Michał Kowalczyk
6c7ff2d3a6
We decided to merge the sample app integrations submodule back because working with git submodules turned out to be really painful. The only blocker for this was the fact, that previously it contained a lot of binary blobs and copy-pasted sources, but this was cleaned up recently. Credits: (authors of particular integration examples, extracted from commits and PR history in https://github.com/oscarlab/graphene-tests) apache: Chia-Che Tsai <chiache@tamu.edu>, Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> bash: Chia-Che Tsai <chiache@tamu.edu>, Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> blender: borysp <borysp@invisiblethingslab.com> busybox: borysp <borysp@invisiblethingslab.com> capnproto: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> curl: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> gcc: Thomas Knauth <thomas.knauth@intel.com> lighttpd: Chia-Che Tsai <chiache@tamu.edu>, Thomas Knauth <thomas.knauth@intel.com> lmbench: Chia-Che Tsai <chiache@tamu.edu> memcached: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> nginx: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> nodejs: jack.wxz <jack.wxz@alibaba-inc.com> nodejs-express-server: Eduardo Rodriguez <erodrig@us.ibm.com> openvino: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> python-scipy-insecure: Chia-Che Tsai <chiache@tamu.edu>, Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> python-simple: Chia-Che Tsai <chiache@tamu.edu>, Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> pytorch: Thomas Knauth <thomas.knauth@intel.com> r: Chia-Che Tsai <chiache@tamu.edu> redis: Dmitrii Kuvaiskii <dmitrii.kuvaiskii@intel.com> tensorflow: Thomas Knauth <thomas.knauth@intel.com> LTP was moved to LibOS/shim/test/ltp. It was recently rewritten by Wojtek Porczyk <woju@invisiblethingslab.com>.
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OpenVINO
This directory contains a Makefile and a template manifest for the most recent version of OpenVINO toolkit (as of this writing, version 2019_R2). We use the "Object Detection C++ Sample SSD" (object_detection_sample_ssd) example from the OpenVINO distribution as a concrete application running under Graphene-SGX. We test only the CPU backend (i.e., no GPU or FPGA). This was tested on a machine with SGX v1 and Ubuntu 16.04.
The Makefile and the template manifest contain extensive comments. Please review them to understand the requirements for OpenVINO/object_detection_sample_ssd running under Graphene-SGX.
We build OpenVINO from the source code instead of using an existing installation. Note: the build process requires ~1.1GB of disk space and takes ~20 minutes.
We also download the Open Model Zoo repository and use the SSD300 pre-trained model from it. Note: the model zoo requires ~350MB of disk space.
Prerequisites
For Ubuntu 16.04, install the following prerequisite packages:
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Install CMake version >= 3.7.2 (on Ubuntu 16.04, this may require installing Cmake from a non-official APT repository like Kitware).
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Install libusb version >= 1.0.0.
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Install packages for Python3:
pip3 install pyyaml numpy networkx test-generator defusedxml protobuf>=3.6.1
Quick Start
# build OpenVINO together with object_detection_sample_ssd and the final manifest;
# note that it also downloads the SSD300 model and transforms it from the Caffe
# format to an optimized Intermediate Representation (IR)
make SGX=1
# run original OpenVINO/object_detection_sample_ssd
# note that this assumes the Release build of OpenVINO (no DEBUG=1)
./openvino/inference-engine/bin/intel64/Release/object_detection_sample_ssd -i images/horses.jpg -m model/VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.xml -d CPU
# run OpenVINO/object_detection_sample_ssd in non-SGX Graphene
./pal_loader openvino.manifest -i images/horses.jpg -m model/VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.xml -d CPU
# run OpenVINO/object_detection_sample_ssd in Graphene-SGX
SGX=1 ./pal_loader openvino.manifest.sgx -i images/horses.jpg -m model/VGG_VOC0712Plus_SSD_300x300_ft_iter_160000.xml -d CPU
# Each of these commands produces an image out_0.bmp with detected objects
xxd out_0.bmp # or open in any image editor