What is Lilypad?

Lilypad serves as a verifiable, trustless, and decentralized computational network engineered to facilitate mainstream adoption of web3 applications. By extending unrestricted, global access to computational power, Lilypad strategically collaborates with decentralized infrastructure networks, such as Filecoin, to formulate a transparent, efficient, and accessible computational ecosystem. While Lilypad does not specifically resolve issues related to the accessibility of AI models, it significantly alleviates challenges associated with procuring high-performance AI hardware. In this context, Lilypad provides decentralized AI computational services. The network recently unveiled its second version, dubbed Lilypad V2 (Aurora), and is actively laying groundwork for multi-chain integration and the deployment of an incentivized test net.

Objective and problem statement

Lilypad aims to mitigate the challenges predominantly associated with the accessibility of high-performance computational hardware. At present, numerous barriers impede developers and organizations from seamlessly integrating projects that require high-performance computing, such as AI technologies, into their applications. Unlike conventional centralized systems, where access to powerful compute hardware is restricted and costly, Lilypad endeavors to democratize this access. Through its verifiable, trustless, and decentralized computational network, Lilypad extends unrestricted, global access to computational power. By leveraging decentralized infrastructure networks such as Filecoin, Lilypad is strategically positioned to enhance the efficiency, transparency, and accessibility of high-performance computing hardware.

Applications

Perform off-chain decentralised compute over data, with on-chain guarantees, and to call this functionality directly from a smart contract, CLI and an easy to use abstraction layer, opens the door to a multitude of possible applications including:

• Inference AI jobs

• ML training jobs

• Invoking & supporting generic ZK computations

• Cross-chain interoperability complement to bridge protocols

• Utilising inbuilt storage on IPFS

• Federated Learning consensus (with Bacalhau insulated jobs)

• IOT & Sensor Data integrations

• Providing a platform for Digital twins

• Supply chain tracking & analysis

• ETL & data preparation jobs

Key features

Some of the key features of Lilypad include:

1. Verifiable Trustless Decentralized Compute Network: Lilypad is a decentralized compute network that aims to provide global, permissionless access to compute power. It leverages decentralized physical infrastructure networks like Filecoin to ensure trustlessness and verifiability.

2. Mainstream Web3 Application Support: Lilypad is designed to enable mainstream web3 applications to use its compute network. It aims to make decentralized AI more accessible, efficient, and transparent for developers and users.

3. Open Compute Network: Lilypad creates an open compute network that allows users to access and run AI models and jobs. It separates module creators from users and curates a set of deterministic modules for users to run, ensuring determinism in verification systems.

4. Multichain Support: Lilypad plans to go multichain, which means it will support multiple blockchain networks. This will increase the scalability and interoperability of the network, allowing users to choose the blockchain that best suits their needs.

5. Incentivized Test Net: Lilypad has plans to launch an incentivized test net, which will provide users with incentives to participate in testing and improving the network. This will help identify and address any issues or challenges before the mainnet launch.

6. Decentralization of Mediators: The team also aims to decentralize the mediators in the network. This means that the decision-making process and governance of the network will be distributed among multiple participants, ensuring a more decentralized and resilient system.

Presentation

In the video above, Ally Haire, Co-Founder of Lilypad, will provide an introduction to the project and its goals.

The cloud is just somebody else's computer...

Overview

This guide will take you through

• Setting up Metamask Wallet for the Lilypad v2 Aurora testnet

• Funding your wallet with Lilypad Testnet tokens from the faucet.

Funding your wallet

To obtain funds, connect to the lilypad v2 Aurora testnet network on your wallet and head to the faucet at http://faucet.lilypad.tech to get ETH and LP

Copy your metamask wallet address into the bar and click request.

Install Requirements

Install CLI

1. With GO toolchain

go install github.com/bacalhau-project/lilypad@latest

You may then need to set:

NO export SERVICE_SOLVER="0xd4646ef9f7336b06841db3019b617ceadf435316"
export SERVICE_MEDIATORS="0x2d83ced7562e406151bd49c749654429907543b4"
export WEB3_PRIVATE_KEY=<your private key>

2. Via officially released binaries

# Detect your machine's architecture and set it as $OSARCH
OSARCH=$(uname -m | awk '{if ($0 ~ /arm64|aarch64/) print "arm64"; else if ($0 ~ /x86_64|amd64/) print "amd64"; else print "unsupported_arch"}') && export OSARCH;
# Detect your operating system and set it as $OSNAME
OSNAME=$(uname -s | awk '{if ($1 == "Darwin") print "darwin"; else if ($1 == "Linux") print "linux"; else print "unsupported_os"}') && export OSNAME;
# Download the latest production build
curl -sSL -o lilypad https://github.com/bacalhau-project/lilypad/releases/download/v2.0.0-701b8cb/lilypad-$OSNAME-$OSARCH
# Make Lilypad executable and install it
chmod +x lilypad
sudo mv lilypad /usr/local/bin/lilypad

You may then need to set:

export SERVICE_SOLVER="0xd4646ef9f7336b06841db3019b617ceadf435316"
export SERVICE_MEDIATORS="0x2d83ced7562e406151bd49c749654429907543b4"
export WEB3_PRIVATE_KEY=<your private key>

Verifying if the install is successful. Execute lilypad on your terminal and it should produce the following response.

Lilypad

Usage:
  lilypad [command]

Available Commands:
  completion        Generate the autocompletion script for the specified shell
  help              Help about any command
  mediator          Start the lilypad mediator service.
  resource-provider Start the lilypad resource-provider service.
  run               Run a job on the Lilypad network.
  solver            Start the lilypad solver service.

Flags:
  -h, --help   help for lilypad

Use "lilypad [command] --help" for more information about a command.

Setting up Metamask

• Install metamask Extension here

• Next, add the Lilypad Testnet chain to metamask.

To do this, open metamask then click on the network button at the top left of the popup (in the menu bar):

``

Then click the "Add Network" Button.

Next, click on "Add a network manually" at the bottom of the page and enter the Lilypad Testnet details:

Note: Make sure to you have Lilypad CLI installed.

Note: Make sure you have SERVICE_SOLVER, SERVICE_MEDIATORS and WEB3_PRIVATE_KEY env variables in your environment

Run Cowsay

1. Run the command below

lilypad run cowsay:v0.0.1 -i Message="moo"

1. Wait for the compute to take place and for the results to be published

1. Viewing your results

cat /tmp/lilypad/data/downloaded-files/Qma2Ds9uGmtDd3GkerqqKLJe9TjcZC4yxuGRUaFBsQi7yr/stdout

This page is a dynamic work in progress! We're working on some better diagrams!

Lilypad Technical Architecture

Based on Academic Research

The architecture of Lilypad is inspired by the research paper titled "Mechanisms for Outsourcing Computation via a Decentralized Market." The paper introduces MODiCuM, a decentralized system that allows for computational outsourcing in an open market. Just like MODiCuM, Lilypad aims to create an open market of computational resources by introducing various decentralized services like solver, resource provider, job creator, mediator, and directory services. MODiCuM's unique approach to deterring misbehavior in a decentralized environment through dedicated mediators and enforceable fines has influenced Lilypad's own design, particularly in the areas of dispute resolution and system integrity.

Abstract of the paper: Mechanisms for Outsourcing Computation via a Decentralized Market As the number of personal computing and IoT devices grows rapidly, so does the amount of computational power that is available at the edge. Since many of these devices are often idle, there is a vast amount of computational power that is currently untapped, and which could be used for outsourcing computation. Existing solutions for harnessing this power, such as volunteer computing (e.g., BOINC), are centralized platforms in which a single organization or company can control participation and pricing. By contrast, an open market of computational resources, where resource owners and resource users trade directly with each other, could lead to greater participation and more competitive pricing. To provide an open market, we introduce MODiCuM, a decentralized system for outsourcing computation. MODiCuM deters participants from misbehaving-which is a key problem in decentralized systems-by resolving disputes via dedicated mediators and by imposing enforceable fines. However, unlike other decentralized outsourcing solutions, MODiCuM minimizes computational overhead since it does not require global trust in mediation results. We provide analytical results proving that MODiCuM can deter misbehavior, and we evaluate the overhead of MODiCuM using experimental results based on an implementation of our platform.

Developer Experience and Determinism: A Talk by Luke Marsden

In this insightful presentation, Luke Marsden, a core member of the Lilypad project, delves into the complex relationship between determinism and developer experience. He highlights the necessity for determinism in verification systems and discusses the challenges of implementing it during runtime. The talk also covers the project's strategy to separate module creators from end-users and to provide a curated list of deterministic modules. Key points in the roadmap like improving user experience, decentralizing mediators, and automating determinism checks are also touched upon. The presentation concludes with a live demo of running AI jobs on Lilypad V2.