Deploy MinIO on a virtual machine powered by Azure Cobalt 100
Introduction
Overview of Azure Cobalt 100 and MinIO
Create an Azure Cobalt 100 virtual machine
Open MinIO ports in the Azure Network Security Group
Install and configure MinIO on Azure Cobalt 100
Benchmark MinIO storage performance on Azure Cobalt 100
Use MinIO for AI/ML Dataset and Model Storage
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  30 min
Last updated:  29 Sep 2026
Skill level:
  Introductory
Reading time:
  30 min
Last updated:
  29 Sep 2026
Author:Jason Andrews, Arm
Arm IP:Neoverse
Tags: Databases Microsoft Azure Cobalt Linux MinIO Python boto3
Author:
  • Jason Andrews, Arm
Arm IP:
Neoverse
Tags:
Databases Microsoft Azure Cobalt Linux MinIO Python boto3

Who is this for?

This is an introductory topic for developers, DevOps engineers, and platform engineers who want to deploy MinIO object storage on virtual machines (VMs) powered by Microsoft Azure Cobalt 100.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Provision a VM powered by Azure Cobalt 100 and deploy MinIO.
  • Benchmark MinIO storage throughput for large object transfers.
  • Validate S3 API compatibility using the boto3 Python SDK.
  • Store and retrieve AI and ML datasets, as well as model artifacts, using MinIO.

Prerequisites

Before starting, you will need the following:

  • A Microsoft Azure account with access to Cobalt 100-based instances (Dpsv6)
  • Familiarity with SSH and remote server access
  • Basic understanding of cloud storage concepts

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll deploy and validate a single-node MinIO object store on an Azure Cobalt 100-based VM. First, you’ll create a Dpsv6 instance, open ports 9000 and 9001, and connect through SSH to configure MinIO with local storage. You’ll generate a 1 GB test file and time its upload with the MinIO Client. Finally, you’ll use Python and boto3 to confirm S3 API compatibility.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which ports should I open in Azure for MinIO, and where do I add the rule?
Open TCP ports 9000 for the S3 API and 9001 for the MinIO Console. Add the inbound rules to the network security group attached to your VM’s network interface or subnet.
How do I SSH into the VM after provisioning it?
Run ssh -i <your-key>.pem azureuser@<VM-IP> with the private key that you downloaded during VM creation. Verify the VM’s public IP in the Azure portal before you connect.
How do I verify that MinIO is running and reachable?
Open http://<VM-IP>:9001 to access the MinIO Console. You can also run MinIO Client commands and confirm that the server returns successful responses.
When generating test data and running the upload benchmark, what should I see?
Confirm that dd creates dataset/file1.bin at about 1 GB. Your timed mc cp upload should finish without errors, report its duration, and display the object in the MinIO Console.
How do I know that S3 API validation with boto3 worked?
Confirm that your boto3 operations complete without exceptions and list the expected bucket or objects. Verify that the uploaded objects also appear in the MinIO Console.
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