Update release note and readme (#2637)

* update release note and readme

* remove package upload step on internal publish wf

.github/workflows/publish_internal.yml

@@ -60,15 +60,6 @@ jobs:
         with:
           text: ${{ github.ref }}
           regex: '^refs/heads/releases/[0-9]+\.[0-9]+\.[0-9]+(\.[0-9]+rc[0-9]+|rc[0-9]+)?$'
-      - name: Upload package distributions to github
-        if: ${{ steps.check-tag.outputs.match != '' }}
-        uses: svenstaro/upload-release-action@v2
-        with:
-          repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: dist/*
-          tag: ${{ github.ref }}
-          overwrite: true
-          file_glob: true
       - name: Check dist contents
         run: twine check dist/*
       - name: Publish package dist to internal PyPI

README.md

@@ -4,9 +4,9 @@
 
 ---
 
-[Key Features](#key-features)
-[Installation](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/get_started/installation.html)
-[Documentation](https://openvinotoolkit.github.io/training_extensions/1.4.2/index.html)
+[Key Features](#key-features) •
+[Installation](https://openvinotoolkit.github.io/training_extensions/1.4.4/guide/get_started/installation.html) •
+[Documentation](https://openvinotoolkit.github.io/training_extensions/1.4.4/index.html) •
 [License](#license)
 
 [![PyPI](https://img.shields.io/pypi/v/otx)](https://pypi.org/project/otx)
@@ -54,7 +54,7 @@ OpenVINO™ Training Extensions supports the following computer vision tasks:
 - **Action recognition** including action classification and detection
 - **Anomaly recognition** tasks including anomaly classification, detection and segmentation
 
-OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/explanation/algorithms/index.html):
+OpenVINO™ Training Extensions supports the [following learning methods](https://openvinotoolkit.github.io/training_extensions/1.4.4/guide/explanation/algorithms/index.html):
 
 - **Supervised**, incremental training, which includes class incremental scenario and contrastive learning for classification and semantic segmentation tasks
 - **Semi-supervised learning**
@@ -64,17 +64,17 @@ OpenVINO™ Training Extensions will provide the following features in coming re
 
 - **Distributed training** to accelerate the training process when you have multiple GPUs
 - **Half-precision training** to save GPUs memory and use larger batch sizes
-- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
+- Integrated, efficient [hyper-parameter optimization module (HPO)](https://openvinotoolkit.github.io/training_extensions/1.4.4/guide/explanation/additional_features/hpo.html). Through dataset proxy and built-in hyper-parameter optimizer, you can get much faster hyper-parameter optimization compared to other off-the-shelf tools. The hyperparameter optimization is dynamically scheduled based on your resource budget.
 - OpenVINO™ Training Extensions uses [Datumaro](https://openvinotoolkit.github.io/datumaro/v1.4.1/index.html) as the backend to hadle datasets. Thanks to that, OpenVINO™ Training Extensions supports the most common academic field dataset formats for each task. We constantly working to extend supported formats to give more freedom of datasets format choice.
-- [Auto-configuration functionality](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
+- [Auto-configuration functionality](https://openvinotoolkit.github.io/training_extensions/1.4.4/guide/explanation/additional_features/auto_configuration.html). OpenVINO™ Training Extensions analyzes provided dataset and selects the proper task and model template to provide the best accuracy/speed trade-off. It will also make a random auto-split of your dataset if there is no validation set provided.
 
 ---
 
 ## Getting Started
 
 ### Installation
 
-Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/get_started/installation.html).
+Please refer to the [installation guide](https://openvinotoolkit.github.io/training_extensions/1.4.4/guide/get_started/installation.html).
 
 Note: Python 3.8 and 3.9 were tested, along with Ubuntu 18.04 and 20.04.
 
@@ -90,7 +90,7 @@ Note: Python 3.8 and 3.9 were tested, along with Ubuntu 18.04 and 20.04.
 - `otx demo` allows one to apply a trained model on the custom data or the online footage from a web camera and see how it will work in a real-life scenario.
 - `otx explain` runs explain algorithm on the provided data and outputs images with the saliency maps to show how your model makes predictions.
 
-You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/1.4.2/guide/get_started/cli_commands.html).
+You can find more details with examples in the [CLI command intro](https://openvinotoolkit.github.io/training_extensions/1.4.4/guide/get_started/cli_commands.html).
 
 ---
 

docs/source/guide/release_notes/index.rst

@@ -4,6 +4,13 @@ Releases
 .. toctree::
    :maxdepth: 1
 
+v1.4.4 (4Q23)
+-------------
+
+- Update ModelAPI configuration
+- Add Anomaly modelAPI changes
+- Update Image numpy access
+
 v1.4.3 (4Q23)
 -------------