Maxim Shevtsovandapankratovantonp
5779fb4a22
* Optimized Infer Request Scheduling * Fixed misprint * Brushing the code and comments a bit * further brushing of the ScheduleToWorkerRequest: moving the task execution directly into the loop over devices (avoids pointers and 'else' clause) * 1) zero-copy (assuming determenistic app-level scheduling) for the multi-device, via "borrowing" the corresponding device-specific blobs and letting the app to implicitly use these 2) Initial MULTI section in the opt guide (primarily to document a tip on helping the MULTI to keep the zero-copy path) Co-authored-by: apankratovantonp <anton.pankratov@intel.com>
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.
This open source version includes several components: namely Model Optimizer, ngraph and Inference Engine, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as Caffe*, TensorFlow*, MXNet* and ONNX*.
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Deep Learning Deployment 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.
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