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update nncf version (openvinotoolkit#1330)
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13 files changed

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-34
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13 files changed

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‎.docker/Pipfile

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@@ -23,7 +23,7 @@ librosa = ">=0.8.1"
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matplotlib = ">=3.4,<3.5.3"
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monai = ">=0.9.1,<1.0.0"
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nbval = "*"
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nncf = "==2.5.0"
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nncf = "==2.6.0"
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numpy = ">=1.21.0"
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onnx = ">=1.11.0"
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opencv-python = "*"

‎.docker/Pipfile.lock

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‎notebooks/110-ct-segmentation-quantize/110-ct-scan-live-inference.ipynb

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{
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"cells": [
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -q \"openvino>=2023.1.0\" \"monai>=0.9.1,<1.0.0\""
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"%pip install -q \"openvino>=2023.1.0\" \"monai>=0.9.1,<1.0.0\" \"nncf>=2.5.0\""
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]
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},
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"attachments": {},
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"metadata": {},
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"attachments": {},
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"cell_type": "markdown",
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"attachments": {},
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"attachments": {},
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"tags": []
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"attachments": {},
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‎notebooks/112-pytorch-post-training-quantization-nncf/112-pytorch-post-training-quantization-nncf.ipynb

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"attachments": {},
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"attachments": {},
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"cell_type": "markdown",
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"source": [
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"source": [
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"# Install openvino package\n",
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"%pip install -q \"openvino>=2023.1.0\" torch torchvision\n",
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"%pip install -q \"git+https://github.com/openvinotoolkit/nncf.git@release_v260\""
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"%pip install -q \"nncf>=2.6.0\""
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]
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"source": [
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"It should be noted that the inference time for the quantized PyTorch model is longer than that of the original model, as fake quantizers are added to the model by NNCF. However, the model's performance will significantly improve when it is in the OpenVINO Intermediate Representation (IR) format."
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"attachments": {},
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"source": [

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