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[DOCS] Fixing formatting issues in articles (#17994)

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* fixing-formatting
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Sebastian Golebiewski 2023-06-13 07:59:27 +02:00 committed by GitHub
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2
docs/Documentation/inference_modes_overview.md
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@ -17,7 +17,7 @@ OpenVINO Runtime offers multiple inference modes to allow optimum hardware utili
The remaining modes assume certain levels of automation in selecting devices for inference. Using them in the deployed solution may potentially increase its performance and portability. The automated modes are:
* :doc:`Automatic Device Selection (AUTO) <openvino_docs_OV_UG_supported_plugins_AUTO>`
* :doc:``Multi-Device Execution (MULTI) <openvino_docs_OV_UG_Running_on_multiple_devices>`
* :doc:`Multi-Device Execution (MULTI) <openvino_docs_OV_UG_Running_on_multiple_devices>`
* :doc:`Heterogeneous Execution (HETERO) <openvino_docs_OV_UG_Hetero_execution>`
* :doc:`Automatic Batching Execution (Auto-batching) <openvino_docs_OV_UG_Automatic_Batching>`

10
docs/MO_DG/prepare_model/convert_model/Convert_Model_From_Kaldi.md
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@ -56,7 +56,7 @@ Examples of CLI Commands
C_{i}=log(S\*C_{i})
where :math:`C` - the counts array, :math:`C_{i} - i^{th}` element of the counts array, :math:`|C|` - number of elements in the counts array;
where :math:`C` - the counts array, :math:`C_{i} - i^{th}` element of the counts array, :math:`|C|` - number of elements in the counts array;
* The normalized counts are subtracted from biases of the last or next to last layer (if last layer is SoftMax).
@ -64,13 +64,13 @@ Examples of CLI Commands
* If you want to remove the last SoftMax layer in the topology, launch the model conversion with the ``remove_output_softmax`` flag:
.. code-block:: cpp
.. code-block:: cpp
mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts --remove_output_softmax
mo --input_model wsj_dnn5b_smbr.nnet --counts wsj_dnn5b_smbr.counts --remove_output_softmax
Model conversion API finds the last layer of the topology and removes this layer only if it is a SoftMax layer.
Model conversion API finds the last layer of the topology and removes this layer only if it is a SoftMax layer.
.. note:: Model conversion can remove SoftMax layer only if the topology has one output.
.. note:: Model conversion can remove SoftMax layer only if the topology has one output.
* You can use the *OpenVINO Speech Recognition* sample application for the sample inference of Kaldi models. This sample supports models with only one output. If your model has several outputs, specify the desired one with the ``output`` option.

120
docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md
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@ -164,23 +164,23 @@ Command-Line Interface (CLI) Examples Using TensorFlow-Specific Parameters
* Launching model conversion for Inception V1 frozen model when model file is a plain text protobuf:
.. code-block:: sh
.. code-block:: sh
mo --input_model inception_v1.pbtxt --input_model_is_text -b 1
mo --input_model inception_v1.pbtxt --input_model_is_text -b 1
* Launching model conversion for Inception V1 frozen model and dump information about the graph to TensorBoard log dir ``/tmp/log_dir``
.. code-block:: sh
.. code-block:: sh
mo --input_model inception_v1.pb -b 1 --tensorboard_logdir /tmp/log_dir
mo --input_model inception_v1.pb -b 1 --tensorboard_logdir /tmp/log_dir
* Launching model conversion for BERT model in the SavedModel format, with three inputs. Specify explicitly the input shapes where the batch size and the sequence length equal 2 and 30 respectively.
.. code-block:: sh
.. code-block:: sh
mo --saved_model_dir BERT --input mask,word_ids,type_ids --input_shape [2,30],[2,30],[2,30]
mo --saved_model_dir BERT --input mask,word_ids,type_ids --input_shape [2,30],[2,30],[2,30]
Conversion of TensorFlow models from memory using Python API
############################################################
@ -189,96 +189,96 @@ Model conversion API supports passing TensorFlow/TensorFlow2 models directly fro
* ``tf.keras.Model``
.. code-block:: python
.. code-block:: python
model = tf.keras.applications.ResNet50(weights="imagenet")
ov_model = convert_model(model)
model = tf.keras.applications.ResNet50(weights="imagenet")
ov_model = convert_model(model)
* ``tf.keras.layers.Layer``. Requires setting the "input_shape".
.. code-block:: python
.. code-block:: python
import tensorflow_hub as hub
import tensorflow_hub as hub
model = hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/5")
ov_model = convert_model(model, input_shape=[-1, 224, 224, 3])
model = hub.KerasLayer("https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/5")
ov_model = convert_model(model, input_shape=[-1, 224, 224, 3])
* ``tf.Module``. Requires setting the "input_shape".
.. code-block:: python
.. code-block:: python
class MyModule(tf.Module):
def __init__(self, name=None):
super().__init__(name=name)
self.variable1 = tf.Variable(5.0, name="var1")
self.variable2 = tf.Variable(1.0, name="var2")
def __call__(self, x):
return self.variable1 * x + self.variable2
class MyModule(tf.Module):
def __init__(self, name=None):
super().__init__(name=name)
self.variable1 = tf.Variable(5.0, name="var1")
self.variable2 = tf.Variable(1.0, name="var2")
def __call__(self, x):
return self.variable1 * x + self.variable2
model = MyModule(name="simple_module")
ov_model = convert_model(model, input_shape=[-1])
model = MyModule(name="simple_module")
ov_model = convert_model(model, input_shape=[-1])
* ``tf.compat.v1.Graph``
.. code-block:: python
.. code-block:: python
with tf.compat.v1.Session() as sess:
inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
inp2 = tf.compat.v1.placeholder(tf.float32, [100], 'Input2')
output = tf.nn.relu(inp1 + inp2, name='Relu')
tf.compat.v1.global_variables_initializer()
model = sess.graph
with tf.compat.v1.Session() as sess:
inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
inp2 = tf.compat.v1.placeholder(tf.float32, [100], 'Input2')
output = tf.nn.relu(inp1 + inp2, name='Relu')
tf.compat.v1.global_variables_initializer()
model = sess.graph
ov_model = convert_model(model)
ov_model = convert_model(model)
* ``tf.compat.v1.GraphDef``
.. code-block:: python
.. code-block:: python
with tf.compat.v1.Session() as sess:
inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
inp2 = tf.compat.v1.placeholder(tf.float32, [100], 'Input2')
output = tf.nn.relu(inp1 + inp2, name='Relu')
tf.compat.v1.global_variables_initializer()
model = sess.graph_def
with tf.compat.v1.Session() as sess:
inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
inp2 = tf.compat.v1.placeholder(tf.float32, [100], 'Input2')
output = tf.nn.relu(inp1 + inp2, name='Relu')
tf.compat.v1.global_variables_initializer()
model = sess.graph_def
ov_model = convert_model(model)
ov_model = convert_model(model)
* ``tf.function``
.. code-block:: python
.. code-block:: python
@tf.function(
input_signature=[tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32),
tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32)])
def func(x, y):
return tf.nn.sigmoid(tf.nn.relu(x + y))
@tf.function(
input_signature=[tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32),
tf.TensorSpec(shape=[1, 2, 3], dtype=tf.float32)])
def func(x, y):
return tf.nn.sigmoid(tf.nn.relu(x + y))
ov_model = convert_model(func)
ov_model = convert_model(func)
* ``tf.compat.v1.session``
.. code-block:: python
.. code-block:: python
with tf.compat.v1.Session() as sess:
inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
inp2 = tf.compat.v1.placeholder(tf.float32, [100], 'Input2')
output = tf.nn.relu(inp1 + inp2, name='Relu')
tf.compat.v1.global_variables_initializer()
with tf.compat.v1.Session() as sess:
inp1 = tf.compat.v1.placeholder(tf.float32, [100], 'Input1')
inp2 = tf.compat.v1.placeholder(tf.float32, [100], 'Input2')
output = tf.nn.relu(inp1 + inp2, name='Relu')
tf.compat.v1.global_variables_initializer()
ov_model = convert_model(sess)
ov_model = convert_model(sess)
* ``tf.train.checkpoint``
.. code-block:: python
.. code-block:: python
model = tf.keras.Model(...)
checkpoint = tf.train.Checkpoint(model)
save_path = checkpoint.save(save_directory)
# ...
checkpoint.restore(save_path)
ov_model = convert_model(checkpoint)
model = tf.keras.Model(...)
checkpoint = tf.train.Checkpoint(model)
save_path = checkpoint.save(save_directory)
# ...
checkpoint.restore(save_path)
ov_model = convert_model(checkpoint)
Supported TensorFlow and TensorFlow 2 Keras Layers
##################################################

20
docs/MO_DG/prepare_model/convert_model/mxnet_specific/Convert_GluonCV_Models.md
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@ -21,25 +21,25 @@ This article provides the instructions and examples on how to convert `GluonCV S
2. Run model conversion API, specifying the ``enable_ssd_gluoncv`` option. Make sure the ``input_shape`` parameter is set to the input shape layout of your model (NHWC or NCHW). The examples below illustrate running model conversion for the SSD and YOLO-v3 models trained with the NHWC layout and located in the ``<model_directory>``:
* **For GluonCV SSD topologies:**
* **For GluonCV SSD topologies:**
.. code-block:: sh
.. code-block:: sh
mo --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data --output_dir <OUTPUT_MODEL_DIR>
mo --input_model <model_directory>/ssd_512_mobilenet1.0.params --enable_ssd_gluoncv --input_shape [1,512,512,3] --input data --output_dir <OUTPUT_MODEL_DIR>
* **For YOLO-v3 topology:**
* **For YOLO-v3 topology:**
* To convert the model:
* To convert the model:
.. code-block:: sh
.. code-block:: sh
mo --input_model <model_directory>/yolo3_mobilenet1.0_voc-0000.params --input_shape [1,255,255,3] --output_dir <OUTPUT_MODEL_DIR>
mo --input_model <model_directory>/yolo3_mobilenet1.0_voc-0000.params --input_shape [1,255,255,3] --output_dir <OUTPUT_MODEL_DIR>
* To convert the model with replacing the subgraph with RegionYolo layers:
* To convert the model with replacing the subgraph with RegionYolo layers:
.. code-block:: sh
.. code-block:: sh
mo --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params --input_shape [1,255,255,3] --transformations_config "front/mxnet/yolo_v3_mobilenet1_voc. json" --output_dir <OUTPUT_MODEL_DIR>
mo --input_model <model_directory>/models/yolo3_mobilenet1.0_voc-0000.params --input_shape [1,255,255,3] --transformations_config "front/mxnet/ yolo_v3_mobilenet1_voc. json" --output_dir <OUTPUT_MODEL_DIR>
@endsphinxdirective

2
docs/MO_DG/prepare_model/convert_model/pytorch_specific/Convert_YOLACT.md
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@ -184,7 +184,7 @@ Converting a YOLACT Model to the OpenVINO IR format
mo --input_model /path/to/yolact.onnx
**Step 4**. Embed input preprocessing into the IR:
**Step 5**. Embed input preprocessing into the IR:
To get performance gain by offloading to the OpenVINO application of mean/scale values and RGB->BGR conversion, use the following model conversion API parameters:

6
docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_BERT_From_Tensorflow.md
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@ -79,13 +79,13 @@ Follow these steps to make a pretrained TensorFlow BERT model reshapable over ba
* For UNIX-like systems, run the following command:
.. code-block:: sh
.. code-block:: sh
wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
wget https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py
* For Windows systems:
Download the `Python script <https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py>`__ to the current working directory.
Download the `Python script <https://gist.githubusercontent.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e/raw/17b8dd0d724281ed7c3b2aeeda662b92809aadd5/download_glue_data.py>`__ to the current working directory.
6. Download GLUE data by running:

6
docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_CRNN_From_Tensorflow.md
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@ -40,15 +40,13 @@ If you have another implementation of CRNN model, it can be converted to OpenVIN
* For Linux:
.. code-block:: sh
.. code-block:: sh
export PYTHONPATH="${PYTHONPATH}:/path/to/CRNN_Tensorflow/"
export PYTHONPATH="${PYTHONPATH}:/path/to/CRNN_Tensorflow/"
* For Windows, add ``/path/to/CRNN_Tensorflow/`` to the ``PYTHONPATH`` environment variable in settings.
2. Edit the ``tools/demo_shadownet.py`` script. After ``saver.restore(sess=sess, save_path=weights_path)`` line, add the following code:
.. code-block:: python

6
docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_DeepSpeech_From_Tensorflow.md
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@ -19,10 +19,10 @@ To download the model, follow the instruction below:
* For UNIX-like systems, run the following command:
.. code-block:: sh
.. code-block:: sh
wget -O - https://github.com/mozilla/DeepSpeech/archive/v0.8.2.tar.gz | tar xvfz -
wget -O - https://github.com/mozilla/DeepSpeech/releases/download/v0.8.2/deepspeech-0.8.2-checkpoint.tar.gz | tar xvfz -
wget -O - https://github.com/mozilla/DeepSpeech/archive/v0.8.2.tar.gz | tar xvfz -
wget -O - https://github.com/mozilla/DeepSpeech/releases/download/v0.8.2/deepspeech-0.8.2-checkpoint.tar.gz | tar xvfz -
* For Windows systems:

3
docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_NCF_From_Tensorflow.md
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@ -27,8 +27,7 @@ This tutorial explains how to convert Neural Collaborative Filtering (NCF) model
where ``rating/BiasAdd`` is an output node.
3. Convert the model to the OpenVINO format. If you look at your frozen model, you can see that
it has one input that is split into four ``ResourceGather`` layers. (Click image to zoom in.)
3. Convert the model to the OpenVINO format. If you look at your frozen model, you can see that it has one input that is split into four ``ResourceGather`` layers. (Click image to zoom in.)
.. image:: ./_static/images/NCF_start.svg

12
docs/OV_Runtime_UG/Model_caching_overview.md
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@ -40,16 +40,16 @@ To enable model caching, the application must specify a folder to store the cach
.. tab-item:: C++
:sync: cpp
.. doxygensnippet:: docs/snippets/ov_caching.cpp
:language: cpp
:fragment: [ov:caching:part0]
.. doxygensnippet:: docs/snippets/ov_caching.cpp
:language: cpp
:fragment: [ov:caching:part0]
.. tab-item:: Python
:sync: py
.. doxygensnippet:: docs/snippets/ov_caching.py
:language: py
:fragment: [ov:caching:part0]
.. doxygensnippet:: docs/snippets/ov_caching.py
:language: py
:fragment: [ov:caching:part0]
With this code, if the device specified by ``device_name`` supports import/export model capability,

1
docs/OV_Runtime_UG/Python_API_inference.md
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@ -74,6 +74,7 @@ Example usage:
"Postponed Return" is a practice to omit overhead of ``OVDict``, which is always returned from
synchronous calls. "Postponed Return" could be applied when:
* only a part of output data is required. For example, only one specific output is significant
in a given pipeline step and all outputs are large, thus, expensive to copy.
* data is not required "now". For example, it can be later extracted inside the pipeline as

120
docs/OV_Runtime_UG/ShapeInference.md
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@ -56,101 +56,101 @@ When using the ``reshape`` method, you may take one of the approaches:
1. You can pass a new shape to the method in order to change the input shape of
the model with a single input. See the example of adjusting spatial dimensions to the input image:
the model with a single input. See the example of adjusting spatial dimensions to the input image:
.. tab-set::
.. tab-set::
.. tab-item:: C++
:sync: cpp
.. tab-item:: C++
:sync: cpp
.. doxygensnippet:: docs/snippets/ShapeInference.cpp
:language: cpp
:fragment: spatial_reshape
.. doxygensnippet:: docs/snippets/ShapeInference.cpp
:language: cpp
:fragment: spatial_reshape
.. tab-item:: Python
:sync: py
.. tab-item:: Python
:sync: py
.. doxygensnippet:: docs/snippets/ShapeInference.py
:language: python
:fragment: simple_spatials_change
.. doxygensnippet:: docs/snippets/ShapeInference.py
:language: python
:fragment: simple_spatials_change
To do the opposite - to resize input image to match the input shapes of the model,
use the :doc:`pre-processing API <openvino_docs_OV_UG_Preprocessing_Overview>`.
To do the opposite - to resize input image to match the input shapes of the model,
use the :doc:`pre-processing API <openvino_docs_OV_UG_Preprocessing_Overview>`.
2. You can express a reshape plan, specifying the input by the port, the index, and the tensor name:
.. tab-set::
.. tab-set::
.. tab-item:: Port
.. tab-item:: Port
.. tab-set::
.. tab-set::
.. tab-item:: C++
:sync: cpp
.. tab-item:: C++
:sync: cpp
``map<ov::Output<ov::Node>, ov::PartialShape`` specifies input by passing actual input port:
``map<ov::Output<ov::Node>, ov::PartialShape`` specifies input by passing actual input port:
.. doxygensnippet:: docs/snippets/ShapeInference.cpp
:language: cpp
:fragment: [obj_to_shape]
.. doxygensnippet:: docs/snippets/ShapeInference.cpp
:language: cpp
:fragment: [obj_to_shape]
.. tab-item:: Python
:sync: py
.. tab-item:: Python
:sync: py
``openvino.runtime.Output`` dictionary key specifies input by passing actual input object.
Dictionary values representing new shapes could be ``PartialShape``:
``openvino.runtime.Output`` dictionary key specifies input by passing actual input object.
Dictionary values representing new shapes could be ``PartialShape``:
.. doxygensnippet:: docs/snippets/ShapeInference.py
:language: python
:fragment: [obj_to_shape]
.. doxygensnippet:: docs/snippets/ShapeInference.py
:language: python
:fragment: [obj_to_shape]
.. tab-item:: Index
.. tab-item:: Index
.. tab-set::
.. tab-set::
.. tab-item:: C++
:sync: cpp
.. tab-item:: C++
:sync: cpp
``map<size_t, ov::PartialShape>`` specifies input by its index:
``map<size_t, ov::PartialShape>`` specifies input by its index:
.. doxygensnippet:: docs/snippets/ShapeInference.cpp
:language: cpp
:fragment: [idx_to_shape]
.. doxygensnippet:: docs/snippets/ShapeInference.cpp
:language: cpp
:fragment: [idx_to_shape]
.. tab-item:: Python
:sync: py
.. tab-item:: Python
:sync: py
``int`` dictionary key specifies input by its index.
Dictionary values representing new shapes could be ``tuple``:
``int`` dictionary key specifies input by its index.
Dictionary values representing new shapes could be ``tuple``:
.. doxygensnippet:: docs/snippets/ShapeInference.py
:language: python
:fragment: [idx_to_shape]
.. doxygensnippet:: docs/snippets/ShapeInference.py
:language: python
:fragment: [idx_to_shape]
.. tab-item:: Tensor Name
.. tab-item:: Tensor Name
.. tab-set::
.. tab-set::
.. tab-item:: C++
:sync: cpp
.. tab-item:: C++
:sync: cpp
``map<string, ov::PartialShape>`` specifies input by its name:
``map<string, ov::PartialShape>`` specifies input by its name:
.. doxygensnippet:: docs/snippets/ShapeInference.cpp
:language: cpp
:fragment: [name_to_shape]
.. doxygensnippet:: docs/snippets/ShapeInference.cpp
:language: cpp
:fragment: [name_to_shape]
.. tab-item:: Python
:sync: py
.. tab-item:: Python
:sync: py
``str`` dictionary key specifies input by its name.
Dictionary values representing new shapes could be ``str``:
``str`` dictionary key specifies input by its name.
Dictionary values representing new shapes could be ``str``:
.. doxygensnippet:: docs/snippets/ShapeInference.py
:language: python
:fragment: [name_to_shape]
.. doxygensnippet:: docs/snippets/ShapeInference.py
:language: python
:fragment: [name_to_shape]
You can find the usage scenarios of the ``reshape`` method in

58
docs/OV_Runtime_UG/deployment/conditional_compilation_deployment.md
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@ -161,41 +161,41 @@ Considering that JIT kernels can be affected by L1/L2/L3 cache size and the numb
- L2/L3 cache emulation
Hack the function of get cache size:
Hack the function of get cache size
``unsigned int dnnl::impl::cpu::platform::get_per_core_cache_size(int level)``
to make it return emulated cache size in analyzed stage, the simplest way is to leverage environment variable to pass the emulated cache size, for example:
.. code-block:: cpp
.. code-block:: cpp
#if defined(SELECTIVE_BUILD_ANALYZER)
if (level == 2) {
const char* L2_cache_size = std::getenv("OV_CC_L2_CACHE_SIZE");
if (L2_cache_size) {
int size = std::atoi(L2_cache_size);
if (size > 0) {
return size;
}
}
} else if (level == 3) {
const char* L3_cache_size = std::getenv("OV_CC_L3_CACHE_SIZE");
if (L3_cache_size) {
int size = std::atoi(L3_cache_size);
if (size > 0) {
return size;
}
}
} else if (level == 1) {
const char* L1_cache_size = std::getenv("OV_CC_L1_CACHE_SIZE");
if (L1_cache_size) {
int size = std::atoi(L1_cache_size);
if (size > 0) {
return size;
}
}
}
#endif
#if defined(SELECTIVE_BUILD_ANALYZER)
if (level == 2) {
const char* L2_cache_size = std::getenv("OV_CC_L2_CACHE_SIZE");
if (L2_cache_size) {
int size = std::atoi(L2_cache_size);
if (size > 0) {
return size;
}
}
} else if (level == 3) {
const char* L3_cache_size = std::getenv("OV_CC_L3_CACHE_SIZE");
if (L3_cache_size) {
int size = std::atoi(L3_cache_size);
if (size > 0) {
return size;
}
}
} else if (level == 1) {
const char* L1_cache_size = std::getenv("OV_CC_L1_CACHE_SIZE");
if (L1_cache_size) {
int size = std::atoi(L1_cache_size);
if (size > 0) {
return size;
}
}
}
#endif
- CPU core number emulation

28
docs/OV_Runtime_UG/supported_plugins/Device_Plugins.md
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@ -29,20 +29,20 @@ Feature Support Matrix
The table below demonstrates support of key features by OpenVINO device plugins.
========================================================================================= ============================ =============== ===============
Capability CPU GPU GNA
========================================================================================= ============================ =============== ===============
:doc:`Heterogeneous execution <openvino_docs_OV_UG_Hetero_execution>` Yes Yes No
:doc:`Multi-device execution <openvino_docs_OV_UG_Running_on_multiple_devices>` Yes Yes Partial
:doc:`Automatic batching <openvino_docs_OV_UG_Automatic_Batching>` No Yes No
:doc:`Multi-stream execution <openvino_docs_deployment_optimization_guide_tput>` Yes (Intel® x86-64 only) Yes No
:doc:`Models caching <openvino_docs_OV_UG_Model_caching_overview>` Yes Partial Yes
:doc:`Dynamic shapes <openvino_docs_OV_UG_DynamicShapes>` Yes Partial No
:doc:`Import/Export <openvino_ecosystem>` Yes No Yes
:doc:`Preprocessing acceleration <openvino_docs_OV_UG_Preprocessing_Overview>` Yes Yes No
:doc:`Stateful models <openvino_docs_OV_UG_model_state_intro>` Yes No Yes
:doc:`Extensibility <openvino_docs_Extensibility_UG_Intro>` Yes Yes No
========================================================================================= ============================ =============== ===============
========================================================================================= ============================ =============== ===============
Capability CPU GPU GNA
========================================================================================= ============================ =============== ===============
:doc:`Heterogeneous execution <openvino_docs_OV_UG_Hetero_execution>` Yes Yes No
:doc:`Multi-device execution <openvino_docs_OV_UG_Running_on_multiple_devices>` Yes Yes Partial
:doc:`Automatic batching <openvino_docs_OV_UG_Automatic_Batching>` No Yes No
:doc:`Multi-stream execution <openvino_docs_deployment_optimization_guide_tput>` Yes (Intel® x86-64 only) Yes No
:doc:`Models caching <openvino_docs_OV_UG_Model_caching_overview>` Yes Partial Yes
:doc:`Dynamic shapes <openvino_docs_OV_UG_DynamicShapes>` Yes Partial No
:doc:`Import/Export <openvino_ecosystem>` Yes No Yes
:doc:`Preprocessing acceleration <openvino_docs_OV_UG_Preprocessing_Overview>` Yes Yes No
:doc:`Stateful models <openvino_docs_OV_UG_model_state_intro>` Yes No Yes
:doc:`Extensibility <openvino_docs_Extensibility_UG_Intro>` Yes Yes No
========================================================================================= ============================ =============== ===============
For more details on plugin-specific feature limitations, see the corresponding plugin pages.

44
docs/OV_Runtime_UG/supported_plugins/GNA.md
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@ -78,39 +78,29 @@ Starting with the 2021.4.1 release of OpenVINO™ and the 03.00.00.1363 version
In this mode, the GNA driver automatically falls back on CPU for a particular infer request if the HW queue is not empty.
Therefore, there is no need for explicitly switching between GNA and CPU.
.. tab-set::
.. tab-item:: C++
:sync: cpp
.. doxygensnippet:: docs/snippets/gna/configure.cpp
:language: cpp
:fragment: [include]
.. doxygensnippet:: docs/snippets/gna/configure.cpp
:language: cpp
:fragment: [ov_gna_exec_mode_hw_with_sw_fback]
.. tab-item:: Python
:sync: py
.. doxygensnippet:: docs/snippets/gna/configure.py
:language: py
:fragment: [import]
.. tab-set::
.. tab-item:: C++
:sync: cpp
.. doxygensnippet:: docs/snippets/gna/configure.cpp
:language: cpp
:fragment: [include]
.. doxygensnippet:: docs/snippets/gna/configure.cpp
:language: cpp
:fragment: [ov_gna_exec_mode_hw_with_sw_fback]
.. tab-item:: Python
:sync: py
.. doxygensnippet:: docs/snippets/gna/configure.py
:language: py
:fragment: [import]
.. doxygensnippet:: docs/snippets/gna/configure.py
:language: py
:fragment: [ov_gna_exec_mode_hw_with_sw_fback]
.. doxygensnippet:: docs/snippets/gna/configure.py
:language: py
:fragment: [ov_gna_exec_mode_hw_with_sw_fback]
.. note::

16
docs/OV_Runtime_UG/supported_plugins/GPU_RemoteTensor_API.md
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@ -428,12 +428,12 @@ on waiting for the completion of inference. The pseudo-code may look as follows:
Limitations
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
- Some primitives in the GPU plugin may block the host thread on waiting for the previous primitives before adding its kernels
to the command queue. In such cases, the ``ov::InferRequest::start_async()`` call takes much more time to return control to the calling thread
as internally it waits for a partial or full network completion.
Examples of operations: Loop, TensorIterator, DetectionOutput, NonMaxSuppression
- Synchronization of pre/post processing jobs and inference pipeline inside a shared queue is user's responsibility.
- Throughput mode is not available when queue sharing is used, i.e., only a single stream can be used for each compiled model.
- Some primitives in the GPU plugin may block the host thread on waiting for the previous primitives before adding its kernels
to the command queue. In such cases, the ``ov::InferRequest::start_async()`` call takes much more time to return control to the calling thread
as internally it waits for a partial or full network completion.
Examples of operations: Loop, TensorIterator, DetectionOutput, NonMaxSuppression
- Synchronization of pre/post processing jobs and inference pipeline inside a shared queue is user's responsibility.
- Throughput mode is not available when queue sharing is used, i.e., only a single stream can be used for each compiled model.
Low-Level Methods for RemoteContext and RemoteTensor Creation
#####################################################################
@ -490,7 +490,7 @@ To see pseudo-code of usage examples, refer to the sections below.
See Also
#######################################
* ov::Core
* ov::RemoteTensor
* ``:ref:`ov::Core <doxid-classov-1-1-core>```
* ``:ref:`ov::RemoteTensor <doxid-classov-1-1-remote-tensor>```
@endsphinxdirective

16
docs/install_guides/configurations-for-intel-gna.md
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@ -68,17 +68,17 @@ What’s Next?
Now you are ready to try out OpenVINO™. You can use the following tutorials to write your applications using Python and C++.
Developing in Python:
* Developing in Python:
* `Start with tensorflow models with OpenVINO™ <notebooks/101-tensorflow-to-openvino-with-output.html>`__
* `Start with ONNX and PyTorch models with OpenVINO™ <notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
* `Start with PaddlePaddle models with OpenVINO™ <notebooks/103-paddle-to-openvino-classification-with-output.html>`__
* `Start with tensorflow models with OpenVINO™ <notebooks/101-tensorflow-to-openvino-with-output.html>`__
* `Start with ONNX and PyTorch models with OpenVINO™ <notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
* `Start with PaddlePaddle models with OpenVINO™ <notebooks/103-paddle-to-openvino-classification-with-output.html>`__
Developing in C++:
* Developing in C++:
* :doc:`Image Classification Async C++ Sample <openvino_inference_engine_samples_classification_sample_async_README>`
* :doc:`Hello Classification C++ Sample <openvino_inference_engine_samples_hello_classification_README>`
* :doc:`Hello Reshape SSD C++ Sample <openvino_inference_engine_samples_hello_reshape_ssd_README>`
* :doc:`Image Classification Async C++ Sample <openvino_inference_engine_samples_classification_sample_async_README>`
* :doc:`Hello Classification C++ Sample <openvino_inference_engine_samples_hello_classification_README>`
* :doc:`Hello Reshape SSD C++ Sample <openvino_inference_engine_samples_hello_reshape_ssd_README>`
@endsphinxdirective

13
docs/install_guides/configurations-for-intel-gpu.md
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@ -83,21 +83,20 @@ What’s Next?
You can try out the toolkit with:
`Python Quick Start Example <notebooks/201-vision-monodepth-with-output.html>`_ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
* `Python Quick Start Example <notebooks/201-vision-monodepth-with-output.html>`_ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
Visit the :ref:`Tutorials <notebook tutorials>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
Visit the :ref:`Tutorials <notebook tutorials>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
* `OpenVINO Python API Tutorial <notebooks/002-openvino-api-with-output.html>`__
* `Basic image classification program with Hello Image Classification <notebooks/001-hello-world-with-output.html>`__
* `Convert a PyTorch model and use it for image background removal <notebooks/205-vision-background-removal-with-output.html>`__
* `C++ Quick Start Example <openvino_docs_get_started_get_started_demos.html>`__ for step-by-step instructions on building and running a basic image classification C++ application.
`C++ Quick Start Example <openvino_docs_get_started_get_started_demos.html>`__ for step-by-step instructions on building and running a basic image classification C++ application.
Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
* `Basic object detection with the Hello Reshape SSD C++ sample <openvino_inference_engine_samples_hello_reshape_ssd_README.html>`_
* `Automatic speech recognition C++ sample <openvino_inference_engine_samples_speech_sample_README.html>`_
* `Basic object detection with the Hello Reshape SSD C++ sample <openvino_inference_engine_samples_hello_reshape_ssd_README.html>`_
* `Automatic speech recognition C++ sample <openvino_inference_engine_samples_speech_sample_README.html>`_
@endsphinxdirective

2
docs/install_guides/installing-openvino-brew.md
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@ -30,7 +30,7 @@ See `Installing Additional Components <#optional-installing-additional-component
* `Homebrew <https://brew.sh/>`_
* `CMake 3.13 or higher <https://cmake.org/download/>`__ (choose "macOS 10.13 or later"). Add ``/Applications/CMake.app/Contents/bin`` to path (for default installation).
* `Python 3.7 - 3.11 <https://www.python.org/downloads/mac-osx/>`__ (choose 3.7 - 3.10). Install and add it to path.
* `Python 3.7 - 3.11 <https://www.python.org/downloads/mac-osx/>`__ . Install and add it to path.
* Apple Xcode Command Line Tools. In the terminal, run ``xcode-select --install`` from any directory to install it.
* (Optional) Apple Xcode IDE (not required for OpenVINO™, but useful for development)

2
tools/pot/openvino/tools/pot/api/README.md
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@ -107,7 +107,7 @@ All subclasses should override the following methods:
- `sampler` - `Sampler` instance that provides a way to iterate over the dataset. (See details below).
- `metric_per_sample` - if `Metric` is specified and this parameter is set to True, then the metric value should be
calculated for each data sample, otherwise for the whole dataset.
calculated for each data sample, otherwise for the whole dataset.
- `print_progress` - print inference progress.
*Returns*