Windows - GPU Slowly Node

Guide: Installing Ubuntu 22.04 on WSL2

For Windows users, it's essential to set up Ubuntu 22.04 specifically on WSL2.

Warning

Ensure you're installing Ubuntu 22.04 on WSL2. Unfortunately, Ubuntu 20.04 is not compatible with WSL2.

For detailed instructions on how to install WSL and run Ubuntu 22.04, follow the tutorial linked below:

How to Install Ubuntu on WSL2open in new window

Once installed, you can verify your WSL2 Ubuntu version by running the following command:

lsb_release -a

Please confirm that you have installed version 22.04.

# Docker

To ensure a successful setup, follow these steps to install and configure Docker:

1. Install Docker Desktop with the WSL2 backend by visiting this link: Install Docker Desktop with WSL2 backendopen in new window.

2. After installation, make sure Docker is added to its own user group.

# NVIDIA

To fully utilize the GPU on the grid, we will need to install both the NVIDIA drivers and NVIDIA's CUDA Toolkit.

# NVIDIA Driver Installation Guide

To use NVIDIA drivers, download and install the appropriate driver from the official NVIDIA website: NVIDIA Driver Downloadsopen in new window.

To verify if the drivers are correctly installed, open a terminal and run the following command:

nvidia-smi

If the drivers are correctly installed, you should see detailed information about your GPU, similar to the following example output:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.54       Driver Version: 526.56       CUDA Version: 12.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  On   | 00000000:01:00.0 Off |                  N/A |
| N/A   43C    P5     9W /  N/A |      0MiB /  4096MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

These commands will help you generate the necessary configuration file and verify the CDI support.

# Install the NVIDIA Container Toolkit

To install the NVIDIA Container Toolkitopen in new window (nvidia-ctk), run the following commands:

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list \
  && \
    sudo apt-get update

Then we can install the NVIDIA Container Toolkit package:

sudo apt-get install -y nvidia-container-toolkit

# Configure the NVIDIA Container Toolkit

For configuring the NVIDIA Container Toolkit to run Podman v4 natively on WSL2 (as Podman in Docker is not supported on WSL2), please follow the instructions for CDI configuration. You can find these instructions at https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.htmlopen in new window.

Once you have completed the configuration, run the following commands:

sudo nvidia-ctk cdi generate --output=/etc/cdi/nvidia.yaml

nvidia-ctk cdi list

# Podman

The Slowly Node connects to Podman and runs your containers inside of it. On WSL2, you'll need to natively install Podman >v4.1:

echo "deb https://download.opensuse.org/repositories/devel:/kubic:/libcontainers:/unstable/xUbuntu_22.04/ /" | sudo tee /etc/apt/sources.list.d/devel:kubic:libcontainers:unstable.list
curl -fsSL https://download.opensuse.org/repositories/devel:/kubic:/libcontainers:/unstable/xUbuntu_22.04/Release.key | sudo gpg --dearmor | sudo tee /etc/apt/trusted.gpg.d/devel_kubic_libcontainers_unstable.gpg > /dev/null
sudo apt update
sudo apt install podman

Check if you have Podman version 4 installed and if you have GPU support:

podman --version
podman run --rm --device nvidia.com/gpu=all --security-opt=label=disable ubuntu nvidia-smi -L

If this doesn't work, make sure you have the NVIDIA drivers installed and the nvidia-ctk installedopen in new window and configuredopen in new window

If you see Error: container create failed (no logs from conmon)... when running the command, follow the steps here to resolve issue

# Slowly Test Grid Script

With just a single command in your command line, you can easily set up a Slowly Node on your machine. Simply run the following command:

Please note that this script has certain requirements and is specifically designed to run without the need for sudo privileges. It's crucial to exercise caution when running any script from the internet with sudo privileges. Even in this case, it's advisable to thoroughly review the script before executing it on your system.

The script performs a series of tests to verify the successful completion of the previous steps outlined in the guide. Once all the checks pass, your node will start up.

You will see your node's information displayed in the following format. As long as there are no error messages, your node will automatically join the Test Grid market, where it will receive a Test Grid benchmark job. It's important to keep the node running during this process!

Upon successful completion of the Test Grid benchmark job, you will receive a code in the logs. This code is required for your registration. Please follow the steps below:

1. Check the logs for the following message:

   Job Succeeded: https://explorer.slowly.ai/jobs/<JOB_ID>?network=devnet
   Test Grid Registration code: <CODE>

2. Copy the Test Grid registration code: <CODE>.

Congratulations! 🎉 You have completed the technical part of the registration.

Info

To find your Node's Solana key, navigate to ~/.slowly/slowly_key.json. It is essential to back up this file to ensure its safety.

# Advanced (optional)

# Run Podman API

This command can be used to start Podman service on port 8080, so our Slowly Node can reach it. This is also already done by the register.sh script in the final step, so this step is optional:

podman system service --time 0 tcp:0.0.0.0:8080&

To validate Podman's proper functioning, use:

curl http://localhost:8080/v4.5.0/libpod/info

# Launching the Slowly Node with Custom Parameters

You can manually launch the Slowly Node to modify certain parameters:

• Use the --podman parameter to direct to your Podman service if it's running elsewhere.

• Use --volume to map your solana key to /root/.slowly/slowly_key.json within the Docker container if you wish to use your own key.

docker run \
      --pull=always \
      --network host  \
      --interactive \
      --volume ~/.config/solana/id.json:/root/.slowly/slowly_key.json \
      slowly/slowly-node \
         --podman http://$(ip addr show eth0 | grep -oP '(?<=inet\s)\d+(\.\d+){3}'):8080 \
         join-test-grid