OpenVINO™ Project Details
Overview
OpenVINO™ (Open Visual Inference and Neural Network Optimization) is an open-source deep learning inference optimization toolkit developed by Intel. This project focuses on improving the inference performance of AI models on various hardware platforms, particularly in areas such as computer vision, automatic speech recognition, generative AI, and natural language processing.
The core philosophy of OpenVINO™ is to enable developers to easily deploy trained deep learning models into production environments, achieving optimal inference performance on both edge devices and cloud servers.
Core Features and Characteristics
1. Inference Optimization
- Deep Learning Performance Enhancement: Specifically optimized for tasks such as computer vision, automatic speech recognition, generative AI, and natural language processing.
- Large Language Model Support: Supports efficient inference for both large and small language models.
- Multi-Task Optimization: Covers a wide range of common AI application scenarios.
2. Flexible Model Support
- Multi-Framework Compatibility: Supports major deep learning frameworks including PyTorch, TensorFlow, ONNX, Keras, PaddlePaddle, and JAX/Flax.
- Hugging Face Integration: Directly integrates transformers and diffusers models from the Hugging Face Hub via Optimum Intel.
- No Original Framework Required: Models can be converted and deployed without the original training framework.
3. Broad Platform Compatibility
- CPU Support: Optimization for x86 and ARM architecture CPUs.
- GPU Support: Intel integrated and discrete graphics cards.
- AI Accelerators: Intel NPU (Neural Network Processing Unit).
- Edge-to-Cloud: Comprehensive deployment support from edge devices to cloud servers.
4. Rich APIs and Tools
- Multi-Language APIs: Provides programming interfaces in various languages including C++, Python, C, and NodeJS.
- GenAI API: API interfaces specifically optimized for generative AI.
- Model Conversion Tools: Convenient tools for model format conversion and optimization.
Main Components and Ecosystem
Core Tools and Libraries
- Neural Network Compression Framework (NNCF): Advanced model optimization techniques including quantization, filter pruning, binarization, and sparsity.
- OpenVINO GenAI: Resources and tools specifically for generative AI applications.
- OpenVINO Tokenizers: Tokenization tools for developing and optimizing generative AI applications.
Services and Platforms
- OpenVINO™ Model Server (OVMS): Scalable, high-performance model serving solution.
- Intel® Geti™: Interactive video and image annotation tool.
Integrations and Partnerships
- 🤗Optimum Intel: Deep integration with the Hugging Face API.
- Torch.compile: Supports JIT compilation optimization for native PyTorch applications.
- vLLM Integration: Enhances vLLM's fast model serving capabilities.
- ONNX Runtime: Serves as an execution provider for ONNX Runtime.
- LlamaIndex and LangChain: Deep integration with mainstream AI frameworks.
- Keras 3: Supports the Keras 3 multi-backend deep learning framework.
Quick Start Examples
PyTorch Model Inference
import openvino as ov
import torch
import torchvision
# Load the PyTorch model into memory
model = torch.hub.load("pytorch/vision", "shufflenet_v2_x1_0", weights="DEFAULT")
# Convert the model to an OpenVINO model
example = torch.randn(1, 3, 224, 224)
ov_model = ov.convert_model(model, example_input=(example,))
# Compile the model for the CPU device
core = ov.Core()
compiled_model = core.compile_model(ov_model, 'CPU')
# Infer the model on random data
output = compiled_model({0: example.numpy()})
TensorFlow Model Inference
import numpy as np
import openvino as ov
import tensorflow as tf
# Load the TensorFlow model into memory
model = tf.keras.applications.MobileNetV2(weights='imagenet')
# Convert the model to an OpenVINO model
ov_model = ov.convert_model(model)
# Compile the model for the 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({0: data})
Learning Resources
Official Documentation and Tutorials
Practical Examples
- OpenVINO Notebooks: Rich Jupyter notebook tutorials.
- LLM Chatbot Creation
- YOLOv11 Optimization
- Text-to-Image Generation
- Multimodal Assistant Development
- Speech Recognition Application
Community Resources
Technical Advantages
Performance Optimization
- Deeply optimized for Intel hardware architectures.
- Supports various hardware acceleration technologies.
- Provides detailed performance benchmark data.
Ease of Use
- Simple installation process:
pip install -U openvino
- Rich code examples and tutorials.
- Comprehensive documentation and community support.
Flexibility
- Supports multiple deep learning frameworks.
- Cross-platform deployment capabilities.
- Extensible architecture design.
Installation and System Requirements
Quick Installation
pip install -U openvino
System Requirements
Community and Support
Get Help
Contribution Guidelines
License and Privacy
License
The OpenVINO™ toolkit is licensed under the Apache License Version 2.0 open-source license.
Data Collection
OpenVINO™ collects software performance and usage data to improve the tool. You can opt out using the following command:
opt_in_out --opt_out
Summary
OpenVINO™ is a powerful and easy-to-use open-source AI inference optimization toolkit that provides developers with a complete solution from model training to production deployment. Its main advantages include:
- Comprehensive Framework Support: Compatible with all major deep learning frameworks.
- Excellent Performance: Deeply optimized for Intel hardware, providing superior inference performance.
- Wide Range of Applications: Covers multiple fields such as computer vision, NLP, and generative AI.
- Rich Ecosystem: Deeply integrated with mainstream platforms such as Hugging Face, PyTorch, and TensorFlow.
- Active Community: Comprehensive documentation, tutorials, and community support.