OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
aicomputer-visiondeep-learningdeploy-aidiffusion-modelsgenerative-aigood-first-issueinferencellm-inferencenatural-language-processingnlpopenvinooptimize-aiperformance-boostrecommendation-systemspeech-recognitionstable-diffusiontransformersyolo
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* Added Torchscript Backend * First commit for backend with Torch FX Decoder * Merging changes from Torch FX branch * Torch FX initial fixes (Temporary) * Fixed type/shape issues in Torch FX decoder * Added translation for built-in getitem * MaxPool update & Output shape fix (Torch FX) * Torch FX graph outputs fix * Torch FX support for sigmoid and slu_ * Torch FX graph module caching * Torch Fx partitioner cache removed * Torch FX initial getitem replacer added * Index check for torch fx getitem replacer * Debug print removed from partitioner * Added environment variables for pytorch tracing mode and openvino device * FX translation fix for getitem & getitem replacer removed * Added checks for PyTorch tracing mode environment variable * Adding compile mode for fallback * Added more ops for resnet18 * Added a check for environment variable * Generalized addmm to work with torchscript and torchfx * Added the missing batch_norm.default translation * fx_backend: include get_attr ops to the partitions * AddeTODO note t to improvget_attr algorithm * created function for adding get_attr nodes * fx_backend: added aten.mul.Tensor, re-enabled aten.empty.memory_format * fx_backend: Additional op support/improvement for Inception V3 * Added comment for fix 64-bit to 32-bit max int conversion * fx_backend: Update for avg_poolnd to support 3 inputs * Fixed erorr in decoder.py * TorchFX caching fix * Torch backend, op support for Stable Diff. & BERT * Arranged ops in order and added torch tensor mapping * Added support for more ops for super glue * TorchFX: Initial permanent fallback * TorchFX: New ops for improved TorchVision support * TorchFX backend optimizations for partitioning and tmp fallback * working operator updates for superglue * Updates to operators for superglue * Removed max.dim and stack * Cleanup * Cleanup * Fixed a couple of syntax issues * Fixed a couple of syntax issues * Added missing method to TorchFX Decoder * Added missing method to TorchFX Decoder * Removed redundant code for transpose * TorchFX: Initial StableDiffusion support * PyTorch decoder ovtype to ctype fix for int64 * Added ops for distilbert * Fixed few unnecessary include statements * Seperated TorchFX and TorchScript decoders * Modified import statements to reflect two decoders * f64 fix for TorchFX * Import fix for PyTorch backend modules * TorchFX serialize graph for debugging (Temporary) * Serialize and load back feature enabled for TorchFX * Temporary optimization to remove Broadcast * Temporary SoftmaxRehapeElimination pass is added * TorchFX custom model cache directory * PyTorch bitwise translation, conversion checks enabled * Naming fix in make_list_construct * TorchFX: Added comments to Softmax and Slice translations * translate_chunk temporarily removed for TS backend * Fixed linter issues * Addressed clang formatting issues * Fixed few more clang and linter issues * Fixed tests to use ts_decoder * Fixed naming convention issues * Added missing import * Added inlined_inputs to TorchScriptDecoder * Added tests for torch fx backend * Removed magic numbers in PyTorch decoder utils * TorchFX decoder data type fix * Added cast from size_t to int * TorchFX output handling code cleanup * TorchFX: Use detached input tensor * Added missing cast from size_t to int * Added static cast in group_norm * Fixed casting issue in split --------- Co-authored-by: ynimmaga <yamini.nimmagadda@intel.com> Co-authored-by: Cavus Mustafa <mustafa.cavus@intel.com> |
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Contents:
- What is OpenVINO?
- Supported Hardware matrix
- License
- Documentation
- Tutorials
- Products which use OpenVINO
- System requirements
- How to build
- How to contribute
- Get a support
- See also
What is OpenVINO toolkit?
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
This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, GNA, 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 - provides the base API for model representation and modification.
- inference - provides an API to infer models on the device.
- transformations - contains the set of common transformations which are used in OpenVINO plugins.
- low precision transformations - contains the set of transformations that are used in low precision models
- bindings - contains all available OpenVINO bindings which are maintained by the OpenVINO team.
- 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.
- Frontends - contains available OpenVINO frontends that allow reading models from the native framework format.
- Model Optimizer - is a cross-platform command-line tool that facilitates the transition between training and deployment environments, performs static model analysis, and adjusts deep learning models for optimal execution on end-point target devices.
- Post-Training Optimization Tool - is designed to accelerate the inference of deep learning models by applying special methods without model retraining or fine-tuning, for example, post-training 8-bit quantization.
- 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.
| Device | Plugin | Library | ShortDescription |
|---|---|---|---|
| CPU | Intel CPU | openvino_intel_cpu_plugin | 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) |
| ARM CPU | openvino_arm_cpu_plugin | Raspberry Pi™ 4 Model B, Apple® Mac mini with M1 chip, NVIDIA® Jetson Nano™, Android™ devices | |
| GPU | Intel GPU | openvino_intel_gpu_plugin | Intel Processor Graphics, including Intel HD Graphics and Intel Iris Graphics |
| GNA | Intel GNA | openvino_intel_gna_plugin | Intel Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel Pentium Silver J5005 Processor, Intel Pentium Silver N5000 Processor, Intel Celeron J4005 Processor, Intel Celeron J4105 Processor, Intel Celeron Processor N4100, Intel Celeron Processor N4000, Intel Core i3-8121U Processor, Intel Core i7-1065G7 Processor, Intel Core i7-1060G7 Processor, Intel Core i5-1035G4 Processor, Intel Core i5-1035G7 Processor, Intel Core i5-1035G1 Processor, Intel Core i5-1030G7 Processor, Intel Core i5-1030G4 Processor, Intel Core i3-1005G1 Processor, Intel Core i3-1000G1 Processor, Intel Core i3-1000G4 Processor |
OpenVINO™ Toolkit also contains several plugins which simplify loading models on several hardware devices:
| Plugin | Library | ShortDescription |
|---|---|---|
| Auto | openvino_auto_plugin | Auto plugin enables selecting Intel device for inference automatically |
| Auto Batch | openvino_auto_batch_plugin | 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 |
| Hetero | openvino_hetero_plugin | Heterogeneous execution enables automatic inference splitting between several devices |
| Multi | openvino_auto_plugin | Multi plugin enables simultaneous inference of the same model on several devices in parallel |
License
OpenVINO™ Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.
Documentation
User documentation
The latest documentation for OpenVINO™ Toolkit is available here. 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
Developer documentation 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.
Tutorials
The list of OpenVINO tutorials:
Products which use OpenVINO
System requirements
The system requirements vary depending on platform and are available on dedicated pages:
How to build
See How to build OpenVINO to get more information about the OpenVINO build process.
How to contribute
See Contributions Welcome for good first issues.
See CONTRIBUTING for contribution details. Thank you!
Get a support
Report questions, issues and suggestions, using:
- GitHub* Issues
- The
openvinotag on StackOverflow* - Forum
Additional Resources
- OpenVINO Wiki
- OpenVINO Storage
- Additional OpenVINO™ toolkit modules:
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit Release Notes
- Neural Network Compression Framework (NNCF) - a suite of advanced algorithms for model inference optimization including quantization, filter pruning, binarization and sparsity
- OpenVINO™ Training Extensions (OTE) - convenient environment to train Deep Learning models and convert them using OpenVINO for optimized inference.
- OpenVINO™ Model Server (OVMS) - a scalable, high-performance solution for serving deep learning models optimized for Intel architectures
- Computer Vision Annotation Tool (CVAT) - an online, interactive video and image annotation tool for computer vision purposes.
- Dataset Management Framework (Datumaro) - a framework and CLI tool to build, transform, and analyze datasets.
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