Update docs (#3238)

* Update docs for release

* Fix wrong contents

docs/source/guide/explanation/additional_features/tiling.rst

@@ -67,12 +67,6 @@ To enable tiling in OTX training, set ``data.config.tile_config.enable_tiler`` p
 
             (otx) ...$ otx train ... --data.config.tile_config.enable_tiler True
 
-.. note::
-
-    To learn how to deploy the trained model and run the exported demo, refer to :doc:`../../tutorials/base/deploy`.
-
-    To learn how to run the demo in CLI and visualize results, refer to :doc:`../../tutorials/base/demo`.
-
 
 Tile Size and Tile Overlap Optimization
 -----------------------------------------

docs/source/guide/explanation/product_design.rst

@@ -174,12 +174,77 @@ Authors: @wonjuleee @vinnamkim
 Entrypoint
 ~~~~~~~~~~
 
-TBD @samet-akcay @harimkang
 
-Intel Device Support
-~~~~~~~~~~~~~~~~~~~~
+1. **User Workflow for OpenVINO™ Training Extensions**
+
+   We defined the user workflow for the OpenVINO™ Training Extensions
+   before defining the entry points to provide to users. 
+
+   +----------------------------------------------------------------+
+   | |Definition of OpenVINO™ Training Extensions Workflow|         |
+   +================================================================+
+   | Figure 4. Definition of OpenVINO™ Training Extensions Workflow |
+   +----------------------------------------------------------------+
+
+   As shown above, the User will define the Dataset and the Task they want to solve.
+   OpenVINO™ Training Extensions will provide a model to solve that Problem.
+   You can use the built-in model or choose from several models.
+   Advanced Users can also import their own Custom Model.
+
+   The User will then define the Training Configuration and start the Training.
+   Basically, OpenVINO™ Training Extensions provide a recipe that allows user to run
+   the training and change the training parameters.
+
+   Users can also use HPO to help the machine find the optimal parameters.
+   After training, the user can evaluate the model and deploy it to the edge device.
+   We have defined this natural workflow, and the end result is that
+   the user is provided with a trained model, a model that is available on the edge device,
+   and an optimized model.
+
+2. **Designing Engine Classes for a Natural Workflow**
+
+   +--------------------------------+
+   | |Engine Class Diagram|         |
+   +================================+
+   | Figure 5. Engine Class Diagram |
+   +--------------------------------+
+
+   As shown in Figure 5, we have designed the Engine classes to provide a natural workflow.
+   In addition to the models and datamodules provided by OpenVINO™ Training Extensions's core,
+   Engine provides all the entry points that OpenVINO™ Training Extensions wants to provide through Engine.
+
+   By looking at the Engine class, users can see what OpenVINO™ Training Extensions is trying to provide and use it in a natural way.
+   User configure Model and Data, and train it using a Trainer called Engine.
+   The role of Model can be done in core's Model, and Dataset-related things can be done in Datamodule,
+   The rest, from training to deployment, will be handled by Engine.
+
+3. **Auto-Configurator to help novice users get started**
+
+   Users without typical Trainer experience, such as Lightning, struggle to configure models and data pipelines.
+   This stems from the structure of the Model, Datamodule, and Engine requiring separate configuration.
+   To minimize this difficulty, OpenVINO™ Training Extensions uses a feature called Auto-Configuration.
+   If the user does not provide the required input, whether it is a Model or Data pipeline, the Auto-Configurator fills in the gaps.
+   This allows users to easily use OpenVINO™ Training Extensions by only configuring the dataset.
+
+   .. tab-set::
+
+      .. tab-item:: API
+
+         .. code-block:: python
+
+            from otx.engine import Engine
+
+            engine = Engine(data_root="<path_to_data_root>")
+            engine.train()
+
+      .. tab-item:: CLI
+
+         .. code-block:: bash
+
+            (otx) ...$ otx train ... --data_root <path_to_data_root>
+
+Authors: @samet-akcay @harimkang
 
-TBD
 
 .. [1]
    Meijer, Erik, and Peter Drayton. “Static typing where possible,
@@ -210,3 +275,5 @@ TBD
 .. |Task-Data-Model| image:: ../../../utils/images/product_design/task_data_model.png
 .. |Reuse Model| image:: ../../../utils/images/product_design/reuse_model.png
 .. |Support Various Data Format| image:: ../../../utils/images/product_design/support_various_data_format.png
+.. |Definition of OpenVINO™ Training Extensions Workflow| image:: ../../../utils/images/product_design/otx_workflow.png
+.. |Engine Class Diagram| image:: ../../../utils/images/product_design/engine_diagram.png

docs/source/guide/tutorials/base/how_to_train/action_classification.rst

@@ -188,15 +188,14 @@ while training logs can be found in the ``{work_dir}/{timestamp}`` dir.
 .. code-block::
 
     otx-workspace
-    ├── outputs/
-        ├── 20240403_134256/
-            ├── csv/
-            ├── checkpoints/
-            |   └── epoch_*.pth
-            ├── tensorboard/
-            └── configs.yaml
-        └── .latest
-            └── train/
+    ├── 20240403_134256/
+        ├── csv/
+        ├── checkpoints/
+        |   └── epoch_*.pth
+        ├── tensorboard/
+        └── configs.yaml
+    └── .latest
+        └── train/
     ...
 
 The training time highly relies on the hardware characteristics, for example on 1 NVIDIA GeForce RTX 3090 the training took about 3 minutes.

docs/source/guide/tutorials/base/how_to_train/action_detection.rst

@@ -181,15 +181,14 @@ while training logs can be found in the ``{work_dir}/{timestamp}`` dir.
 .. code-block::
 
     otx-workspace
-    ├── outputs/
-        ├── 20240403_134256/
-            ├── csv/
-            ├── checkpoints/
-            |   └── epoch_*.pth
-            ├── tensorboard/
-            └── configs.yaml
-        └── .latest
-            └── train/
+    ├── 20240403_134256/
+        ├── csv/
+        ├── checkpoints/
+        |   └── epoch_*.pth
+        ├── tensorboard/
+        └── configs.yaml
+    └── .latest
+        └── train/
     ...
 
 The training time highly relies on the hardware characteristics, for example on 1 NVIDIA GeForce RTX 3090 the training took about 3 minutes.

docs/source/guide/tutorials/base/how_to_train/classification.rst

@@ -279,15 +279,14 @@ while training logs can be found in the ``{work_dir}/{timestamp}`` dir.
 .. code-block::
 
     otx-workspace
-    ├── outputs/
-        ├── 20240403_134256/
-            ├── csv/
-            ├── checkpoints/
-            |   └── epoch_*.pth
-            ├── tensorboard/
-            └── configs.yaml
-        └── .latest
-            └── train/
+    ├── 20240403_134256/
+        ├── csv/
+        ├── checkpoints/
+        |   └── epoch_*.pth
+        ├── tensorboard/
+        └── configs.yaml
+    └── .latest
+        └── train/
     ...
 
 The training time highly relies on the hardware characteristics, for example on 1 NVIDIA GeForce RTX 3090 the training took about 3 minutes.

docs/source/guide/tutorials/base/how_to_train/detection.rst

@@ -291,15 +291,14 @@ while training logs can be found in the ``{work_dir}/{timestamp}`` dir.
 .. code-block::
 
     otx-workspace
-    ├── outputs/
-        ├── 20240403_134256/
-            ├── csv/
-            ├── checkpoints/
-            |   └── epoch_*.pth
-            ├── tensorboard/
-            └── configs.yaml
-        └── .latest
-            └── train/
+    ├── 20240403_134256/
+        ├── csv/
+        ├── checkpoints/
+        |   └── epoch_*.pth
+        ├── tensorboard/
+        └── configs.yaml
+    └── .latest
+        └── train/
     ...
 
 The training time highly relies on the hardware characteristics, for example on 1 NVIDIA GeForce RTX 3090 the training took about 3 minutes.

docs/source/guide/tutorials/base/how_to_train/instance_segmentation.rst

@@ -305,15 +305,14 @@ while training logs can be found in the ``{work_dir}/{timestamp}`` dir.
 .. code-block::
 
   otx-workspace
-  └── outputs/
-      ├── 20240403_134256/
-      |   ├── csv/
-      |   ├── checkpoints/
-      |   |   └── epoch_*.pth
-      |   ├── tensorboard/
-      |   └── configs.yaml
-      └── .latest
-          └── train/
+    ├── 20240403_134256/
+    |   ├── csv/
+    |   ├── checkpoints/
+    |   |   └── epoch_*.pth
+    |   ├── tensorboard/
+    |   └── configs.yaml
+    └── .latest
+        └── train/
   ...
 
 After that, we have the PyTorch instance segmentation model trained with OpenVINO™ Training Extensions, which we can use for evaluation, export, optimization and deployment.

docs/source/guide/tutorials/base/how_to_train/semantic_segmentation.rst

@@ -50,13 +50,7 @@ The list of supported recipes for semantic segmentation is available with the co
 
   The characteristics and detailed comparison of the models could be found in :doc:`Explanation section <../../../explanation/algorithms/segmentation/semantic_segmentation>`.
 
-<<<<<<< HEAD
-  We also can modify the architecture of supported models with various backbones, please refer to the :doc:`advanced tutorial for model customization <../../advanced/backbones>`.
-
 .. tab-set::
-=======
-.. code-block::
->>>>>>> b55d82cf6f648c42b6b9e3a6c9b1c1e3dbe5d6c2
 
     .. tab-item:: CLI
 
@@ -98,7 +92,7 @@ The list of supported recipes for semantic segmentation is available with the co
           ]
           '''
 
-2.  On this step we will configure configuration 
+1.  On this step we will configure configuration 
 with:
 
 - all necessary configs for litehrnet_18
@@ -230,15 +224,14 @@ while training logs can be found in the ``{work_dir}/{timestamp}`` dir.
 .. code-block::
 
   otx-workspace
-  └── outputs/
-      ├── 20240403_134256/
-      |   ├── csv/
-      |   ├── checkpoints/
-      |   |   └── epoch_*.pth
-      |   ├── tensorboard/
-      |   └── configs.yaml
-      └── .latest
-          └── train/
+    ├── 20240403_134256/
+    |   ├── csv/
+    |   ├── checkpoints/
+    |   |   └── epoch_*.pth
+    |   ├── tensorboard/
+    |   └── configs.yaml
+    └── .latest
+        └── train/
   ...
 
 After that, we have the PyTorch instance segmentation model trained with OpenVINO™ Training Extensions, which we can use for evaluation, export, optimization and deployment.

docs/source/guide/tutorials/base/how_to_train/visual_prompting.rst

@@ -290,15 +290,14 @@ while training logs can be found in the ``{work_dir}/{timestamp}`` dir.
 .. code-block::
 
     otx-workspace
-    ├── outputs/
-        ├── 20240403_134256/
-            ├── csv/
-            ├── checkpoints/
-            |   └── epoch_*.pth
-            ├── tensorboard/
-            └── configs.yaml
-        └── .latest
-            └── train/
+    ├── 20240403_134256/
+        ├── csv/
+        ├── checkpoints/
+        |   └── epoch_*.pth
+        ├── tensorboard/
+        └── configs.yaml
+    └── .latest
+        └── train/
     ...
 
 The training time highly relies on the hardware characteristics, for example on 1 NVIDIA GeForce RTX 3090 the training took about 4 minutes.