Label Studio on Ubuntu 24.04 on Azure User Guide

Overview

Label Studio is the popular open source data labeling and annotation platform for machine learning and AI. It lets teams label and annotate text, images, audio, video and time series data, manage annotation projects and quality, and export ready-to-use training datasets. The cloudimg image installs Label Studio 1.23 into a Python virtual environment at /opt/label-studio on Python 3.12, run by an unprivileged labelstudio service account, fronts it with an nginx reverse proxy on TCP 80, persists all data on a dedicated Azure data disk, and generates a unique password on the first boot of every VM. Backed by 24/7 cloudimg support.

What is included:

• Label Studio 1.23 in a Python venv at /opt/label-studio (Python 3.12)
• nginx reverse proxy on :80 with HTTP Basic Authentication in front of the Label Studio server (bound on :8080); the /health probe stays open
• A dedicated Azure data disk at /var/lib/label-studio holding the SQLite database, media uploads, annotations and exports — separate from the OS disk and re-provisioned with every VM
• Per-VM password generated at first boot, in a root-only file, securing both the HTTP gate and the Label Studio administrator account (admin@cloudimg.local)
• label-studio.service + nginx.service as systemd units, enabled and active
• 24/7 cloudimg support

Prerequisites

An active Azure subscription, an SSH key pair, and a VNet + subnet in the target region. Standard_B2ms (2 vCPU / 8 GiB RAM) is a good starting point; scale up for larger projects or more concurrent annotators. NSG inbound: allow 22/tcp from your management network and 80/tcp from the users who will label data (front port 80 with TLS for public exposure — see Enabling HTTPS).

Step 1 — Deploy from the Azure Marketplace

Sign in to the Azure Portal, choose Create a resource, search the Marketplace for Label Studio by cloudimg, and select Create. On Basics pick your subscription, resource group, region and size; under Administrator account choose SSH public key and paste your key; under Inbound port rules allow SSH (22) and HTTP (80). Review the dedicated data disk on the Disks tab, then Review + create → Create.

Step 2 — Deploy from the Azure CLI

az vm create \
  --resource-group <your-rg> \
  --name label-studio \
  --image <marketplace-image-urn> \
  --size Standard_B2ms \
  --admin-username azureuser \
  --ssh-key-values ~/.ssh/id_ed25519.pub \
  --vnet-name <your-vnet> --subnet <your-subnet> \
  --public-ip-sku Standard

az vm open-port --resource-group <your-rg> --name label-studio --port 80 --priority 1010

Step 3 — Connect to your VM

ssh azureuser@<vm-public-ip>

Step 4 — Confirm the services are running

systemctl is-active label-studio.service nginx.service
curl -s http://127.0.0.1/health

Both services report active and the health endpoint returns {"status": "UP"}.

Step 5 — Retrieve your password

The password is generated uniquely on the first boot of your VM and written to a root-only file:

sudo cat /root/label-studio-credentials.txt

The LABEL_STUDIO_PASSWORD value is the password. The same password secures the HTTP gate (user admin) and the Label Studio administrator account (admin@cloudimg.local).

Step 6 — Sign in

Browse to http://<vm-public-ip>/. The browser first prompts for the HTTP gate — enter user admin and the password from Step 5. Then sign in to Label Studio with the email admin@cloudimg.local and the same password.

Step 7 — Create a labeling project

1. Choose Create Project, name it, and import data (drag and drop files, paste URLs, or connect cloud storage).
2. In Labeling Setup, pick a template — text classification, named entity recognition, image bounding boxes, audio transcription, and many more — or build a custom config.
3. Start labeling. Annotations are saved as you go and can be exported in JSON, CSV, COCO, YOLO and other formats from the Export menu.

Connect a machine learning backend (an external model) under Settings > Model to pre-label data and accelerate annotation.

Step 8 — Confirm the runtime

/opt/label-studio/venv/bin/pip show label-studio | grep ^Version

Expected: Version: 1.23.0. Projects, annotations, uploads and the database persist on the dedicated data disk at /var/lib/label-studio.

Production scale — PostgreSQL

The image defaults to SQLite and local storage on the data disk. For team scale, set PostgreSQL in /etc/label-studio/label-studio.env (see the Label Studio docs for DJANGO_DB / POSTGRE_* and cloud storage settings), then restart:

sudo systemctl restart label-studio.service

Enabling HTTPS

For production, terminate TLS at nginx with a real domain pointed at the VM's public IP. Install certbot and request a certificate (replace the domain):

sudo apt-get update && sudo apt-get install -y certbot python3-certbot-nginx
sudo certbot --nginx -d your-domain.example.com

certbot edits the nginx site at /etc/nginx/sites-available/cloudimg-label-studio to add the TLS listener and arranges automatic renewal.

Backup and maintenance

All Label Studio state — the SQLite database, uploads, annotations and exports — lives on the dedicated data disk at /var/lib/label-studio. Snapshot that disk in Azure to back up your projects and annotations, and keep the OS patched with sudo apt update && sudo apt upgrade. The HTTP gate password is in /etc/nginx/.label-studio.htpasswd; rotate it with sudo htpasswd /etc/nginx/.label-studio.htpasswd admin. Restart with sudo systemctl restart label-studio.service; logs: sudo journalctl -u label-studio.service.

Support

This image is backed by 24/7 cloudimg support. Contact us by email and chat for help with labeling configuration, machine learning backends, database and storage configuration, TLS termination and scaling.

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