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Run FLock node on Akash network
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Run FLock node on Akash network
Last updated
NOTE: This post was originally published on the Akash Blog.
Developing truly open and decentralised AI is one of society’s most critical challenges. The networks, platforms, and clients we build will help guide this development, from open compute networks like Akash to platforms like FLock that make it easy to train and fine-tune AI models. Integrating these projects simplifies the user experience, enabling greater access that puts AI development directly in the hands of people around the world.
This case study outlines the integration between Akash and FLock that enables users to easily train AI models on decentralised compute. It illustrates how Akash, the first open-source Supercloud, gives AI developers access to the high-performance compute resources needed to train AI models with FLock.io, a platform for decentralised machine learning.
As the adage goes, “birds of a feather flock together.” As the decentralized AI movement takes flight, Akash and FLock.io are flying in unison. Akash is an open network that lets users buy and sell computing resources securely and efficiently. FLock.io is an open network that lets users train, validate, and govern AI models democratically and transparently.
Akash is purpose-built for public utility. Compute is available to anyone on the network through Akash’s peer-to-peer marketplace, and the network does not limit the types of tasks or workloads that can be deployed. Similarly, FLock.io is built for and by the community, and the platform does not restrict where models are trained, validated, hosted, and/or deployed.
With a clear alignment on visions for the future and shared values of openness, permissionlessness, and composability, it’s clear to see why Akash and FLock.io are closely aligned.
As you'll see below, FLock has integrated with Akash to create 1-click templates that make it easy to run FLock.io nodes on the Akash Supercloud.
Video Tutorial:
Follow the steps 1 and 2 mentioned in our validator guide to stake Flock token (FML ) for a validation task and generate an API.
You will need a Keplr or Leap wallet to create an Akash account.
Go to https://console.akash.network/
Click “CONNECT WALLET” to connect a Keplr or Leap wallet.
When connected to your Keplr wallet, Akash console would show the USDC and AKT balance in your wallet. Deployment on Akash will consume your USDC or AKT.
You can buy some AKT from any CEX or DEX below, then you need to transfer your AKT to your Keplr or Leap wallet.
Choose Flock validator template from templates page https://console.akash.network/templates
(Docker image link: https://hub.docker.com/repository/docker/z471899214308/llm-loss-validator)
Click deploy
Input your Flock API and task ID you have staked FML on.
Then change the storage to a minimum of 50Gi in builder page, click create deployment.
You can also use the YML file to create deployment. Add your Flock API Key and the Task ID
Choose a provider who is audited, click accept bid.
Then the Flock validator will run automatically in 10 min.
This section is if you wish to run your custom deployment
Click the “DEPLOY” in the upper left corner, and select “Rent GPUs” to rent a GPU
The GPU on the Akash platform is provided to users through docker containers. So you need to provide the image information of the docker container to be deployed, and the hardware configuration of the container.
Choose the GPU count, vCPU count, RAM size.
You can use flockvalidator/pytorch:2.0.1-py3.10-cuda11.8.0-devel-ubuntu22.04 as your docker image, use 8vCPU, 24GB Memory and 60G storage.
After the container configuration is selected, you will enter the provider selection page. You can see all providers that meet the configuration requirements, and their prices and availability are marked. Select one and proceed.
After deployment, you can enter the "SHELL" tab and use the sh command to configure the environment and run the validator.
For advanced users, you can configure sshd service and login via third-party tools such as mobaxterm, xshell.
