Openvino
OpenVINO™ is an open source toolkit for optimizing and deploying AI inference
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What is Openvino?
OpenVINO (Open Visual Inference and Neural network Optimization) is an open-source software toolkit developed by Intel for optimizing, accelerating, and deploying deep learning models. It enables efficient inference across a wide range of Intel hardware platforms including CPUs (x86 and Arm), GPUs, and NPUs, from edge devices to cloud environments.
Key features include support for models from popular frameworks like PyTorch, TensorFlow, ONNX, TensorFlow Lite, and PaddlePaddle, allowing conversion and deployment without original frameworks. The toolkit offers advanced optimization techniques such as quantization, filter pruning, binarization, and sparsity through the Neural Network Compression Framework (NNCF). It includes OpenVINO Model Server (OVMS) for scalable serving, Optimum Intel for optimizing Transformers and Diffusers, and GenAI Repository tools for Generative AI applications.
OpenVINO is designed for machine learning engineers, AI developers, data scientists, and organizations deploying deep learning models in production. It serves industries including healthcare, retail, safety and security, transportation, computer vision, automatic speech recognition, generative AI, and natural language processing. The toolkit provides Python and C++ APIs, interactive Jupyter notebook tutorials, code samples, and an active community for support.
The toolkit accelerates deep learning inference with automatic performance enhancements including asynchronous execution, batch processing, tensor fusion, load balancing, dynamic inference parallelism, and automatic BF16 conversion. It supports versatile use cases from cloud to edge deployment with minimal accuracy drop while maximizing throughput on target hardware.
Openvino pricing
Pricing model: Freemium
OpenVINO is completely free and open-source under the Apache License Version 2.0. There are no licensing fees for any use including commercial deployment. Users can modify and redistribute the toolkit freely. The Intel Distribution of OpenVINO includes the Single Named-User License Type with no cost. All features including model optimization, runtime, OpenVINO Model Server, and ecosystem tools are included at no charge.
Openvino pros
- Open-source and free under Apache License 2.0 with no licensing fees
- Supports models from PyTorch, TensorFlow, ONNX, TensorFlow Lite, and PaddlePaddle
- Optimizes inference on Intel CPUs (x86 and Arm), GPUs, and NPUs
- Advanced model compression with quantization, pruning, binarization, and sparsity via NNCF
- OpenVINO Model Server enables scalable high-performance model serving
- Optimum Intel simplifies optimization of Transformers and Diffusers models
- GenAI Repository and Tokenizers for developing Generative AI applications
- Cross-platform support on Windows, Linux, and macOS
- Deploy from cloud to edge without original frameworks
- Automatic performance enhancements including async execution and batch processing
- Interactive Jupyter notebook tutorials with key feature demonstrations
- Python and C++ APIs with extensive code samples
- Active community with GitHub Issues, Discord, and Stack Overflow support
- Optimizes for computer vision, speech recognition, NLP, and generative AI
- Reduces latency and memory footprint while maintaining accuracy
Openvino cons
- Primarily optimized for Intel hardware, less effective on non-Intel chips
- Model conversion can be complex for very complex neural networks
- Model optimization process is relatively slow
- Limited integration with broader machine learning tool ecosystem
- Support issues can take 2-3 months to resolve for complex problems
- Requires custom layers for models that cannot convert automatically
- Limited support for vehicle recognition differentiation
- Not ideal for Raspberry Pi 4 and some edge devices
- OpenMP runtime conflicts may occur with third-party Intel MKL components
- Resizable input resize algorithms not fully optimized causing performance degradation
Frequently asked questions about Openvino
What is OpenVINO used for?
OpenVINO is used for optimizing, accelerating, and deploying deep learning models for user applications. It streamlines integration and deployment of deep learning models with optimization features for computer vision, automatic speech recognition, generative AI, natural language processing with large and small language models, and many other common tasks. It enables efficient inference from cloud to edge on Intel hardware.
Is OpenVINO free to use?
Yes, OpenVINO is completely free and open-source under the Apache License Version 2.0. It allows modification and redistribution with no licensing fees for commercial deployment. The Intel Distribution of OpenVINO toolkit is free for commercial use.
What hardware does OpenVINO support?
OpenVINO works efficiently on a wide range of Intel hardware platforms including CPUs (x86 and Arm), GPUs (OpenCL capable, integrated and discrete), and AI accelerators (Intel NPU). It is actively developed by Intel to work optimally on Intel hardware, though it is cross-platform on Windows, Linux, and macOS.
Which deep learning frameworks are compatible with OpenVINO?
OpenVINO supports neural network models trained with TensorFlow, PyTorch, ONNX, TensorFlow Lite, and PaddlePaddle. You can convert and deploy models without needing the original frameworks after conversion to OpenVINO Intermediate Representation format.
How do I install OpenVINO?
You can install OpenVINO using pip with the command 'pip install -U openvino'. It is available for Windows, macOS, and Linux operating systems with various installation methods. The quickstart example requires no installation to try OpenVINO capabilities.
What is the OpenVINO Model Server?
OpenVINO Model Server (OVMS) is a scalable, high-performance serving microservice that enables scalability via serving models optimized for Intel architectures. It provides a convenient solution for deploying models in production environments with high throughput.
What optimization techniques does OpenVINO offer?
OpenVINO offers advanced model optimization techniques through NNCF including quantization, filter pruning, binarization, and sparsity. Runtime optimizations include asynchronous execution, batch processing, tensor fusion, load balancing, dynamic inference parallelism, and automatic BF16 conversion to reduce latency and memory footprint.
How do I get support for OpenVINO?
OpenVINO support is available through GitHub Issues for bug reports, OpenVINO channels on the Intel DevHub Discord server, and the openvino tag on Stack Overflow. The active community contributes to enhancement and users can contribute to the open-source toolkit.
What is OpenVINO Intermediate Representation?
OpenVINO Intermediate Representation (IR) consists of an .xml file describing network topology and a .bin file containing weights and biases in binary format. Models must be converted to IR format from ML frameworks using the ovc command line tool or openvino.convert_model() Python API before inference.
Can OpenVINO be used for Generative AI?
Yes, OpenVINO includes the GenAI Repository and OpenVINO Tokenizers specifically for developing and optimizing Generative AI applications. Optimum Intel provides a simple interface to optimize Transformers and Diffusers models for generative AI use cases including large language models.