Taylor Yeonbok Lee
30ddd06159
* Allocate internal buffer to usm_device when one of the input tensor is from usm_device. Allocate output tensors if there is no user which is cpu impl. * Move intermediate buffer allocation to primitive_inst * Allocate to usm_host when the internal buffer is allocated close to limitation of device memory * Remove internal_buffer_info and replace it with vector of layout. Updated conditions to use alloc_type w.r.t the availability. * Allocate internal buffer within primitive_inst construction * Fixed device_mem allocation condition aligned with driver team - Single allocation should be less than CL_DEVICE_MAX_MEM_ALLOC_SIZE - Total allocation for a kernel should be less than CL_DEVICE_GLOBAL_MEM_SIZE * Apply review comment
OpenVINO™ Toolkit
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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