Updating references to MO docs. (openvinotoolkit#1210)

* mo-update-references

Update with regard to CVS-112883

* Update .pyspelling.wordlist.txt

Author: sgolebiewski-intel

.ci/spellcheck/.pyspelling.wordlist.txt

@@ -169,6 +169,8 @@ IMU
 IMUs
 InceptionResNetV
 inferencing
+InferRequest
+InferRequests
 inpainting
 installable
 InstructGPT

notebooks/002-openvino-api/002-openvino-api.ipynb

@@ -114,10 +114,10 @@
     "\n",
     "### OpenVINO IR Model\n",
     "\n",
-    "An OpenVINO IR (Intermediate Representation) model consists of an `.xml` file, containing information about network topology, and a `.bin` file, containing the weights and biases binary data. Models in OpenVINO IR format are obtained by using Model Optimizer tool. The `read_model()` function expects the `.bin` weights file to have the same filename and be located in the same directory as the `.xml` file: `model_weights_file == Path(model_xml).with_suffix(\".bin\")`. If this is the case, specifying the weights file is optional. If the weights file has a different filename, it can be specified using the `weights` parameter in `read_model()`.\n",
+    "An OpenVINO IR (Intermediate Representation) model consists of an `.xml` file, containing information about network topology, and a `.bin` file, containing the weights and biases binary data. Models in OpenVINO IR format are obtained by using model conversion API. The `read_model()` function expects the `.bin` weights file to have the same filename and be located in the same directory as the `.xml` file: `model_weights_file == Path(model_xml).with_suffix(\".bin\")`. If this is the case, specifying the weights file is optional. If the weights file has a different filename, it can be specified using the `weights` parameter in `read_model()`.\n",
     "\n",
-    "The OpenVINO [Model Optimizer](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html#doxid-openvino-docs-m-o-d-g-deep-learning-model-optimizer-dev-guide) tool is used to convert models to OpenVINO IR format. Model Optimizer reads the original model and creates an OpenVINO IR model (`.xml` and `.bin` files) so inference can be performed without delays due to format conversion. Optionally, Model Optimizer can adjust the model to be more suitable for inference, for example, by alternating input shapes, embedding preprocessing and cutting training parts off.\n",
-    "For information on how to convert your existing TensorFlow, PyTorch or ONNX model to OpenVINO IR format with Model Optimizer, refer to the [tensorflow-to-openvino](../101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb) and [pytorch-onnx-to-openvino](../102-pytorch-onnx-to-openvino/102-pytorch-onnx-to-openvino.ipynb) notebooks. "
+    "The OpenVINO [model conversion API](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html#doxid-openvino-docs-m-o-d-g-deep-learning-model-optimizer-dev-guide) tool is used to convert models to OpenVINO IR format. Model conversion API reads the original model and creates an OpenVINO IR model (`.xml` and `.bin` files) so inference can be performed without delays due to format conversion. Optionally, model conversion API can adjust the model to be more suitable for inference, for example, by alternating input shapes, embedding preprocessing and cutting training parts off.\n",
+    "For information on how to convert your existing TensorFlow, PyTorch or ONNX model to OpenVINO IR format with model conversion API, refer to the [tensorflow-to-openvino](../101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb) and [pytorch-onnx-to-openvino](../102-pytorch-onnx-to-openvino/102-pytorch-onnx-to-openvino.ipynb) notebooks. "
    ]
   },
   {
@@ -275,7 +275,7 @@
     "TensorFlow models saved in frozen graph format can also be passed to `read_model` starting in OpenVINO 2022.3.\n",
     "\n",
     "> **NOTE**: Directly loading TensorFlow models is available as a preview feature in the OpenVINO 2022.3 release. Fully functional support will be provided in the upcoming 2023 releases.\n",
-    "> Currently support is limited to only frozen graph inference format. Other TensorFlow model formats must be converted to OpenVINO IR using [Model Optimizer](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html)."
+    "> Currently support is limited to only frozen graph inference format. Other TensorFlow model formats must be converted to OpenVINO IR using [model conversion API](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html)."
    ]
   },
   {

notebooks/101-tensorflow-classification-to-openvino/101-tensorflow-classification-to-openvino.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Convert a TensorFlow Model to OpenVINO™\n",
     "\n",
-    "This short tutorial shows how to convert a TensorFlow [MobileNetV3](https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) image classification model to OpenVINO [Intermediate Representation](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_IR_and_opsets.html) (OpenVINO IR) format, using [Model Optimizer](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html). After creating the OpenVINO IR, load the model in [OpenVINO Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) and do inference with a sample image.  "
+    "This short tutorial shows how to convert a TensorFlow [MobileNetV3](https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v3_small_1_0_224_tf.html) image classification model to OpenVINO [Intermediate Representation](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_IR_and_opsets.html) (OpenVINO IR) format, using [model conversion API](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html). After creating the OpenVINO IR, load the model in [OpenVINO Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) and do inference with a sample image.  "
    ]
   },
   {
@@ -149,8 +149,8 @@
     "\n",
     "### Convert a TensorFlow Model to OpenVINO IR Format\n",
     "\n",
-    "Call the OpenVINO Model Optimizer Python API to convert the TensorFlow model to OpenVINO IR. `mo.convert_model` function accept path to saved model directory and returns OpenVINO Model class instance which represents this model. Obtained model is ready to use and loading on device using `compile_model` or can be saved on disk using `serialize` function.\n",
-    "See the [Model Optimizer Developer Guide](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html) for more information about Model Optimizer and TensorFlow models conversion."
+    "Use the model conversion Python API to convert the TensorFlow model to OpenVINO IR. The `mo.convert_model` function accept path to saved model directory and returns OpenVINO Model class instance which represents this model. Obtained model is ready to use and to be loaded on a device using `compile_model` or can be saved on a disk using the `serialize` function.\n",
+    "See the [tutorial](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow.html) for more information about using model conversion API with TensorFlow models."
    ]
   },
   {
@@ -167,7 +167,7 @@
     }
    ],
    "source": [
-    "# Run Model Optimizer if the IR model file does not exist\n",
+    "# Run model conversion API if the IR model file does not exist\n",
     "if not ir_path.exists():\n",
     "    print(\"Exporting TensorFlow model to IR... This may take a few minutes.\")\n",
     "    ov_model = mo.convert_model(saved_model_dir=model_path, input_shape=[[1, 224, 224, 3]], compress_to_fp16=True)\n",

notebooks/102-pytorch-to-openvino/102-pytorch-onnx-to-openvino.ipynb

@@ -204,7 +204,7 @@
    "source": [
     "### Convert ONNX Model to OpenVINO IR Format\n",
     "\n",
-    "Use Model Optimizer to convert the ONNX model to OpenVINO IR with `FP16` precision. The models are saved inside the current directory. For more information about Model Optimizer, see the [Model Optimizer Developer Guide](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)."
+    "To convert the ONNX model to OpenVINO IR with `FP16` precision, use model conversion API. The models are saved inside the current directory. For more information on how to convert models, see this [page](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html)."
    ]
   },
   {
@@ -765,8 +765,8 @@
     "* [Pytorch ONNX Documentation](https://pytorch.org/docs/stable/onnx.html)\n",
     "* [PIP install openvino-dev](https://pypi.org/project/openvino-dev/)\n",
     "* [OpenVINO ONNX support](https://docs.openvino.ai/2021.4/openvino_docs_IE_DG_ONNX_Support.html)\n",
-    "* [Model Optimizer Documentation](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model_General.html)\n",
-    "* [Model Optimizer Pytorch conversion guide](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch.html)\n"
+    "* [Model Conversion API documentation](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html)\n",
+    "* [Converting Pytorch model](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch.html)\n"
    ]
   }
  ],

notebooks/102-pytorch-to-openvino/102-pytorch-to-openvino.ipynb

@@ -273,7 +273,7 @@
     "* `example_input` - input data sample which can be used for model tracing.\n",
     "* `input_shape` - the shape of input tensor for conversion\n",
     "\n",
-    "and any other advanced options supported by Model Optimizer Python API. More details can be found on this [page](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Python_API.html)"
+    "and any other advanced options supported by model conversion Python API. More details can be found on this [page](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)"
    ]
   },
   {

notebooks/103-paddle-to-openvino/README.md

@@ -8,7 +8,7 @@ This notebook shows how to convert [PaddlePaddle](https://www.paddlepaddle.org.c
 
 ## Notebook Contents
 
-The notebook uses [OpenVINO [Model Optimizer](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) to convert a MobileNet V3 [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) model, pre-trained on the [ImageNet](https://www.image-net.org) dataset, to OpenVINO IR. It also shows how to perform classification inference on an image, using [OpenVINO Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) and compares the results of the PaddlePaddle model with the OpenVINO IR model. 
+The notebook uses [model conversion API](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html) to convert a MobileNet V3 [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) model, pre-trained on the [ImageNet](https://www.image-net.org) dataset, to OpenVINO IR. It also shows how to perform classification inference on an image, using [OpenVINO Runtime](https://docs.openvino.ai/nightly/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) and compares the results of the PaddlePaddle model with the OpenVINO IR model. 
 
 ## Installation Instructions
 

notebooks/105-language-quantize-bert/105-language-quantize-bert.ipynb

@@ -225,7 +225,7 @@
    "source": [
     "Convert the original PyTorch model to the OpenVINO Intermediate Representation.\n",
     "\n",
-    "From OpenVINO 2023.0, we can directly convert a model from the PyTorch format to the OpenVINO IR format using Model Optimizer. Following PyTorch model formats are supported:\n",
+    "From OpenVINO 2023.0, we can directly convert a model from the PyTorch format to the OpenVINO IR format using model conversion API. Following PyTorch model formats are supported:\n",
     "\n",
     "- `torch.nn.Module`\n",
     "- `torch.jit.ScriptModule`\n",

notebooks/106-auto-device/106-auto-device.ipynb

@@ -56,9 +56,9 @@
     "\n",
     "This tutorial uses [resnet50](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet50.html#resnet50) model from [torchvision](https://pytorch.org/vision/main/index.html?highlight=torchvision#module-torchvision) library.\n",
     "ResNet 50 is image classification model pre-trained on ImageNet dataset described in paper [\"Deep Residual Learning for Image Recognition\"](https://arxiv.org/abs/1512.03385).\n",
-    "From OpenVINO 2023.0, we can directly convert a model from the PyTorch format to the OpenVINO IR format using Model Optimizer. To convert model, we should provide model object instance into `mo.convert_model` function, optionally, we can specify input shape for conversion (by default models from PyTorch converted with dynamic input shapes). `mo.convert_model` returns openvino.runtime.Model object ready to be loaded on device with `openvino.runtime.Core().compile_model` or serialized for next usage with `openvino.runtime.serialize`. \n",
+    "From OpenVINO 2023.0, we can directly convert a model from the PyTorch format to the OpenVINO IR format using model conversion API. To convert model, we should provide model object instance into `mo.convert_model` function, optionally, we can specify input shape for conversion (by default models from PyTorch converted with dynamic input shapes). `mo.convert_model` returns openvino.runtime.Model object ready to be loaded on a device with `openvino.runtime.Core().compile_model` or serialized for next usage with `openvino.runtime.serialize`. \n",
     "\n",
-    "For more information about Model Optimizer, see the [Model Optimizer Developer Guide](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)."
+    "For more information about model conversion API, see this [page](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html)."
    ]
   },
   {

notebooks/107-speech-recognition-quantization/107-speech-recognition-quantization-wav2vec2.ipynb

@@ -98,7 +98,7 @@
     "Perform the following:\n",
     "- Download and unpack a pre-trained Wav2Vec2 model.\n",
     "- Convert the model to ONNX.\n",
-    "- Run Model Optimizer to convert the model from the ONNX representation to the OpenVINO Intermediate Representation (OpenVINO IR)."
+    "- Run model conversion API to convert the model from the ONNX representation to the OpenVINO Intermediate Representation (OpenVINO IR)."
    ]
   },
   {

notebooks/108-gpu-device/108-gpu-device.ipynb

@@ -453,7 +453,7 @@
    "source": [
     "#### Convert the Model to OpenVINO IR format\n",
     "\n",
-    "Use Model Optimizer to convert the model to OpenVINO IR with `FP16` precision. The models are saved to the `model/ir_model/` directory. For more information about Model Optimizer, see the [Model Optimizer Developer Guide](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html)."
+    "To convert the model to OpenVINO IR with `FP16` precision, use model conversion API. The models are saved to the `model/ir_model/` directory. For more details about model conversion, see this [page](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html)."
    ]
   },
   {

notebooks/110-ct-segmentation-quantize/110-ct-segmentation-quantize-nncf.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "This third tutorial in the series shows how to:\n",
     "\n",
-    "- Convert an Original model to OpenVINO IR with [Model Optimizer](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html), using [Model Optimizer Python API](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Python_API.html)\n",
+    "- Convert an Original model to OpenVINO IR with [model conversion API](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html)\n",
     "- Quantize a PyTorch model with NNCF\n",
     "- Evaluate the F1 score metric of the original model and the quantized model\n",
     "- Benchmark performance of the FP32 model and the INT8 quantized model\n",

notebooks/110-ct-segmentation-quantize/README.md

@@ -25,7 +25,7 @@ so it is not required to run the data preparation and training notebooks before
 
 This quantization tutorial consists of the following steps:
 
-* Use Model Optimizer Python API to convert the model to OpenVINO IR. For more information about Model Optimizer Python API,  [Model Optimizer Developer Guide](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Python_API.html)..
+* Use model conversion Python API to convert the model to OpenVINO IR. For more information about model conversion Python API, see this [page](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html).
 * Quantizing the model with NNCF with the [Post-training Quantization with NNCF Tool](https://docs.openvino.ai/nightly/basic_quantization_flow.html) API in OpenVINO.
 * Evaluating the F1 score metric of the original model and the quantized model.
 * Benchmarking performance of the original model and the quantized model.

notebooks/111-yolov5-quantization-migration/111-yolov5-quantization-migration.ipynb

@@ -181,8 +181,7 @@
    "id": "cfc9a783",
    "metadata": {},
    "source": [
-    "Convert the ONNX model to OpenVINO Intermediate Representation (IR) model generated by [Model Optimizer](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html#doxid-openvino-docs-m-o-d-g-deep-learning-model-optimizer-dev-guide). We will use [Model Optimizer Python API](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Python_API.html) `openvino.tools.mo.convert_model` function to convert ONNX model to OpenVINO Model, then it can be serialized using `openvino.runtime.serialize`As the result, directory with the `{MODEL_DIR}` name will be created with the following content:\n",
-    "\n",
+    "Convert the ONNX model to OpenVINO Intermediate Representation (IR) model generated by [model conversion API](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html). We will use the `openvino.tools.mo.convert_model` function of model conversion Python API to convert ONNX model to OpenVINO Model, then it can be serialized using `openvino.runtime.serialize`. As the result, directory with the `{MODEL_DIR}` name will be created with the following content:\n",
     "* `{MODEL_NAME}_fp32.xml`, `{MODEL_NAME}_fp32.bin` - OpenVINO Intermediate Representation (IR) model format with FP32 precision generated by Model Optimizer.\n",
     "* `{MODEL_NAME}_fp16.xml`, `{MODEL_NAME}_fp16.bin` - OpenVINO Intermediate Representation (IR) model format with FP32 precision generated by Model Optimizer."
    ]
@@ -1431,7 +1430,7 @@
     "* [Ultralytics YOLOv5](https://github.com/ultralytics/yolov5)\n",
     "* [OpenVINO Post-training Optimization Tool](https://docs.openvino.ai/2023.0/pot_introduction.html)\n",
     "* [NNCF Post-training quantization](https://docs.openvino.ai/nightly/basic_quantization_flow.html)\n",
-    "* [Model Optimizer](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html#doxid-openvino-docs-m-o-d-g-deep-learning-model-optimizer-dev-guide)\n"
+    "* [Model Conversion API](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html)\n"
    ]
   }
  ],

notebooks/112-pytorch-post-training-quantization-nncf/112-pytorch-post-training-quantization-nncf.ipynb

@@ -638,11 +638,11 @@
    "source": [
     "### III. Convert the models to OpenVINO Intermediate Representation (OpenVINO IR)\n",
     "\n",
-    "Use Model Optimizer Python API to convert the Pytorch models to OpenVINO IR. The models will be saved to the 'OUTPUT' directory for latter benchmarking.\n",
+    "To convert the Pytorch models to OpenVINO IR, use model conversion Python API . The models will be saved to the 'OUTPUT' directory for later benchmarking.\n",
     "\n",
-    "For more information about Model Optimizer, refer to the [Model Optimizer Developer Guide](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html).\n",
+    "For more information about model conversion, refer to this [page](https://docs.openvino.ai/2023.0/openvino_docs_model_processing_introduction.html).\n",
     "\n",
-    "Before converting models export them to ONNX. Executing the following command may take a while.\n"
+    "Before converting models, export them to ONNX. Executing the following command may take a while.\n"
    ]
   },
   {