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Running the Compute Node

Running a Dria Compute Node is pretty straightforward! You can either follow the guide here for all platforms, or follow a much-more user-friendly guide at https://dria.co/guide for MacOS in particular.

Requirements

Software

Depending the AI models of your choice, you may have to install software:

  • OpenAI models: you don't have to install anything, we just need an OPENAI_API_KEY!

  • Ollama models: you have to install Ollama, see download instructions here. After installing, confirm you have it with:

    ollama --version

Hardware

To learn about hardware specifications such as required CPU and RAM, please refer to node specifications.

In general, if you are using Ollama you will need the memory to run large models locally, which depend on the model's size that you are willing to. If you are in a memory-constrained environment, you can opt to use OpenAI models instead.

Note

The compute node is a lightweight process, but you may see increased memory & CPU usage during the initial testing phases, due to various protocol-level operations with the growing network size.

Setup

To be able to run a node, we need to make a few simple preparations. Follow the steps below one by one.

1. Download Launcher

We have a cross-platform node launcher to easily set up the environment and running the compute node. We will install that first.

Download the appropriate ZIP file for your system using the commands below or from your browser. Make sure to replace the URL with the correct version for your operating system and architecture.

macOS:

  1. Check your architecture:

    uname -m
    • If the output is arm64, download the arm64 version.
    • If it's x86_64, download the amd64 version.

  2. Download the ZIP file:

    # for arm64, use arm64
curl -L -o dkn-compute-node.zip https://github.com/firstbatchxyz/dkn-compute-launcher/releases/latest/download/dkn-compute-launcher-macOS-arm64.zip
    # for x86_64, use amd64
curl -L -o dkn-compute-node.zip https://github.com/firstbatchxyz/dkn-compute-launcher/releases/latest/download/dkn-compute-launcher-macOS-amd64.zip

  3. Unzip the downloaded file:

    unzip dkn-compute-node.zip
cd dkn-compute-node

Tip

Some devices need you to bypass macOS's security warning. If you see "macOS cannot verify that this app is free from malware," when running the node use the following command:

xattr -d com.apple.quarantine dkn-compute-launcher

Linux:

  1. Check your architecture:

    uname -m
    • If it's aarch64, download the arm64 version.
    • If the output is x86_64, download the amd64 version.

  2. Download the ZIP file:

    # for aarch64, use arm64
curl -L -o dkn-compute-node.zip https://github.com/firstbatchxyz/dkn-compute-launcher/releases/latest/download/dkn-compute-launcher-linux-arm64.zip
    # for x86_64, use amd64
curl -L -o dkn-compute-node.zip https://github.com/firstbatchxyz/dkn-compute-launcher/releases/latest/download/dkn-compute-launcher-linux-amd64.zip

  3. Unzip the downloaded file:

    unzip dkn-compute-node.zip
cd dkn-compute-node

Windows:

  1. Check your architecture:

    • Open System Information:
      • Press ⊞ Win + R to open the Run dialog.
      • Type msinfo32 and press Enter.
    • Look for the line labeled "Processor" or "CPU":
      • If it includes "x64" or refers to Intel or AMD, it is likely x86 (amd64).
      • If it mentions ARM, then it's an ARM processor.

  2. Download the ZIP file using a web browser or in PowerShell:

    # for x64, use amd64
Invoke-WebRequest -Uri "https://github.com/firstbatchxyz/dkn-compute-launcher/releases/latest/download/dkn-compute-launcher-windows-amd64.zip" -OutFile "dkn-compute-node.zip"
    # for ARM, use arm64
Invoke-WebRequest -Uri "https://github.com/firstbatchxyz/dkn-compute-launcher/releases/latest/download/dkn-compute-launcher-windows-arm64.zip" -OutFile "dkn-compute-node.zip"

  3. Unzip the downloaded file using File Explorer or in PowerShell:

    Expand-Archive -Path "dkn-compute-node.zip" -DestinationPath "dkn-compute-node"
cd dkn-compute-node

2. Prepare Environment Variables

With our launcher, setting up the environment variables happen on the fly! The CLI application will ask you to enter the required environment variables if you don't have them.

This way, you won't have to manually do the copying and creating environment variables yourself, and instead let the CLI do it for you. You can move directly on to the Usage section.

If you would like to do this part manually, you can continue reading this section.

Create .env File

Dria Compute Node makes use of several environment variables. Let's create an .env file from the given example first.

cp .env.example .env

We will fill out the missing parts witin .env file in a moment.

Note

DKN_ADMIN_PUBLIC_KEY is used to verify that the tasks are given by certain nodes, so that your node does not work for tasks given to the network by untrusted people. You don't need to change this, simply copy and paste it to your .env.

Tip

While adding anything to your .env, you can do it without leaving the terminal. For example, suppose you want to set VALUE to some KEY, you can do it as:

echo "KEY=VALUE" >> .env

If you would like to view the .env without leaving the terminal, you can do:

cat .env

3. Prepare Ethereum Wallet

Dria makes use of the same Ethereum wallet, that is the recipient of your hard-earned rewards! Place your private key at DKN_WALLET_SECRET_KEY in .env without the 0x prefix. It should look something like:

DKN_WALLET_SECRET_KEY=ac0974bec39a17e36ba4a6b4d238ff944bacb478cbed5efcae784d7bf4f2ff80

Caution

Always make sure your private key is within the .gitignore'd .env file, nowhere else! To be even safer, you can use a throw-away wallet, you can always transfer your claimed rewards to a main wallet afterwards.

4. Setup LLM Provider

For the final step, we need to make sure we can serve LLM requests.

For OpenAI

If you will be using OpenAI to serve its models, you need to have an API key in the environment. Simply set the key within your .env:

OPENAI_API_KEY=<YOUR_KEY>

For Ollama

First you have to install Ollama, if you haven't already! The compute node is set to download any missing model automatically at the start by default. This is enabled via the OLLAMA_AUTO_PULL=true in .env.

If you would like to disable this feature, set OLLAMA_AUTO_PULL=false and then continue reading this section, otherwise you can skip to optional services.

First, you must first pull a small embedding model that is used internally.

ollama pull hellord/mxbai-embed-large-v1:f16

For the models that you choose (see list of models just below here) you can download them with same command. Note that if your model size is large, pulling them may take a while. For example:

# example
ollama pull llama3.1:latest

Optional Services

Based on presence of API keys, Ollama Workflows may use more superior services instead of free alternatives, e.g. Serper instead of DuckDuckGo or Jina without rate-limit instead of with rate-limit. Add these within your .env as:

SERPER_API_KEY=<key-here>
JINA_API_KEY=<key-here>

Usage

With all setup steps above completed, we are ready to start a node! Either double-click the downloaded launcher dkn-compute-launcher app (dkn-compute-launcher.exe on Windows), or run it from the terminal from your file explorer, or use it from terminal (or cmd/powershell in Windows).

See the available commands with:

# macos or linux
./dkn-compute-launcher --help
# windows
.\dkn-compute-launcher.exe --help

Then simply run the cli app, it will ask you to enter required inputs:

# macos or linux
./dkn-compute-launcher
# windows
.\dkn-compute-launcher.exe

You will see logs of the compute node on the same terminal!

You can stop the node as usual by pressing Control + C, or kill it from the terminal.

Choosing Models

You will be asked to provide your choice of models within the CLI. You can also pass them from the command line using -m flags:

# macos or linux
./dkn-compute-launcher -m=llama3.1:latest -m=gpt-4o-mini
# windows
.\dkn-compute-launcher.exe -m=llama3.1:latest -m=gpt-4o-mini

Available models can be seen within the launcher, or under ollama-workflows/models.

Additional Static Nodes

You can add additional relay nodes & bootstrap nodes from environment, using the DKN_RELAY_NODES and DKN_BOOTSTRAP_NODES variables respectively. Simply write the Multiaddr string of the static nodes as comma-separated values, and the compute node will pick them up at the start.

# dummy example
DKN_BOOTSTRAP_NODES=/ip4/44.206.245.139/tcp/4001/p2p/16Uiu2HAm4q3LZU2TeeejKK4fff6KZdddq8Kcccyae4bbbF7uqaaa