Build OpenVINO on CentOS 7 with Script

Authors: Tong Qiu, Wenyi Zou

Kylin is an operating system based on Linux, developed by academics at the National University of Defense Technology in the People's Republic of China. For more information about Kylin OS, please visit the Wikipedia page at Kylin. In the following sections, we will provide a step-by-step guide to building and running OpenVINO on the Kylin Operating System.

System

The version of Kylin OS we are using is “Kylin HostOS V10”, with the specific version being “V10 (Helium)”. You may obtain this information by executing the command:

cat /etc/*release 

We build OpenVINO using GCC 10.3.1, CMake 3.26.0, and Python 3.9.9, which can all be installed by default using command lines. Next, we will demonstrate how to install these necessary dependencies.

Install Build Dependencies

Instead of executing the ./install_build_dependencies.sh script referenced in  Build OpenVINO™ Runtime for Linux systems, you can directly install the build dependencies using the following command lines:

yum update && yum install -y \
   file \
   cmake3 \
   ninja-build \
   scons \
   gcc \
   gcc-c++ \
   make \
   git \
   fdupes \
   rpm-build \
   tbb-devel \
   libva-devel \
   snappy-devel \
   python3-pip \
   python3-devel

Setup Python Virtual Environment

The next step is to create and activate a Python virtual environment. While this step is optional, we strongly recommend it to ensure better management of your project's dependencies.

python3 -m venv openvino_env
source openvino_env/bin/activate

Following the completion of the steps to build OpenVINO within the Python virtual environment, you can activate OpenVINO alongside the Python virtual environment each time by executing the source command.

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Build OpenVINO with CMake 3.26.0 and GCC 10.3.1

Now we've reached the step to build OpenVINO. First, clone the OpenVINO repository and update its submodules.

git clone https://github.com/openvinotoolkit/openvino.git
cd openvino && git submodule update --init --recursive

Next, install the Python dependencies that are required for building Python wheels.

python3 -m pip install -U pip 
python3 -m pip install -r ./src/bindings/python/wheel/requirements-dev.txt

Then, create the build directory.

mkdir build && cd build

To build OpenVINO with CMake, start by using the command provided below. For enhanced performance, it is recommended to append the -DCMAKE_CXX_FLAGS=-march=native to your command, as this will enable the compiler to optimize the build for your specific hardware by using all supported instruction subsets. Additionally, if you require a Python wheel, include the corresponding build option. Remember to tailor the CMake parameters to fit your particular requirements.

cmake -DCMAKE_BUILD_TYPE=Release .. -DCMAKE_CXX_FLAGS=-march=native -DENABLE_PYTHON=ON -DPython3_EXECUTABLE=/usr/bin/python3 -DENABLE_WHEEL=ON
cmake --build . --parallel

Once the build process is complete, you can install the generated wheel using the pip command.

pip install <openvino_repo>/build/wheel/openvino-*.whl

Quick test for built openvino runtime and openvino-dev tools

You can quickly verify your built and installed OpenVINO setup. Start by creating a model directory and installing the dependencies for the model optimizer.

mkdir ~/ov_models
pip install onnx protobuf openvino-dev[pytorch]

Next, download the resnet50 pytorch model using omz_downloader.

omz_downloader --name resnet-50-pytorch -o ~/ov_models/

Then, convert resnet50 pytorch model to OpenVINO FP32 IR via omz_converter.

omz_converter --name resnet-50-pytorch -o ~/ov_models/ -d ~/ov_models/

Finally, execute benchmark_app with resnet50 FP32 IR model on CPU.

benchmark_app -m ~/ov_models/public/resnet-50-pytorch/FP32/resnet-50-pytorch.xml -d CPU

Additional Details for OpenVINO Setup

If you prefer to build OpenVINO with a different compiler, such as clang, you can modify the CMake configuration step accordingly. To build with the clang compiler, please refer to the website at Clang - Getting Started for instructions on installation and setup. Below is an example of a CMake generation command that specifies clang as the compiler:

cmake -S .. -DCMAKE_C_COMPILER=clang --DCMAKE_CXX_COMPILER=clang++ 
-DCMAKE_CXX_FLAGS=-march=native

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Encrypt Your Dataset and Train Your Model with It Directly

‍Introduction

When we deal with dataset for creating AI models, we need to consider sensitive information managed and stored online in the cloud or on connected devices. Unsecured datasets can be vulnerable to unauthorized access, theft, and misuse, particularly when processed for machine learning workloads. Certain fields, such as industrial or medical sectors, face exceptionally high risks when their data is exposed to these potential threats. For example, if a dataset used to train a detection model for identifying factory process errors is leaked, it can expose sensitive factory process technology. This highlights the importance of safeguarding datasets at every stage, from data storage to model training.

Dataset Management Framework (Datumaro) offers a dataset encryption feature for AI model training. With Datumaro, you can encrypt datasets of any computer vision data format into the DatumaroBinary format. This encrypted dataset can remain encrypted as far as it is needed for decryption. By combining the encrypted dataset with OpenVINO training extensions™, you can use it directly for model training without decryption. Whenever needed, you can use Datumaro once again to decrypt the dataset and convert it back to any major computer vision data format, such as VOC, COCO, or YOLO. Please refer to another posting data_convert for data convert.

Encrypt Your Dataset Using Datumaro

Datumaro provides two ways to encrypt a dataset: CLI and Python API. First, you need to install Datumaro on your system. Please refer to the installation guide here for detailed instructions. Once you have completed the installation of Datumaro, let's first look at the CLI usage. You can encrypt a dataset using the datum convert CLI command as follows:

datum convert -i [input-dataset-path] -o [output-dataset-path] -f datumaro_binary -- --save-media --encryption

The necessary user inputs for this command are as follows:

1. -i <input-dataset-path>: Enter the path to the dataset you want to encrypt in <input-dataset-path>.
2. -o <output-dataset-path>: Enter the path where the encrypted dataset will be produced in <output-dataset-path>.

NOTE:: (Optional) You can additionally specify the data format of your input dataset by entering the -if <input-dataset-format> argument. In most cases, Datumaro can automatically infer the data format of the input dataset, but it might fail. In such cases, you can use the datum detect --show-rejections <input-dataset-path> command to identify the cause of the failure while inferring the data format.

NOTE:: The --save-media argument is a flag that allows you to convert your media files (e.g., images) as well. If this argument is not provided, the encrypted media will not be included in the output directory and only the encrypted annotations are included in the output directory.

Next, let's take a look at how to encrypt a dataset using the Python API. Please examine the following code snippet:

from datumaro import Dataset 

dataset = Dataset.import_from(path="[input-dataset-path]")
dataset.export(save_dir="[output-dataset-path]", 
    format="datumaro_binary", 
    encryption=True, 
    save_media=True,)

You import the dataset by specifying the path of the input dataset in the import_from function as path="<input-dataset-path>". Then, to export the dataset, you specify the path of the output dataset in the save_dir="<output-dataset-path>" of the export function. Similarly, you also need to provide the encryption=True and format="datumaro_binary" keyword arguments as in the CLI example. A more detailed end-to-end example for this can be found in a Jupyter notebook. Please refer to this link for more information.

So far, all the examples have used the datumaro_binary (DatumaroBinary) format for the exported dataset. Currently, the dataset encryption feature is only supported for the datumaro_binary format. DatumaroBinary is a Datumaro's own data format that stores annotation data in binary representation. It is much faster and storage efficient compared to string-based datasets such as COCO based on JSON. For more detailed information about DatumaroBinary, please refer to this link.

How Datumaro Encrypts Your Dataset?

Datumaro uses the Fernet symmetric encryption recipe provided by the cryptography library to encrypt the dataset. Fernet is built on top of a number of standard cryptographic primitives such as AES or HMAC, and hence Fernet guarantees that a message encrypted cannot be manipulated or read without the key. Please refer to this link for detailed information.

When encrypting the dataset, Datumaro generates a secret key through Fernet and saves it as a txt file at the following path: <output-dataset-path>/secret_key.txt. The secret key generated at this path is a 50-characters string, which consists of a randomly generated 32-bytes string encoded in base64, with the prefix datum- added.

cat [output-dataset-path]/secret_key.txt

# A secret key will be randomly generated.
datum-IedFogo3TiyVKF2V1-jT2aO-_r3lWHNQoCWvGEyyjKo=

If you have checked the secret key in this file, you must ensure that it is not in the same location with the dataset. If this secret key is uncovered, an attacker would be able to access the contents of the encrypted dataset. Additionally, this secret key is required when training models using OpenVINO training extensions™ with the encrypted dataset or when decrypting it later. Therefore, you should be careful not to lose this secret key.

The following table briefly shows how the data is encrypted. The binary representation of the data is encrypted, so that the following image cannot be seen by the image viewer.

Train Your Model with the Encrypted Dataset Using OpenVINO Training Extensions™

OpenVINO training extensions™ is a tool that allows convenient training of computer vision models and accelerated inference on Intel® devices by exporting trained models to OpenVINO Intermediate Representation (IR) through a CLI. Within the OpenVINO ecosystem, Datumaro is integrated with OpenVINO training extensions™ as a dataset interface. Therefore, the encrypted dataset can be directly used for model training through OpenVINO training extensions™. For detailed installation instructions of OpenVINO training extensions™, please refer to the following link.

Next, let's explore how to use the encrypted dataset directly for model training through the CLI command.

otx train [template] --train-data-roots [encrypted-dataset-path] --val-data-roots [encrypted-dataset-path] --encryption-key [secret-key]

The user inputs required for this command are as follows:

1. --train-data-roots <encrypted-dataset-path> and --val-data-roots <encrypted-dataset-path>: Specify the path to the encrypted dataset by replacing <encrypted-dataset-path>. Since the DatumaroBinary format uses the same root directory for both the training and validation subsets, both arguments should have the same value.
2. --encryption-key <secret-key>: Provide the secret key corresponding to the encrypted dataset in <secret-key>. This is the 50-character string with the datum- prefix described in the previous section.

NOTE:: <template> is the name of the model template provided by OpenVINO training extensions™. A model template is a recipe for a deep learning model for a specific computer vision task. To explore all the model templates supported by OpenVINO training extensions™, you can use the otx find CLI command or refer to this link.

Decrypt the Encrypted Dataset Using Datumaro

If you want to utilize the encrypted dataset in another AI workload, you need to decrypt the encrypted data. This process reverses the dataset encryption using Datumaro, and encryption-decryption preserves all the information without loss. Similar to the previous section, decryption can be done using the CLI or Python API. Let's first look at decryption using the CLI.

datum convert -i [encrypted-dataset-path] -o [output-dataset-path] -f [output-data-format] --encryption-key [secret-key] --save-media

You can use the same datum convert command as before. However, specify the path to the encrypted dataset as the input dataset path (-i <encrypted-dataset-path>), and provide the secret key, which is a 50-character string with the datum- prefix described in the previous section, as the <secret-key> argument for --encryption-key <secret-key>. Additionally, you can choose any data format supported by Datumaro as the output data format. To learn more about the data formats supported by Datumaro, refer to this link.

Next, let's see how decryption can be done using Python API.

from datumaro import Dataset

dataset = Dataset.import_from(
    path="[encrypted-dataset-path]",
    encryption_key=""
)
dataset.export(
    save_dir="[output-dataset-path]",
    format="[output-data-format]",
    save_media=True
)

Similar to the CLI method, provide the path to the encrypted dataset and the secret key as arguments to the import_from function. For the export function, specify the output dataset path and the output data format.

Conclusion

This post introduced dataset encryption feature provided by Datumaro. It demonstrated how to encrypt a dataset using Datumaro and train a model with the encrypted dataset using OpenVINO training extensions™. Whenever needed you can decrypt it with Datumaro for other AI projects and training frameworks. You can refer to the end-to-end Jupyter notebook example provided on this blog post here for step-by-step guide. The features introduced in this post are available in Datumaro version 1.4.0 or higher and OpenVINO training extensions™ version 1.4.0 or higher.

Datumaro offers a range of useful features for managing datasets besides the dataset encryption feature. You can find examples of other Datumaro features, such as noisy label detection during training with OpenVINO training extensions™, in the Jupyter examples directory. For more information about Datumaro and its capabilities, you can visit the Datumaro documentation page. If you have any questions or requests about using Datumaro, feel free to open an issue here.

Authors: Vinnam Kim, Wonju Lee, Mark Byun, Minje Park‍

Introduction

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OpenVINO™ provides an easy way to deploy your model with the best inference performance on any Intel hardwares. However, to train your own model for deployment you need to prepare a training framework and dataset. Fortunately, there are many ready-to-use training frameworks and implementations. Then, what about the dataset? A specific training framework requires a specific data format, but there are many data formats in the world. For example, in object detection tasks there are data formats such as YOLO, COCO, and Pascal VOC that are widely used. These formats have different directory structures and annotation file formats as well as different extensions such as txt, json, and, xml, respectively. It's tedious task to convert dataset from one format to another whenever you adopt different training framework.

Let's assume you choose Detectron2, which only supports COCO format datasets. If your dataset is formatted as VOC, you have to convert it into COCO format. Below, we compare the directory structures and annotation file formats of both datasets, VOC and COCO. These datasets have distinct formats and you need to implement codes for format conversion at each time of handling different formats. Of course, this is not technically challenging but this may require tedious code work and debugging for several days. It won't be good to repeat this process if you intend to add more datasets with different formats.

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└─ Dataset/
   ├── Annotations/
   │     ├── ann1.xml
   │     ├── ann2.xml
   │     └── ...
   ├── JPEGImages/
   │    ├── img1.jpg
   │    ├── img2.jpg
   │    └── ...
   ├── SegmentationClass/
   │    ├── img1.png
   │    ├── img2.png
   │    └── ...
   ├── SegmentationObject/
   │    ├── img1.png
   │    ├── img2.png
   │    └── ...
   │
   └── ImageSets/
        ├── Main/
        │   ├── test.txt
        |   ├── train.txt
        |   └── ...
        ├── Layout/
        │   ├── test.txt
        |   ├── train.txt
        |   └── ...
        ├── Action/
        │   ├── test.txt
        |   ├── train.txt
        |   └── ...
        └── Segmentation/
            ├── test.txt
            ├── train.txt
            └── ...

└─ Dataset/
    ├── images/
    │   ├── train/
    │   │   ├── img1.jpg
    │   │   ├── img2.jpg
    │   │   └── ...
    │   └── val/
    │       ├── img1.jpg
    │       ├── img2.jpg
    │       └── ...
    └── annotations/
        ├── instances_train.json
        └── ...

<?xml version="1.0" encoding="UTF-8"?>
<annotation>
  <folder>VOC2007</folder>
  <filename>img1.jpg</filename>
  <size>
    <width>20</width>
    <height>10</height>
    <depth>3</depth>
  </size>
  <segmented>1</segmented>
  <object>
    <name>cat</name>
    <pose>Unspecified</pose>
    <truncated>1</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>1</xmin>
      <ymin>2</ymin>
      <xmax>3</xmax>
      <ymax>4</ymax>
    </bndbox>
  </object>
  <object>
    <name>dog</name>
    <bndbox>
      <xmin>4</xmin>
      <ymin>5</ymin>
      <xmax>6</xmax>
      <ymax>7</ymax>
    </bndbox>
    <part>
      <name>head</name>
      <bndbox>
        <xmin>5.5</xmin>
        <ymin>6</ymin>
        <xmax>7.5</xmax>
        <ymax>8</ymax>
      </bndbox>
    </part>
    <actions>
      <other>1</other>
      <jumping>0</jumping>
      <phoning>1</phoning>
      <playinginstrument>0</playinginstrument>
      <reading>1</reading>
      <ridingbike>0</ridingbike>
      <ridinghorse>1</ridinghorse>
      <running>0</running>
      <takingphoto>1</takingphoto>
      <usingcomputer>0</usingcomputer>
      <walking>1</walking>
    </actions>
  </object>
</annotation>

{
  "licenses": [
    {
      "name": "",
      "id": 0,
      "url": ""
    }
  ],
  "info": {
    "contributor": "",
    "date_created": "",
    "description": "",
    "url": "",
    "version": "",
    "year": ""
  },
  "categories": [
    {
      "id": 1,
      "name": "cat",
      "supercategory": ""
    },
    {
      "id": 2,
      "name": "dog",
      "supercategory": ""
    },
  ],
  "images": [
    {
      "id": 5,
      "width": 10,
      "height": 5,
      "file_name": "img1.jpg",
      "license": 0,
      "flickr_url": "",
      "coco_url": "",
      "date_captured": 0
    }
  ],
  "annotations": [
    {
      "id": 1,
      "image_id": 5,
      "category_id": 2,
      "segmentation": [],
      "area": 3.0,
      "bbox": [
        2.0,
        2.0,
        3.0,
        1.0
      ],
      "iscrowd": 0
    }
  ]
}

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‍Dataset Management Framework (Datumaro) is a framework that provides Python API and CLI tools to convert, transform, and analyze datasets. Among the many features of Datumaro, we would like to introduce the data format conversion feature on this blog, which is one of the fundamental feature for handling many datasets with different training frameworks. Datumaro supports the import and export of over 40 computer vision data formats (please take a look at supported formats for details!). This means that you can easily change your data format through Datumaro. If your model training framework can only read specific formats, don't worry. Use Datumaro and convert it!

Train YOLOv8 model and export it to OpenVINO™ model

• Prepare dataset
• Convert dataset with Datumaro
• Train with YOLOv8 and export to OpenVINO™ IR
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YOLOv8 is a well-known model training framework for object detection and tracking, instance segmentation, image classification, and pose estimation tasks. It provides simple CLI commands to train, test, and export a model to OpenVINO™ Intermediate Representation (IR). However, the data format consumed by YOLOv8 is slightly different from the YOLO format itself. Datumaro named it refers to it as YOLO-Ultralytics format. As you can see here, it requires a special meta file to indicate annotation files for each subset and subset files to list subset image files. It further requires them to be placed in an appropriate directory structure. It can be very tedious to go through these details and implement dataset preprocessing when you want to train a model on your custom dataset.

On this blog, we provide an end-to-end example that covers the complete process of converting your dataset, training a model with the converted dataset, and exporting the trained model to OpenVINO™ IR. We understand that dataset conversion can be a tricky process, especially if you have annotated and built your own dataset. Therefore, we will provide an example of converting the dataset created by the popular CVAT annotation tool. By following our step-by-step guide, you will be able to convert your data format easily and accelerate the inference of your trained model with OpenVINO™.

Prepare dataset

In this section, we introduce the steps to export the project annotated by CVAT for the following workflows. You can skip this section if your dataset is formatted as a different data format and is ready to be imported by Datumaro.

NOTE: We used the cats-and-dogs dataset for this example. You can find the reference for this dataset here.

NOTE: You should have three subsets in your project: "train", "val", and "test" (optional). If your dataset has different subset names, you have to rename them. You can do this by using Datumaro's MapSubsets transform.

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We export this project to CVAT for images 1.1 data format. Datumaro can import this data format and export it to YOLO-Ultralytics format which can be consumed by YOLOv8.

Export CVAT project to CVAT for images 1.1 data format. After exporting the dataset, extract it to the cvat_dataset directory.

ls yolo_v8_dataset

You can see the following directory structure:

annotations.xml  images

Convert your dataset using Datumaro

You can convert the dataset located in cvat_dataset using Datumaro's CLI command as follows. For a detailed explanation of the input arguments, see here.

datum convert -i cvat_dataset -if cvat -f yolo_ultralytics -o yolo_v8_dataset -- --save-media

NOTE: If your dataset is not CVAT for images 1.1 format, you can replace -if cvat with the different input format as -if INPUT_FORMAT. Use datum detect CLI command to figure out what format your dataset is.

After the conversion, you can see that yolo_v8_dataset directory is created.

ls yolo_v8_dataset

This directory is structured as follows.

data.yaml  images  labels  test.txt  train.txt  val.txt

Train with YOLOv8 Trainer and Export to OpenVINO™ IR

In this section, we will train the YOLOv8 detector with the dataset converted in the previous section. To train a YOLOv8 detector, please execute the following command.

yolo detect train model=yolov8n.pt data=$(realpath yolo_v8_dataset/data.yaml) project=my-project

NOTE: We use data=$(realpath yolo_v8_dataset/data.yaml) to convert the relative path yolo_v8_dataset/data.yaml to the absolute path. This is because YOLOv8 needs the absolute path for the custom dataset.

After the training, the following command enables testing on the test dataset.

yolo detect val model=my-project/train/weights/best.pt data=$(realpath yolo_v8_dataset/data.yaml) split=test

Lastly, we will export your YOLOv8 detector to OpenVINO™ IR for inference acceleration on Intel devices.

yolo detect export model=my-project/train/weights/best.pt format=openvino

Using this command, the exported IR is created at this directory path, my-project/train/weights/best_openvino_model.

ls my-project/train/weights/best_openvino_model

best.bin  best.xml  metadata.yaml

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Conclusion

This post provided an example of training a YOLOv8 detector on an arbitrary data format by utilizing the data format conversion feature of Datumaro and exporting the model to OpenVINO™ IR. You can refer to the executable Jupyter notebook example provided on this blog post here for step-by-step guide. Datumaro offers a range of useful features for managing datasets beyond data format conversion. You can find examples of other Datumaro features, such as noisy label detection during training with OpenVINO™ Training Extensions, in the Jupyter examples directory. For more information about Datumaro and its capabilities, you can visit the Datumaro documentation page. If you have any questions or requests about using Datumaro, feel free to open an issue here.