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.

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.

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.

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.

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:

Getting LilyPad Tokens

To acquire LilyPad tokens, follow these steps:

1. Visit the Lilypad Faucet at

2. Enter your wallet address.

3. Receive some LP tokens.

Setting up Environment Variables

Set the environment variables as shown below:

Running cowsay

Note: Ensure that you have Lilypad installed before proceeding.

To execute cowsay, use the following command:

Output:

See the Results

1. by navigating to the local folder

Here, you can view the stdout and stderr as well as the outputs folder for the run

Overview

Generically, stable diffusion is what happens when you put a couple of drops of dye into a bucket of water. Given time, the dye randomly disperses and eventually settles into a uniform distribution which colours all the water evenly

In computer science, you define rules for your (dye) particles to follow and the medium this takes place in.

Stable Diffusion is a machine learning model used for text-to-image processing (like Dall-E) and based on a diffusion probabilistic model that uses a transformer to generate images from text. There are several open-source stable diffusion models out there (made famous by Stability.ai) and they continue to improve and become even more fully featured - SDXL0.9 is one of the more recently open-sourced models.

[CLI] Running Stable Diffusion SDXL 0.9

To run stable diffusion use the sdxl module like so:

The output will look like this:

Take the ipfs link given in the results and paste it into your browser:

You could also check the output folder that would have been downloaded at the end of running the job

In the /outputs folder, you'll find the image:

Since modules are deterministic, running this command with the same text prompt will produce the same image, since the same seed is also used (the default seed is 0).

To change the image, you can pass in a different seed number:

CLI Video

In this video, Sam demos the use of SDXL in the Lilypad network.

Looking for Smart Contracts?

Try this to start!

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

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

1. Viewing your results

Run Hello, 🐮 World! Job [CLI]

1. Start Docker

2. Open a Terminal window and run the following command

3. Make sure to have the following env in your shell session

1. Run lilypad

(ensure your user is in the docker group if necessary on your platform) Output:

See the Results

1. by navigating to the local folder

Here, you can view the stdout and stderr as well as the outputs folder for the run

Run Hello,🐮 World! Job [Smart Contract]

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.

Getting LilyPad Tokens

To acquire LilyPad tokens, follow these steps:

1. Visit the Lilypad Faucet at here

2. Enter your wallet address.

3. Receive some LP tokens.

Setting up Environment Variables

Set the environment variables as shown below:

export WEB3_CHAIN_ID=80001
export WEB3_RPC_URL=https://polygon-mumbai.infura.io/v3/[YOUR_INFURA_API_KEY]
export WEB3_CONTROLLER_ADDRESS=0xB08492E6Ae019609E6bF3a19873d00D2257f614b
export WEB3_TOKEN_ADDRESS=0xBe72246911F254E3D221C965A50d89C6D660DCc0
export WEB3_MEDIATION_ADDRESS=0x16f1Bb6784D3Fbb735423A3603BA44427c4aDe4F
export WEB3_JOBCREATOR_ADDRESS=0xD5723D6758E93d2E612210127f6Ab9705E606Ac8
export WEB3_PAYMENTS_ADDRESS=0x01B18F94B61253ba63b810ddA371eA54bbACbdC6
export WEB3_STORAGE_ADDRESS=0xC5a58D6BDbdB66c50ecD795C5456E1f6ADc52dD9
export WEB3_USERS_ADDRESS=0x11F3f6e51B822c4f0FF8955510f81B6654a9BD0C
export SERVICE_SOLVER="0x08D118d3300c82CD94a4080805426AB025fe9852"
export SERVICE_MEDIATORS="0xd6244f8c08d4b7bb7ccbd72e585b19ee68a8d1eb"

Note: You will need to set up your own Infura account and get your infura Api Key. You can follow the instructions here

Running cowsay

Note: Ensure that you have Lilypad installed before proceeding.

To execute cowsay, use the following command:

lilypad run cowsay:v0.0.1 -i Message="<hello lilypad>"

Output:

See the Results

1. see the results by navigating to the IPFS CID Navigate to the IPFS CID result output in the Results -> https://ipfs.io/ipfs/QmNjJUyFZpSg7HC9akujZ6KHWvJbCEytre3NRSMHzCA6NR

1. by navigating to the local folder

open /tmp/lilypad/data/downloaded-files/Qmay5vZ7u5jvn2VkAGMLkkYXGQQd5GFZFuHkbDEFYb5VeF

Here, you can view the stdout and stderr as well as the outputs folder for the run

Overview

This module takes a voice sample as an input and can then output another voice sample for a given text prompt - in multiple languages.

OSS Model

The OSS Model is based on this model: https://github.com/coqui-ai/TTS

Lilypad Module

Run in CLI

First upload a voice .wav file to IPFS

Here's are two voice recording examples:

https://bafybeibrum5bqhslpoemqiyvikbg3b5ujte6qppvk2cbdstwxnhejypxl4.ipfs.w3s.link/ally-voice.wav

https://bafybeif3qp2sd7qb67kjhds65b6qr5ecerib76cjclyo625ovledffabfu.ipfs.w3s.link/

Then use the command below to run the module. Note the parameters for changing the language and adding a prompt.

lilypad run github.com/dub3-space/lily-synthetizoor:b4d2e078c3447e1df12588ee854360087ce7f081 -i URL=https://bafybeibrum5bqhslpoemqiyvikbg3b5ujte6qppvk2cbdstwxnhejypxl4.ipfs.w3s.link/ally-voice.wav -i SENTENCE='Hello! Youre listening to voice translation on Lilypad with dub3!' -i LANGUAGE=en -i INPUT_FOLDER=/inputs -i OUTPUT_FOLDER=/outputs

You should see Lilypad run something like this:

You'll find your results under the output folder:

CLI Example Command

lilypad run mistral-7b-instruct:v0.1-lilypad3 -i PromptEnv="PROMPT=Name 10 renaissance artists"

Output Example in stout

1. Leonardo da Vinci

2. Michelangelo

3. Raphael

4. Titian

5. Albrecht Dürer

6. Sandro Botticelli

7. Jan van Eyck

8. Leon Battista Alberti

9. Johannes Gutenberg

10. Andrea Mantegna

Github Module