94caf7987e
This commit is a result of debugging a rare race condition during build
of some of our examples, which resulted in "enclave EINIT failed -
Invalid measurement" error.
It turns out that Make in versions that doesn't support the `&:`
operator ("Rules with Grouped Targets") silently ignores it and calls
the recipe for each target separately, without even a warning.
Signed-off-by: Michał Kowalczyk <mkow@invisiblethingslab.com>
PyTorch
This directory contains steps and artifacts to run a PyTorch sample workload on Graphene. Specifically, this example uses a pre-trained model for image classification. We tested this on Ubuntu 16.04 and 18.04. The sample uses the package versions of Python (3.5 and 3.6) coming with each distribution.
The workload reads an image from a file on disk input.jpg and runs the classifier to detect what
object is present in the image. For the image shipping with the workload, the output should be
[('Labrador retriever', 41.58518600463867), ('golden retriever', 16.59165382385254), ('Saluki, gazelle hound', 16.286855697631836), ('whippet', 2.853914976119995), ('Ibizan hound, Ibizan Podenco', 2.3924756050109863)]
With high probability (41%) the classifier detected the image to contain a Labrador retriever.
Pre-requisites
The following steps should suffice to run the workload on a stock Ubuntu 16.04 and 18.04 installation.
sudo apt install libnss-mdns libnss-myhostnameto install additional DNS-resolver libraries.sudo apt install python3-pip lsb-releaseto installpipandlsb_release. The former is required to install additional Python packages while the latter is used by the Makefile.pip3 install --user torchvision pillowto install the torchvision and pillow Python packages and their dependencies (usually in $HOME/.local). WARNING: This downloads several hundred megabytes of data!make download_modelto download and save the pre-trained model. WARNING: this downloads about 200MB of data!
Build
Run make to build the non-SGX version and make SGX=1 to build the SGX version.
Run
Execute any one of the following commands to run the workload
- natively:
python3 pytorchexample.py - Graphene w/o SGX:
./pal_loader ./pytorch ./pytorchexample.py - Graphene with SGX:
SGX=1 ./pal_loader ./pytorch ./pytorchexample.py