GPU DePIN (Decentralized Physical Infrastructure Network.)

Case Study

Client Brief

Based in New York, this company is all about decentralized computing. They’ve got this cloud thing that lets machine learning folks tap into scalable clusters at way lower costs compared to the big centralized players. Their vibe is all about making computing power accessible.

They’re on a mission to bring together a million GPUs in what they call DePIN – a cool move to amp up innovation in machine learning. So, it’s like they’re shaking up the tech scene, making high-powered computing not just for the big shots but for everyone with a vision.

Project Details

  • Automate K8s Cluster Deployment:
    • Develop automation scripts or templates for seamless Kubernetes (K8s) cluster deployment.
    • Utilize Infrastructure as Code (IaC) principles to ensure consistency and efficiency in cluster setup.
  • Deploy Ray:
    • Integrate and deploy the Ray framework on the automated K8s cluster.
    • Configure Ray to enable distributed computing for scalable and parallelized workloads.
  • Deploy Kubeflow:
    • Set up and deploy Kubeflow, an open-source machine learning (ML) platform, on the K8s cluster.
    • Configure Kubeflow components to facilitate end-to-end ML workflows.
  • Deploy Jupyter and VSCode:
    • Implement the deployment of Jupyter and Visual Studio Code (VSCode) as collaborative and interactive development environments on the K8s cluster.
    • Configure the environments to support data science, machine learning, and general-purpose coding.

Tech Stack

Containerisation Technologies: Python, Go/Golang
IaC and CM: Kubernetes, Docker
Programming Language: Terrafrom, Ansible
Tools: Prometheus, Grafana, Opsgenie
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