Update docs for 2.2

docs/source/guide/explanation/algorithms/action/index.rst

@@ -6,4 +6,3 @@ Action Recognition
 
 
    action_classification
-   action_detection

docs/source/guide/get_started/cli_commands.rst

@@ -339,11 +339,11 @@ The results will be saved in ``./otx-workspace/`` folder by default. The output
 
             (otx) ...$ otx train --model <model-class-path-or-name> --task <task-type> --data_root <dataset-root>
 
-        For example, if you want to use the ``otx.algo.detection.atss.ATSS`` model class, you can train it as shown below.
+        For example, if you want to use the ``otx.algo.classification.torchvision_model.TVModelForMulticlassCls`` model class, you can train it as shown below.
 
         .. code-block:: shell
 
-            (otx) ...$ otx train --model otx.algo.detection.atss.ATSS --model.variant mobilenetv2 --task DETECTION ...
+            (otx) ...$ otx train --model otx.algo.classification.torchvision_model.TVModelForMulticlassCls --model.backbone mobilenet_v3_small ...
 
 .. note::
     You also can visualize the training using ``Tensorboard`` as these logs are located in ``<work_dir>/tensorboard``.

docs/source/guide/get_started/installation.rst

@@ -68,7 +68,7 @@ according to your system environment.
 
 .. note::
 
-    Currently, only torch==2.1.1 was fully validated. (older versions are not supported due to security issues).
+    Currently, only torch==2.2 was fully validated. (older versions are not supported due to security issues).
 
 
 3. Once the package is installed in the virtual environment, you can use full

docs/source/guide/tutorials/advanced/index.rst

@@ -8,5 +8,7 @@ Advanced Tutorials
    semi_supervised_learning
    huggingface_model
    multi_gpu
+   low_rank_adaptation
+   torch_compile
 
 .. Once we have enough material, we might need to categorize these into `data`, `model learning` sections.

docs/source/guide/tutorials/advanced/low_rank_adaptation.rst

@@ -0,0 +1,39 @@
+LoRA: Low Rank Adaptation for Classification Tasks
+===================================================
+
+.. note::
+
+    LoRA is only supported for VisionTransformer models.
+    See the model in otx.algo.classification.vit.
+
+Overview
+--------
+
+OpenVINO™ Training Extensions now supports Low Rank Adaptation (LoRA) for classification tasks using Transformer models. 
+LoRA is a parameter-efficient approach to adapt pre-trained models by introducing low-rank matrices that capture important adaptations without the need to retrain the entire model.
+
+Benefits of LoRA
+----------------
+
+- **Efficiency**: LoRA allows for efficient adaptation of large pre-trained models with minimal additional parameters.
+- **Performance**: By focusing on key parameters, LoRA can achieve competitive performance with less computational overhead.
+- **Flexibility**: LoRA can be applied to various parts of the transformer model, providing flexibility in model tuning.
+
+How to Use LoRA in OpenVINO™ Training Extensions
+------------------------------------------------
+
+.. tab-set::
+
+    .. tab-item:: API
+
+        .. code-block:: python
+
+            from otx.algo.classification.vit import VisionTransformerForMulticlassCls
+
+            model = VisionTransformerForMulticlassCls(..., lora=True)
+
+    .. tab-item:: CLI
+
+        .. code-block:: bash
+
+            (otx) ...$ otx train ... --model.lora True

docs/source/guide/tutorials/advanced/torch_compile.rst

@@ -0,0 +1,41 @@
+[BETA] Enable torch.compile
+============================
+
+.. warning::
+    Not currently supported on all models.
+    As far as we check, it is available for Classification Task models and some segmentation models.
+    We will continue to optimize this and do not guarantee performance for now.
+
+Overview
+--------
+
+OpenVINO™ Training Extensions now integrates the `torch.compile` feature from PyTorch, allowing users to optimize their models for better performance.
+This feature compiles the model's operations into optimized lower-level code, which can significantly improve execution speed and reduce memory usage.
+
+Benefits of torch.compile
+-------------------------
+
+- **Performance Optimization**: Compiled models run faster by executing optimized low-level operations.
+- **Reduced Memory Footprint**: Optimized models can use less memory, which is beneficial for deploying models on resource-constrained devices.
+For more information on the benefits of `torch.compile`, refer to the official `PyTorch documentation <https://pytorch.org/docs/stable/generated/torch.compile.html>`_.
+
+How to Use torch.compile in OpenVINO™ Training Extensions
+----------------------------------------------------------
+
+**Prepare OTXModel**: Ensure that model is compatible with `torch.compile`. When building the model, give the `torch_compile` option `True`.
+
+.. tab-set::
+
+    .. tab-item:: API
+
+        .. code-block:: python
+
+            from otx.algo.classification.vit import VisionTransformerForMulticlassCls
+
+            model = VisionTransformerForMulticlassCls(..., torch_compile=True)
+
+    .. tab-item:: CLI
+
+        .. code-block:: bash
+
+            (otx) ...$ otx train ... --model.torch_compile True