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Nillion Python Starter

This is a python starter repo for building on the Nillion Network. Complete environment setup, then run the examples:

  • To run multi party examples, go to the multi party compute folder.

  • To run single party examples, go to the single party compute folder.

  • To run permissions examples (storing and retrieving permissioned secrets, revoking permissions, etc.), go to the permissions folder.

Prerequisites: Install the CLI Dependencies

The nillion-devnet tool spins up anvil under the hood, so you need to have foundry installed. The bootstrap-local-environment.sh file uses pidof and grep.

Environment Setup

  1. Create a .env file by copying the sample:

    cp .env.sample .env
  2. Create the virtual environment (.venv), install dependencies, and activate the virtual environment

    bash ./create_venv.sh && source .venv/bin/activate

    Run the bootstrap-local-environment.sh script to spin up nillion-devnet, generate keys, and get bootnodes, cluster, and payment info:

    ./bootstrap-local-environment.sh
  3. Check .env file - keys, bootnodes, cluster, and payment info should now be present. If you want to run against a local cluster, use this configuration. Otherwise, replace values with testnet bootnodes, cluster, and payment info.

  4. Look through the programs folder to see examples of Nada programs.

Compiling Programs

Nada programs need to be compiled ahead of being stored. Compile all programs in the programs folder with the script compile_programs.sh:

bash compile_programs.sh

This generates a programs-compiled folder containing the compiled programs.

Store a Compiled Program

Store a compiled program in the network with this script:

bash store_program.sh {RELATIVE_COMPILED_PROGRAM_PATH}

To store the compiled addition_simple program you can run:

bash store_program.sh programs-compiled/addition_simple.nada.bin

Storing a program results in the stored program_id, the network's reference to the program. The program_id is the {user_id}/{program_name}.

Testing

Most examples and tutorials within this repository can be tested. Docker is required to run the tests.

cd testing
bash run_tests.sh

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  • Python 90.3%
  • Shell 9.5%
  • Dockerfile 0.2%