From 3111e2394c9897f1cf940681949fabc44348b2bb Mon Sep 17 00:00:00 2001
From: Maksim Kutakov <maksim.kutakov@intel.com>
Date: Mon, 19 Dec 2022 18:49:31 +0100
Subject: [PATCH] [CPU][DOCS] Remove recommendation to use partially defined
 shapes (#14675)

---
 docs/OV_Runtime_UG/supported_plugins/CPU.md | 21 ---------------------
 docs/snippets/cpu/dynamic_shape.cpp         |  9 ---------
 docs/snippets/cpu/dynamic_shape.py          |  6 ------
 3 files changed, 36 deletions(-)

diff --git a/docs/OV_Runtime_UG/supported_plugins/CPU.md b/docs/OV_Runtime_UG/supported_plugins/CPU.md
index c0626f025a8ddf..9113d247ae7758 100644
--- a/docs/OV_Runtime_UG/supported_plugins/CPU.md
+++ b/docs/OV_Runtime_UG/supported_plugins/CPU.md
@@ -136,27 +136,6 @@ CPU provides full functional support for models with dynamic shapes in terms of
 
 > **NOTE**: The CPU plugin does not support tensors with dynamically changing rank. In case of an attempt to infer a model with such tensors, an exception will be thrown.
 
-Dynamic shapes support introduces additional overhead on memory management and may limit internal runtime optimizations.
-The more degrees of freedom are used, the more difficult it is to achieve the best performance.
-The most flexible configuration, and the most convenient approach, is the fully undefined shape, which means that no constraints to the shape dimensions are applied.
-However, reducing the level of uncertainty results in performance gains.
-You can reduce memory consumption through memory reuse, achieving better cache locality and increasing inference performance. To do so, set dynamic shapes explicitly, with defined upper bounds.
-
-@sphinxtabset
-
-@sphinxtab{C++}
-@snippet docs/snippets/cpu/dynamic_shape.cpp defined_upper_bound
-@endsphinxtab
-
-@sphinxtab{Python}
-@snippet docs/snippets/cpu/dynamic_shape.py defined_upper_bound
-@endsphinxtab
-
-@endsphinxtabset
-
-> **NOTE**: Using fully undefined shapes may result in significantly higher memory consumption compared to inferring the same model with static shapes.
-> If memory consumption is unacceptable but dynamic shapes are still required, the model can be reshaped using shapes with defined upper bounds to reduce memory footprint.
-
 Some runtime optimizations work better if the model shapes are known in advance.
 Therefore, if the input data shape is not changed between inference calls, it is recommended to use a model with static shapes or reshape the existing model with the static input shape to get the best performance.
 
diff --git a/docs/snippets/cpu/dynamic_shape.cpp b/docs/snippets/cpu/dynamic_shape.cpp
index 74a0ac7637a0b1..f87add1d60ed5e 100644
--- a/docs/snippets/cpu/dynamic_shape.cpp
+++ b/docs/snippets/cpu/dynamic_shape.cpp
@@ -2,15 +2,6 @@
 
 
 int main() {
-    {
-        //! [defined_upper_bound]
-        ov::Core core;
-        auto model = core.read_model("model.xml");
-
-        model->reshape({{ov::Dimension(1, 10), ov::Dimension(1, 20), ov::Dimension(1, 30), ov::Dimension(1, 40)}});
-        //! [defined_upper_bound]
-    }
-
     {
         //! [static_shape]
         ov::Core core;
diff --git a/docs/snippets/cpu/dynamic_shape.py b/docs/snippets/cpu/dynamic_shape.py
index a45ca17d2be4ff..8bfbf5d99eec2a 100644
--- a/docs/snippets/cpu/dynamic_shape.py
+++ b/docs/snippets/cpu/dynamic_shape.py
@@ -4,12 +4,6 @@
 
 from openvino.runtime import Core
 
-#! [defined_upper_bound]
-core = Core()
-model = core.read_model("model.xml")
-model.reshape([(1, 10), (1, 20), (1, 30), (1, 40)])
-#! [defined_upper_bound]
-
 #! [static_shape]
 core = Core()
 model = core.read_model("model.xml")