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Emil Hemdal
f25510a259
The documentation currently specifies SGX_SIGNER_KEY as the parameter to enable Graphene to find your keys. Some examples don't use an environment parameter at all for the key to sign the enclave, this commit fixes that.
This example demonstrates how to run TensorFlow (v1.9) Lite's label_image example on Graphene. Reads an input image image.bmp from the current directory and uses TensorFlow lite and the Inception v3 model to label the image.
Known limitations:
- Tested on Ubuntu 16.04 with Graphene commit 030a088. Ubuntu 18.04 should work, but have not tested.
To install build dependencies on Ubuntu 16.04 there is a convenience target invoked with make install-dependencies-ubuntu. This also serves as a starting point to figure out which packages to install on newer releases of Ubuntu.
To build TensorFlow and Graphene artifacts:
- without SGX do
make - with SGX do
make SGX=1
To run the image labeling example of TensorFlow:
- without Graphene do
make run-native - with Graphene do
make run-graphene - with Graphene-SGX do
make SGX=1 run-graphene