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Michał Kowalczyk
3d31f2d18d
This is the next part of the great loader rework, with a lot of breaking changes: - Complete removal of the "trusted children" thing - now children processes can be spawned arbitrarily and from arbitrary mountpoint types, without any additional configuration needed. - There's a new, required option in the manifest: `libos.entrypoint` - it specifies the URI to the entry binary in the first process. There's no need anymore to name the manifest and the first binary identically. - On SGX, the main binary is not measured in MRENCLAVE anymore - only PAL, LibOS and the manifest are measured. This is enough to bind MRENCLAVE to a specific entrypoint user executable if wanted - it just has to be mounted as a trusted file. - All Graphene SGX enclaves have now exactly the same MRENCLAVE. This is a hash of a "Graphene stub", which can "fork" into one of two states in runtime: initial process or child. The initial process creates a new "Graphene namespace" with a clean state, it can also be attested remotely (contrary to child processes). The initial process can spawn children processes by spawning a Graphene stub and directing it to start in the child mode. It then attests it locally, and if successful, establishes an encrypted pipe, "connects" to its own namespace and treats as trusted (including sending protected files key). - Now, there's only one, central manifest describing the initial state of a Graphene instance which can be spawned from it (previously, each process required a separate manifest which could have different configuration - which wasn't actually supported and didn't make sense design-wise). One downside of central manifests is that all processes require the same enclave configuration (e.g. size), but that was already the case so far because of broken checkpointing code. Also, this is only a temporary problem, which will cease to exist after the introduction of EDMM. - `sgx.static_address` was renamed to `sgx.nonpie_binary` and now has to be inserted manually by users (`sgx_sign` tools doesn't know about the binaries run inside, which can be even provided or generated in runtime by the user's workload). - Caveat: the memory gap for non-PIE executables was removed because it requires adding a new option to the manifest to be cleanly implemented. This is left for some future loader rework PR.
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 ./python3 ./pytorchexample.py - Graphene with SGX:
SGX=1 ./pal_loader ./python3 ./pytorchexample.py