cocos/test/manual/algo

Algorithm

Agent accepts binaries programs, python scripts, Docker images and wasm files. It runs them in a sandboxed environment and returns the output.

Python Example

To test this examples work on your local machine, you need to install the following dependencies:

pip install -r requirements.txt

This can be done in a virtual environment.

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

To run the example, you can use the following command:

python3 test/manual/algo/addition.py

The addition example is a simple algorithm to demonstrate you can run an algorithm without any external dependencies and input arguments. It returns the sum of two numbers.

python3 test/manual/algo/lin_reg.py

The linear regression example is a more complex algorithm that requires external dependencies.It returns a linear regression model trained on the iris dataset found here for demonstration purposes.

python3 test/manual/algo/lin_reg.py predict results.zip  test/manual/data

This will make inference on the results of the linear regression model.

To run the examples in the secure VM (SVM) by the Agent, you can use the following command:

go run ./test/computations/main.go ./test/manual/algo/lin_reg.py public.pem false ./test/manual/data/iris.csv

This command is run from the root directory of the project. This will start the computation server.

In another window, you can run the following command:

sudo MANAGER_QEMU_SMP_MAXCPUS=4 MANAGER_GRPC_URL=localhost:7001 MANAGER_LOG_LEVEL=debug MANAGER_QEMU_USE_SUDO=false  MANAGER_QEMU_ENABLE_SEV=false MANAGER_QEMU_SEV_CBITPOS=51 MANAGER_QEMU_ENABLE_SEV_SNP=false MANAGER_QEMU_OVMF_CODE_FILE=/usr/share/edk2/x64/OVMF_CODE.fd MANAGER_QEMU_OVMF_VARS_FILE=/usr/share/edk2/x64/OVMF_VARS.fd go run main.go

This command is run from the manager main directory. This will start the manager. Make sure you have already built the qemu image.

In another window, you can run the following command:

./build/cocos-cli algo ./test/manual/algo/lin_reg.py ./private.pem -a python -r ./test/manual/algo/requirements.txt

make sure you have built the cocos-cli. This will upload the algorithm and the requirements file.

Next we need to upload the dataset

./build/cocos-cli data ./test/manual/data/iris.csv ./private.pem

After some time when the results are ready, you can run the following command to get the results:

./build/cocos-cli result ./private.pem

This will return the results of the algorithm.

To make inference on the results, you can use the following command:

python3 test/manual/algo/lin_reg.py predict results.zip  test/manual/data

For addition example, you can use the following command:

./build/cocos-cli ./test/manual/algo/addition.py public.pem false

./build/cocos-cli algo ./test/manual/algo/addition.py ./private.pem -a python

./build/cocos-cli result ./private.pem

Docker Example

Here we will use the docker with the linear regression example (lin_reg.py). Throughout the example, we assume that our current working directory is the directory in which the cocos repository is cloned. For example:

# ls
cocos

The docker image must have a cocos directory containing the datasets and results directories. The Agent will run this image inside the SVM and will mount the datasets and results onto the /cocos/datasets and /cocos/results directories inside the image. The docker image must also contain the command that will be run when the docker container is run.

The first step is to create a docker file. Use your favorite editor to create a file named Dockerfile in the current working directory and write in it the following code:

FROM python:3.9-slim

# set the working directory in the container
WORKDIR /cocos
RUN mkdir /cocos/results
RUN mkdir /cocos/datasets 

COPY ./cocos/test/manual/algo/requirements.txt /cocos/requirements.txt
COPY ./cocos/test/manual/algo/lin_reg.py /cocos/lin_reg.py

# install dependencies
RUN pip install -r requirements.txt

# command to be run when the docker container is started
CMD ["python3", "/cocos/lin_reg.py"]

Next, run the build command and then save the docker image as a tar file.

docker build -t linreg .
docker save linreg > linreg.tar

In another window, you can run the following command:

sudo MANAGER_QEMU_SMP_MAXCPUS=4 MANAGER_GRPC_URL=localhost:7001 MANAGER_LOG_LEVEL=debug MANAGER_QEMU_USE_SUDO=false  MANAGER_QEMU_ENABLE_SEV=false MANAGER_QEMU_SEV_CBITPOS=51 MANAGER_QEMU_ENABLE_SEV_SNP=false MANAGER_QEMU_OVMF_CODE_FILE=/usr/share/edk2/x64/OVMF_CODE.fd MANAGER_QEMU_OVMF_VARS_FILE=/usr/share/edk2/x64/OVMF_VARS.fd go run main.go

This command is run from the manager main directory. This will start the manager. Make sure you have already built the qemu image.

In another window, specify what kind of algorithm you want the Agent to run (docker):

./cocos/build/cocos-cli algo ./linreg.tar ./cocos/private.pem -a docker

make sure you have built the cocos-cli. This will upload the docker image.

Next we need to upload the dataset

./cocos/build/cocos-cli data ./cocos/test/manual/data/iris.csv ./cocos/private.pem

After some time when the results are ready, you can run the following command to get the results:

./cocos/build/cocos-cli results ./cocos/private.pem

This will return the results of the algorithm.

To make inference on the results, you can use the following command:

python3 ./cocos/test/manual/algo/lin_reg.py predict result.zip ./cocos/test/manual/data

Wasm Example

More information on how to run wasm files can be found here.

Binary Example

More information on how to run binary files can be found here.