natas_hw
/
openvino
0
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[DOCS] Readme changes (#20818) 

### Details:
 - *item1*
 - *...*

### Tickets:
 - *ticket-id*

---------

Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
This commit is contained in:
Tatiana Savina 2024-04-25 16:48:00 +02:00 committed by GitHub
parent a25a476db2
commit 1320977d89
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
4 changed files with 179 additions and 235 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
CONTRIBUTING.md
View File

@ -27,7 +27,7 @@ product better.
[Feature Request Issue](https://github.com/openvinotoolkit/openvino/issues/new?assignees=octocat&labels=enhancement%2Cfeature&projects=&template=feature_request.yml&title=%5BFeature+Request%5D%3A+)
In both cases, provide a detailed description, including use cases, benefits, and potential challenges.
If your points are especially well aligned with the product vision, they will be included in the
[development roadmap](./ROADMAP.md).
development roadmap.
User feedback is crucial for OpenVINO development and even if your input is not immediately prioritized,
it may be used at a later time or undertaken by the community, regardless of the official roadmap.
@ -37,8 +37,8 @@ product better.
* **Fix Bugs or Develop New Features**
If you want to help improving OpenVINO, choose one of the issues reported in
[GitHub Issue Tracker](https://github.com/openvinotoolkit/openvino/issues) and
[create a Pull Request](./CONTRIBUTING_PR.md) addressing it. Consider one of the
tasks listed as [first-time contributions](https://github.com/orgs/openvinotoolkit/projects/3).
[create a Pull Request](./CONTRIBUTING_PR.md) addressing it. If you want to start with something simple,
check out the [first-time contributions section](#3-start-working-on-your-good-first-issue).
If the feature you want to develop is more complex or not well defined by the reporter,
it is always a good idea to [discuss it](https://github.com/openvinotoolkit/openvino/discussions)
with OpenVINO developers first. Before creating a new PR, check if nobody is already
@ -133,14 +133,21 @@ Choose the component your Good First Issue is related to. You can run tests to m
##### Tools
- [Benchmark Tool](https://github.com/openvinotoolkit/openvino/tree/master/tools/benchmark_tool)
- [Model Optimizer](https://github.com/openvinotoolkit/openvino/tree/master/tools/mo)
- [OpenVINO Model Converter](https://github.com/openvinotoolkit/openvino/tree/master/tools/ovc)
##### Others
- [Documentation](https://github.com/openvinotoolkit/openvino/blob/master/CONTRIBUTING_DOCS.md)
### 3. Start working on your Good First Issue
Use the issue description and locally built OpenVINO to complete the task. Remember that you can always ask users tagged in the "Contact points" section for help! You can also visit [Intel DevHub Discord server](https://discord.gg/7pVRxUwdWG) and ask questions in the channel dedicated to Good First Issue support.
To start contributing, pick a task from the [Good First Issues board](https://github.com/orgs/openvinotoolkit/projects/3).
To be assigned to an issue, simply leave a comment with the `.take` command in the selected issue.
Use the issue description and build OpenVINO locally to complete the task.
You can always ask users tagged in the "Contact points" section for help!
Visit [Intel DevHub Discord server](https://discord.gg/7pVRxUwdWG) and ask
questions in the channel dedicated to Good First Issue support.
### 4. Submit a PR with your changes

284
README.md
View File

@ -1,5 +1,5 @@
<div align="center">
<img src="docs/sphinx_setup/_static/images/img/openvino-logo-purple-black.png" width="400px">
<img src="docs/dev/assets/openvino-logo-purple-black.svg" width="400px">
[![PyPI Status](https://badge.fury.io/py/openvino.svg)](https://badge.fury.io/py/openvino)
[![Anaconda Status](https://anaconda.org/conda-forge/openvino/badges/version.svg)](https://anaconda.org/conda-forge/openvino)
@ -10,208 +10,124 @@
[![brew Downloads](https://img.shields.io/homebrew/installs/dy/openvino)](https://formulae.brew.sh/formula/openvino)
</div>
## Contents:
Welcome to OpenVINO™, an open-source software toolkit for optimizing and deploying deep learning models.
- [What is OpenVINO?](#what-is-openvino-toolkit)
- [Components](#components)
- [Supported Hardware matrix](#supported-hardware-matrix)
- [License](#license)
- [Documentation](#documentation)
- [Tutorials](#tutorials)
- [Products which use OpenVINO](#products-which-use-openvino)
- [System requirements](#system-requirements)
- [How to build](#how-to-build)
- [How to contribute](#how-to-contribute)
- [Get a support](#get-a-support)
- [See also](#see-also)
- **Inference Optimization**: Boost deep learning performance in computer vision, automatic speech recognition, generative AI, natural language processing with large and small language models, and many other common tasks.
- **Flexible Model Support**: Use models trained with popular frameworks such as TensorFlow, PyTorch, ONNX, Keras, and PaddlePaddle.
- **Broad Platform Compatibility**: Reduce resource demands and efficiently deploy on a range of platforms from edge to cloud.
- **Community and Ecosystem**: Join an active community contributing to the enhancement of deep learning performance across various domains.
## What is OpenVINO toolkit?
Check out the [OpenVINO Cheat Sheet](https://docs.openvino.ai/2024/_static/download/OpenVINO_Quick_Start_Guide.pdf) for a quick reference.
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.
- Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks
- Use models trained with popular frameworks like TensorFlow, PyTorch and more
- Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud
## Installation
[Get your preferred distribution of OpenVINO](https://docs.openvino.ai/2024/get-started/install-openvino.html) or use this command for quick installation:
This open-source version includes several components: namely [OpenVINO Model Converter (OVC)], [OpenVINO™ Runtime], as well as CPU, GPU, NPU, multi device and heterogeneous plugins to accelerate deep learning inference on Intel® CPUs and Intel® Processor Graphics.
It supports pre-trained models from [Open Model Zoo], along with 100+ open
source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.
### Components
* [OpenVINO™ Runtime] - is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice.
* [core](./src/core) - provides the base API for model representation and modification.
* [inference](./src/inference) - provides an API to infer models on the device.
* [transformations](./src/common/transformations) - contains the set of common transformations which are used in OpenVINO plugins.
* [low precision transformations](./src/common/low_precision_transformations) - contains the set of transformations that are used in low precision models
* [bindings](./src/bindings) - contains all available OpenVINO bindings which are maintained by the OpenVINO team.
* [c](./src/bindings/c) - C API for OpenVINO™ Runtime
* [python](./src/bindings/python) - Python API for OpenVINO™ Runtime
* [Plugins](./src/plugins) - contains OpenVINO plugins which are maintained in open-source by the OpenVINO team. For more information, take a look at the [list of supported devices](#supported-hardware-matrix).
* [Frontends](./src/frontends) - contains available OpenVINO frontends that allow reading models from the native framework format.
* [OpenVINO Model Converter (OVC)] - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, and adjusts deep learning models for optimal execution on end-point target devices.
* [Samples] - applications in C, C++ and Python languages that show basic OpenVINO use cases.
## Supported Hardware matrix
The OpenVINO™ Runtime can infer models on different hardware devices. This section provides the list of supported devices.
<table>
<thead>
<tr>
<th>Device</th>
<th>Plugin</th>
<th>Library</th>
<th>Short Description</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan=2>CPU</td>
<td> <a href="https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device.html">Intel CPU</a></tb>
<td><b><i><a href="./src/plugins/intel_cpu">openvino_intel_cpu_plugin</a></i></b></td>
<td>Intel Xeon with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel Core Processors with Intel AVX2, Intel Atom Processors with Intel® Streaming SIMD Extensions (Intel® SSE), Intel® Advanced Matrix Extensions (Intel® AMX)</td>
</tr>
<tr>
<td> <a href="https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device.html">ARM CPU</a></tb>
<td><b><i><a href="./src/plugins/intel_cpu">openvino_arm_cpu_plugin</a></i></b></td>
<td>ARM CPUs with armv7a and higher, ARM64 CPUs with arm64-v8a and higher, Apple® Mac with Apple silicon
</tr>
<tr>
<td>GPU</td>
<td><a href="https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device.html">Intel GPU</a></td>
<td><b><i><a href="./src/plugins/intel_gpu">openvino_intel_gpu_plugin</a></i></b></td>
<td>Intel Processor Graphics, including Intel HD Graphics and Intel Iris Graphics</td>
</tr>
<tr>
<td>NPU</td>
<td><a href="https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/npu-device.html">Intel NPU</a></td>
<td><b><i><a href="./src/plugins/intel_npu">openvino_intel_npu_plugin</a></i></b></td>
<td>Intel® Core™ Ultra Processors</td>
</tr>
</tbody>
</table>
OpenVINO™ Toolkit also contains several plugins which simplify loading models on several hardware devices:
<table>
<thead>
<tr>
<th>Plugin</th>
<th>Library</th>
<th>Short Description</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/auto-device-selection.html">Auto</a></td>
<td><b><i><a href="./src/plugins/auto">openvino_auto_plugin</a></i></b></td>
<td>Auto plugin enables selecting Intel device for inference automatically</td>
</tr>
<tr>
<td><a href="https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/automatic-batching.html">Auto Batch</a></td>
<td><b><i><a href="./src/plugins/auto_batch">openvino_auto_batch_plugin</a></i></b></td>
<td>Auto batch plugin performs on-the-fly automatic batching (i.e. grouping inference requests together) to improve device utilization, with no programming effort from the user</td>
</tr>
<tr>
<td><a href="https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/hetero-execution.html">Hetero</a></td>
<td><b><i><a href="./src/plugins/hetero">openvino_hetero_plugin</a></i></b></td>
<td>Heterogeneous execution enables automatic inference splitting between several devices</td>
</tr>
<tr>
<td><a href="https://docs.openvino.ai/2024/openvino-workflow/running-inference/inference-devices-and-modes/multi-device.html">Multi</a></td>
<td><b><i><a href="./src/plugins/auto">openvino_auto_plugin</a></i></b></td>
<td>Multi plugin enables simultaneous inference of the same model on several devices in parallel</td>
</tr>
</tbody>
</table>
## License
OpenVINO™ Toolkit is licensed under [Apache License Version 2.0](LICENSE).
By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
## Telemetry
OpenVINO™ collects software performance and usage data for the purpose of improving OpenVINO™ tools. This data is collected directly by OpenVINO™ or through the use of Google Analytics 4.
You can opt-out at any time by running the command:
``` bash
opt_in_out --opt_out
```sh
pip install openvino
```
More Information is available at https://docs.openvino.ai/latest/openvino_docs_telemetry_information.html.
Check [system requirements](https://docs.openvino.ai/2024/about-openvino/system-requirements.html) and [supported devices](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html) for detailed information.
## Tutorials and Examples
[OpenVINO Quickstart example](https://docs.openvino.ai/2024/get-started.html) will walk you through the basics of deploying your first model.
Learn how to optimize and deploy popular models with the [OpenVINO Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)📚:
- [Create an LLM-powered Chatbot using OpenVINO](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/llm-chatbot/llm-chatbot.ipynb)
- [YOLOv8 Optimization](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/quantizing-model-with-accuracy-control/yolov8-quantization-with-accuracy-control.ipynb)
- [Text-to-Image Generation](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/notebooks/controlnet-stable-diffusion/controlnet-stable-diffusion.ipynb)
Here are easy-to-follow code examples demonstrating how to run PyTorch and TensorFlow model inference using OpenVINO:
**PyTorch Model**
```python
import openvino as ov
import torch
import torchvision
# load PyTorch model into memory
model = torch.hub.load("pytorch/vision", "shufflenet_v2_x1_0", weights="DEFAULT")
# convert the model into OpenVINO model
example = torch.randn(1, 3, 224, 224)
ov_model = ov.convert_model(model, example_input=(example,))
# compile the model for CPU device
core = ov.Core()
compiled_model = core.compile_model(ov_model, 'CPU')
# infer the model on random data
output = compiled_model.infer_new_request({0: example.numpy()})
```
**TensorFlow Model**
```python
import numpy as np
import openvino as ov
import tensorflow as tf
# load TensorFlow model into memory
model = tf.keras.applications.MobileNetV2(weights='imagenet')
# convert the model into OpenVINO model
ov_model = ov.convert_model(model)
# compile the model for CPU device
core = ov.Core()
compiled_model = core.compile_model(ov_model, 'CPU')
# infer the model on random data
data = np.random.rand(1, 224, 224, 3)
output = compiled_model.infer_new_request({0: data})
```
OpenVINO also supports CPU, GPU, and NPU devices and works with models in TensorFlow, PyTorch, ONNX, TensorFlow Lite, PaddlePaddle model formats.
With OpenVINO you can do automatic performance enhancements at runtime customized to your hardware (preserving model accuracy), including:
asynchronous execution, batch processing, tensor fusion, load balancing, dynamic inference parallelism, automatic BF16 conversion, and more.
## OpenVINO Ecosystem
- [🤗Optimum Intel](https://github.com/huggingface/optimum-intel) - a simple interface to optimize Transformers and Diffusers models.
- [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - advanced model optimization techniques including quantization, filter pruning, binarization, and sparsity.
- [GenAI Repository](https://github.com/openvinotoolkit/openvino.genai) and [OpenVINO Tokenizers](https://github.com/openvinotoolkit/openvino_tokenizers) - resources and tools for developing and optimizing Generative AI applications.
- [OpenVINO™ Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving models optimized for Intel architectures.
- [Intel® Geti™](https://geti.intel.com/) - an interactive video and image annotation tool for computer vision use cases.
Check out the [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) repository to discover a collection of community-made AI projects based on OpenVINO!
## Documentation
### User documentation
[**User documentation**](https://docs.openvino.ai/) contains detailed information about OpenVINO and guides you from installation through optimizing and deploying models for your AI applications.
The latest documentation for OpenVINO™ Toolkit is available [here](https://docs.openvino.ai/). This documentation contains detailed information about all OpenVINO components and provides all the important information you may need to create an application based on binary OpenVINO distribution or own OpenVINO version without source code modification.
[**Developer documentation**](./docs/dev/index.md) focuses on how OpenVINO [components](./docs/dev/index.md#openvino-components) work and describes [building](./docs/dev/build.md) and [contributing](./CONTRIBUTING.md) processes.
### Developer documentation
## Contribution and Support
[Developer documentation](./docs/dev/index.md) contains information about architectural decisions which are applied inside the OpenVINO components. This documentation has all necessary information which could be needed in order to contribute to OpenVINO.
Check out [Contribution Guidelines](./CONTRIBUTING.md) for more details.
Read the [Good First Issues section](./CONTRIBUTING.md#3-start-working-on-your-good-first-issue), if you're looking for a place to start contributing. We welcome contributions of all kinds!
## Tutorials
You can ask questions and get support on:
The list of OpenVINO tutorials:
- [Jupyter notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
## Products which use OpenVINO
- [OpenCV](https://opencv.org/)
- [ONNX Runtime](https://onnxruntime.ai/)
- [OpenVINO™ Integration with TensorFlow](https://www.intel.com/content/www/us/en/developer/tools/devcloud/edge/build/ovtfoverview.html)
- [TNN](https://github.com/Tencent/TNN/tree/master)
You can also check out [Awesome OpenVINO](https://github.com/openvinotoolkit/awesome-openvino) to see all the community-made projects using OpenVINO!
## System requirements
The system requirements vary depending on platform and are available on dedicated pages:
- [Linux](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-linux.html)
- [Windows](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-windows.html)
- [macOS](https://docs.openvino.ai/2024/get-started/install-openvino/install-openvino-macos.html)
## How to build
See [How to build OpenVINO](./docs/dev/build.md) to get more information about the OpenVINO build process.
## How to contribute
See [Contributions Welcome](https://github.com/openvinotoolkit/openvino/issues/17502) for good first issues.
See [CONTRIBUTING](./CONTRIBUTING.md) for contribution details. Thank you!
Visit [Intel DevHub Discord server](https://discord.gg/7pVRxUwdWG) if you need help or wish to talk to OpenVINO developers. You can go to the channel dedicated to Good First Issue support if you are working on a task.
## Take the issue
If you wish to be assigned to an issue please add a comment with `.take` command.
## Get support
Report questions, issues and suggestions, using:
* [GitHub* Issues](https://github.com/openvinotoolkit/openvino/issues)
* The [`openvino`](https://stackoverflow.com/questions/tagged/openvino) tag on StackOverflow\*
* [Forum](https://software.intel.com/en-us/forums/computer-vision)
* OpenVINO channels on the [Intel DevHub Discord server](https://discord.gg/7pVRxUwdWG)
* [GitHub Issues](https://github.com/openvinotoolkit/openvino/issues).
* OpenVINO channels on the [Intel DevHub Discord server](https://discord.gg/7pVRxUwdWG).
* The [`openvino`](https://stackoverflow.com/questions/tagged/openvino) tag on Stack Overflow\*.
## Additional Resources
* [OpenVINO Wiki](https://github.com/openvinotoolkit/openvino/wiki)
* [OpenVINO Storage](https://storage.openvinotoolkit.org/)
* Additional OpenVINO™ toolkit modules:
* [openvino_contrib](https://github.com/openvinotoolkit/openvino_contrib)
* [Intel® Distribution of OpenVINO™ toolkit Product Page](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
* [Intel® Distribution of OpenVINO™ toolkit Release Notes](https://software.intel.com/en-us/articles/OpenVINO-RelNotes)
* [Neural Network Compression Framework (NNCF)](https://github.com/openvinotoolkit/nncf) - a suite of advanced algorithms for model inference optimization including quantization, filter pruning, binarization and sparsity
* [OpenVINO™ Training Extensions (OTE)](https://github.com/openvinotoolkit/training_extensions) - convenient environment to train Deep Learning models and convert them using OpenVINO for optimized inference.
* [OpenVINO™ Model Server (OVMS)](https://github.com/openvinotoolkit/model_server) - a scalable, high-performance solution for serving deep learning models optimized for Intel architectures
* [Computer Vision Annotation Tool (CVAT)](https://github.com/opencv/cvat) - an online, interactive video and image annotation tool for computer vision purposes.
* [Dataset Management Framework (Datumaro)](https://github.com/openvinotoolkit/datumaro) - a framework and CLI tool to build, transform, and analyze datasets.
* [Product Page](https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html)
* [Release Notes](https://docs.openvino.ai/2024/about-openvino/release-notes-openvino.html)
* [OpenVINO Blog](https://blog.openvino.ai/)
* [OpenVINO™ toolkit on Medium](https://medium.com/@openvino)
## License
OpenVINO™ Toolkit is licensed under [Apache License Version 2.0](LICENSE).
By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
---
\* Other names and brands may be claimed as the property of others.
[Open Model Zoo]:https://github.com/openvinotoolkit/open_model_zoo
[OpenVINO™ Runtime]:https://docs.openvino.ai/2024/openvino-workflow/running-inference.html
[OpenVINO Model Converter (OVC)]:https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html#convert-a-model-in-cli-ovc
[Samples]:https://github.com/openvinotoolkit/openvino/tree/master/samples

3
docs/dev/assets/openvino-logo-purple-black.svg Normal file
View File

@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:7a2ad6ccc8a33a5a62f67d2bee37211e9e8998cacba9443c5799e5e1efb8ef68
size 6804

22
docs/dev/index.md
View File

@ -18,7 +18,7 @@ flowchart LR
click onnx "https://github.com/onnx/onnx"
end
mo{{Model Optimizer}}
mo{{Model Converter}}
classDef mos fill:#B1D272, stroke: #8BAE46, color: #000022
class mo mos
@ -55,6 +55,7 @@ flowchart LR
openvino--infer--->cpu
openvino--infer--->gpu
openvino--infer--->npu
openvino--infer--->auto
classDef blue1 fill:#76CEFF, stroke: #00A3F6, color: #000022
class openvino blue1
@ -95,7 +96,24 @@ The OpenVINO Repository includes the following components. Click on the componen
</code>
</pre>
### OpenVINO Component Structure
### OpenVINO Components
OpenVINO Components include:
* [OpenVINO™ Runtime](https://docs.openvino.ai/2024/openvino-workflow/running-inference.html) - is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice.
* [core](./src/core) - provides the base API for model representation and modification.
* [inference](./src/inference) - provides an API to infer models on the device.
* [transformations](./src/common/transformations) - contains the set of common transformations which are used in OpenVINO plugins.
* [low precision transformations](./src/common/low_precision_transformations) - contains the set of transformations that are used in low precision models
* [bindings](./src/bindings) - contains all available OpenVINO bindings which are maintained by the OpenVINO team.
* [c](./src/bindings/c) - C API for OpenVINO™ Runtime
* [python](./src/bindings/python) - Python API for OpenVINO™ Runtime
* [Plugins](./src/plugins) - contains OpenVINO plugins which are maintained in open-source by the OpenVINO team. For more information, take a look at the [list of supported devices](#supported-hardware-matrix).
* [Frontends](./src/frontends) - contains available OpenVINO frontends that allow reading models from the native framework format.
* [OpenVINO Model Converter (OVC)](https://docs.openvino.ai/2024/openvino-workflow/model-preparation.html) - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, and adjusts deep learning models for optimal execution on end-point target devices.
* [Samples](https://github.com/openvinotoolkit/openvino/tree/master/samples) - applications in C, C++ and Python languages that show basic OpenVINO use cases.
#### OpenVINO Component Structure
The OpenVINO component contains all dependencies (for example, third-party, tests, documentation, and others). An example component structure with comments and marks for optional folders is presented below.