Restructure docs for platform components and use case clarity (#1048)

docs/integrations/accelerate.md

@@ -9,7 +9,7 @@ such as required packages and uncommitted changes, and supports reporting scalar
 
 ## Setup
 
-To use Accelerate's ClearML tracker, make sure that `clearml` is [installed and set up](../getting_started/ds/ds_first_steps.md#install-clearml) 
+To use Accelerate's ClearML tracker, make sure that `clearml` is [installed and set up](../clearml_sdk/clearml_sdk_setup#install-clearml) 
 in your environment, and use the `log_with` parameter when instantiating an `Accelerator`:
 
 ```python

docs/integrations/autokeras.md

@@ -3,7 +3,7 @@ title: AutoKeras
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+If you are not already using ClearML, see [Getting Started](../clearml_sdk/clearml_sdk_setup) for setup 
 instructions.
 :::
 

docs/integrations/catboost.md

@@ -3,7 +3,7 @@ title: CatBoost
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+If you are not already using ClearML, see [ClearML Setup](../clearml_sdk/clearml_sdk_setup) for setup 
 instructions.
 :::
 
@@ -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](../fundamentals/hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../hpo.md) 
 for more information.

docs/integrations/click.md

@@ -3,7 +3,7 @@ title: Click
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
+If you are not already using ClearML, see [ClearML Setup](../clearml_sdk/clearml_sdk_setup) for setup 
 instructions.
 :::
 

docs/integrations/fastai.md

@@ -3,8 +3,7 @@ title: Fast.ai
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
 ClearML integrates seamlessly with [fast.ai](https://www.fast.ai/), automatically logging its models and scalars. 

docs/integrations/hydra.md

@@ -3,8 +3,7 @@ title: Hydra
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
 

docs/integrations/ignite.md

@@ -3,8 +3,7 @@ title: PyTorch Ignite
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
 [PyTorch Ignite](https://pytorch.org/ignite/index.html) is a library for training and evaluating neural networks in 

docs/integrations/integrations.md

@@ -0,0 +1,37 @@
+# ClearML Integrations
+
+ClearML seamlessly integrates with a wide range of popular machine learning frameworks, tools, and platforms to enhance your ML development workflow. Our integrations enable automatic experiment tracking, model management, and pipeline orchestration across your preferred tools.
+
+### Deep Learning Frameworks
+* PyTorch
+* TensorFlow
+* Keras
+* Keras Tuner
+* MONAI
+* FastAI
+
+### ML Frameworks and Tools
+* scikit-learn
+* XGBoost
+* LightGBM
+* CatBoost
+* Optuna
+* Matplotlib
+* Seaborn
+
+### AutoML and Optimization
+* AutoKeras
+* Hydra
+* Python Fire
+
+### MLOps and Visualization
+* TensorBoard
+* TensorBoardX
+* OpenMMLab
+* Nvidia TAO
+
+### Production and Deployment
+* YOLO v5
+* YOLO v8
+* jsonargparse
+* LangChain

docs/integrations/jsonargparse.md

@@ -3,11 +3,11 @@ title: jsonargparse
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
 
+
 [jsonargparse](https://github.com/omni-us/jsonargparse) is a Python package for creating command-line interfaces. 
 ClearML integrates seamlessly with `jsonargparse` and automatically logs its command-line parameters and connected 
 configuration files.

docs/integrations/keras.md

@@ -3,10 +3,10 @@ title: Keras
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 ClearML integrates with [Keras](https://keras.io/) out-of-the-box, automatically logging its models, scalars, 
 TensorFlow definitions, and TensorBoard outputs. 
 
@@ -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](../fundamentals/hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../hpo.md) 
 for more information.

docs/integrations/keras_tuner.md

@@ -3,10 +3,10 @@ title: Keras Tuner
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner) is a library that helps you pick the optimal set 
 of hyperparameters for training your models. ClearML integrates seamlessly with `kerastuner` and automatically logs 
 task scalars, the output model, and hyperparameter optimization summary. 

docs/integrations/langchain.md

@@ -3,10 +3,10 @@ title: LangChain
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [LangChain](https://github.com/langchain-ai/langchain) is a popular framework for developing applications powered by 
 language models. You can integrate ClearML into your LangChain code using the built-in `ClearMLCallbackHandler`. This 
 class is used to create a ClearML Task to log LangChain assets and metrics. 

docs/integrations/lightgbm.md

@@ -3,10 +3,10 @@ title: LightGBM
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 ClearML integrates seamlessly with [LightGBM](https://github.com/microsoft/LightGBM), automatically logging its models, 
 metric plots, and parameters. 
 
@@ -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](../fundamentals/hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../hpo.md) 
 for more information.

docs/integrations/matplotlib.md

@@ -3,10 +3,10 @@ title: Matplotlib
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [Matplotlib](https://matplotlib.org/) is a Python library for data visualization. ClearML automatically captures plots 
 and images created with `matplotlib`. 
 

docs/integrations/megengine.md

@@ -3,10 +3,10 @@ title: MegEngine
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 ClearML integrates seamlessly with [MegEngine](https://github.com/MegEngine/MegEngine), automatically logging its models. 
 
 All you have to do is simply add two lines of code to your MegEngine script:
@@ -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](../fundamentals/hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../hpo.md) 
 for more information.

docs/integrations/mmcv.md

@@ -7,10 +7,10 @@ title: MMCV v1.x
 :::
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [MMCV](https://github.com/open-mmlab/mmcv/tree/1.x) is a computer vision framework developed by OpenMMLab. You can integrate ClearML into your 
 code using the `mmcv` package's [`ClearMLLoggerHook`](https://mmcv.readthedocs.io/en/master/_modules/mmcv/runner/hooks/logger/clearml.html)
 class. This class is used to create a ClearML Task and to automatically log metrics. 

docs/integrations/mmengine.md

@@ -3,10 +3,10 @@ title: MMEngine
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [MMEngine](https://github.com/open-mmlab/mmengine) is a library for training deep learning models based on PyTorch. 
 MMEngine supports ClearML through a builtin logger: It automatically logs task environment information, such as 
 required packages and uncommitted changes, and supports reporting scalars, parameters, and debug samples.

docs/integrations/monai.md

@@ -3,10 +3,10 @@ title: MONAI
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [MONAI](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare 
 imaging. You can integrate ClearML into your code using MONAI's built-in handlers: [`ClearMLImageHandler`, `ClearMLStatsHandler`](#clearmlimagehandler-and-clearmlstatshandler), 
 and [`ModelCheckpoint`](#modelcheckpoint). 

docs/integrations/optuna.md

@@ -2,7 +2,7 @@
 title: Optuna
 ---
 
-[Optuna](https://optuna.readthedocs.io/en/latest) is a [hyperparameter optimization](../fundamentals/hpo.md) framework, 
+[Optuna](https://optuna.readthedocs.io/en/latest) is a [hyperparameter optimization](../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/pytorch.md

@@ -3,10 +3,10 @@ title: PyTorch
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 ClearML integrates seamlessly with [PyTorch](https://pytorch.org/), automatically logging its models. 
 
 All you have to do is simply add two lines of code to your PyTorch script:

docs/integrations/pytorch_lightning.md

@@ -3,10 +3,10 @@ title: PyTorch Lightning
 --- 
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [PyTorch Lightning](https://github.com/Lightning-AI/lightning) is a framework that simplifies the process of training and deploying PyTorch models. ClearML seamlessly 
 integrates with PyTorch Lightning, automatically logging PyTorch models, parameters supplied by [LightningCLI](https://lightning.ai/docs/pytorch/stable/cli/lightning_cli.html), 
 and more. 
@@ -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](../fundamentals/hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../hpo.md) 
 for more information.
 

docs/integrations/scikit_learn.md

@@ -3,10 +3,10 @@ title: scikit-learn
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 ClearML integrates seamlessly with [scikit-learn](https://scikit-learn.org/stable/), automatically logging models created
 with `joblib`.
 

docs/integrations/seaborn.md

@@ -3,10 +3,10 @@ title: Seaborn
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [seaborn](https://seaborn.pydata.org/) is a Python library for data visualization. 
 ClearML automatically captures plots created using `seaborn`. All you have to do is add two
 lines of code to your script:

docs/integrations/tensorboard.md

@@ -3,9 +3,10 @@ title: TensorBoard
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md).
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 [TensorBoard](https://www.tensorflow.org/tensorboard) is TensorFlow's data visualization toolkit. 
 ClearML automatically captures all data logged to TensorBoard. All you have to do is add two
 lines of code to your script:

docs/integrations/tensorboardx.md

@@ -3,7 +3,7 @@ title: TensorboardX
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md).
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
 [TensorboardX](https://tensorboardx.readthedocs.io/en/latest/tutorial.html#what-is-tensorboard-x) is a data 

docs/integrations/tensorflow.md

@@ -3,10 +3,10 @@ title: TensorFlow
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
+
 ClearML integrates with [TensorFlow](https://www.tensorflow.org/) out-of-the-box, automatically logging its models, 
 definitions, scalars, as well as TensorBoard outputs. 
 
@@ -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](../fundamentals/hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../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](../fundamentals/hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../hpo.md) 
 for more information.

docs/integrations/xgboost.md

@@ -3,8 +3,7 @@ title: XGBoost
 ---
 
 :::tip
-If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup 
-instructions.
+If you are not already using ClearML, see [ClearML Setup instructions](../clearml_sdk/clearml_sdk_setup).
 :::
 
 ClearML integrates seamlessly with [XGBoost](https://xgboost.readthedocs.io/en/stable/), automatically logging its models,
@@ -145,5 +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](../fundamentals/hpo.md) 
+the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../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](../fundamentals/hpo.md)
+* Get the very best mAP using ClearML [Hyperparameter Optimization](../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