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Introduction

IO Cloud enables the deployment and management of on-demand, decentralized GPU clusters, giving users access to powerful GPU resources without significant hardware investments or the complexity of managing infrastructure. IO Cloud democratizes GPU access by leveraging a decentralized model, providing machine learning engineers and developers with the same seamless experience as traditional cloud providers. The platform utilizes a distributed network of nodes, IO workers, to offer flexible, scalable compute resources. IO Cloud clusters are self-healing, fully meshed GPU systems that ensure high availability and fault tolerance. With IO Cloud, you can tap into a decentralized network of GPUs and CPUs capable of running Python-based machine learning workloads. It is ideal for AI projects requiring distributed compute.

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IO Clouds Integration Flow

The diagram below provides a high-level overview of how users integrate with IO Cloud - from choosing a compute resource to launching AI workloads. It illustrates the end-to-end flow across key components, including cluster selection, container deployment, and runtime execution.

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Create Account

To create an account, go to cloud.io.net using Google, Apple ID, GitHub, or simply with a one-time password by clicking the “Login with Email” button. Newlogin1 Jp

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Payments

IO Cloud compute is paid with IO Credits. You can top up your credits in two convenient ways:
  • Solana: This cryptocurrency option enables fast, secure transactions. You can configure a Solana wallet either during account creation or through your Account Settings. Once configured, you can fund your wallet and top up your credits with $IO Coin or USDC.
  • Credit Cards: We accept all major credit cards, providing a straightforward payment solution.
To learn more about payment options, visit our IO Cloud Payments page.

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App Guide

The home page offers the following options:

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Clusters

io.net offers three distinct cluster options to power your AI projects:

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VM on Demand

Deploy fully equipped virtual machines optimized for AI and machine learning workflows in a few simple clicks.
  • Processor & Location Choice: Select from available GPUs and locations that meet your needs.
To view detailed instructions, see Deploy VM on Demand Cluster.

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Container

Allows you to configure and deploy containers on powerful GPU-backed infrastructure through a simple, guided interface.
  • Step-by-Step Wizard: Set image, command, ports, environment variables, and more.
  • Cluster & Location Choice: Select from available GPUs and regions that meet your needs.
  • Fast Setup: Built for AI and compute-heavy workloads with rapid setup.
To view detailed instructions, see Deploy Containers.

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Managed Services

Covers the cluster types that io.net provisions and configures for you, rather than deploying them yourself from IO Cloud.
  • Available Cluster Types: Bare Metal, Kubernetes, Ray, and Confidential Compute.
  • Provisioned for You: Our specialists size the cluster, set it up to your specifications, and hand it over ready to use.
  • Request Access: Email our sales team with the cluster type you need, your preferred GPU type, and basic project details.
To view detailed instructions, see Managed Services.

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Clusters Tabs

The Cluster tabs provide a central hub for managing your deployed clusters. Each tab displays a list of clusters, categorized by type, with details such as:
  • Name: The unique identifier for the cluster.
  • Accelerator (GPU): The type of GPU used in the cluster.
  • Status: The current state of the cluster (e.g., running, stopped, pending).
  • Remaining Compute Hours: The remaining time on your cluster’s billing cycle.
From here, you can perform actions like:
  • Rename: Change the name of a cluster.
  • Extend: Increase the duration of your cluster’s billing cycle.
  • Terminate: Stop and delete a cluster.
Each cluster tab also provides quick access to essential management tools:
  • Visual Studio: An integrated code editing and debugging development environment.
  • Jupyter Notebook: An interactive environment for data analysis and visualization.
  • Ray Dash: A dashboard for monitoring and managing distributed applications.
For a detailed explanation of monitoring and managing your clusters, see Monitor and Manage Clusters.