml_agent_docker_exec',
-        'clearml_agent/clearml_agent_base_docker',
-        'clearml_agent/clearml_agent_scheduling',
+        'getting_started/hpo',
+        'getting_started/clearml_agent_docker_exec',
+        'getting_started/clearml_agent_base_docker',
+        'getting_started/clearml_agent_scheduling',
         {"Deploying Model Endpoints": [
             {
                 type: 'category',
@@ -688,17 +688,17 @@ module.exports = {
                 {
                     type: 'doc',
                     label: 'Data Scientists',
-                    id: 'getting_started/ds/best_practices'
+                    id: 'best_practices/data_scientist_best_practices'
                 },
                 {
                     type: 'doc',
                     label: 'MLOps and LLMOps',
-                    id: 'getting_started/mlops/mlops_best_practices'
+                    id: 'best_practices/mlops_best_practices'
                 },
                 {
                     type: 'doc',
                     label: 'Data Management',
-                    id: 'clearml_data/best_practices'
+                    id: 'best_practices/data_best_practices'
                 },
             ],
         },