This content originally appeared on DEV Community and was authored by Joy Akinyi
Introduction
Anaconda is an open-source distribution platform that bundles Python, the conda package manager, and essential libraries like NumPy, pandas, and scikit-learn. It streamlines environment management and ensures consistency across team projects. By deploying Anaconda with JupyterLab on an Azure Virtual Machine (VM) running Ubuntu, teams can create a cloud-based, collaborative workspace.
This guide walks you through setting up an Azure VM with Ubuntu, installing Anaconda, configuring JupyterLab for team access on port 8888, and testing the setup with a sample project.
Prerequisites
- An Azure subscription (sign up for a free account at azure.microsoft.com/free) or better still, a student account
- Familiarity with Linux terminal commands.
- Sudo privileges on the VM.
Step 1: Set Up an Azure VM with Ubuntu
- Log in to the Azure portal at portal.azure.com. or use a student account to log in.
- Navigate to Virtual machines under Services and select Create > Virtual machine.
-
In the Basics tab:
- Choose your subscription.
- Create a new resource group (e.g.,
myResourceGroup). - Name the VM (e.g.,
myVM). - Select a region close to your team for low latency.
- Choose Ubuntu Server 22.04 LTS - Gen2 as the Image.
-
Under Administrator account:
- Select SSH public key or Password for authentication.
- Set a username (e.g.,
azureuser). - Provide an SSH key or password as needed.
-
In Inbound port rules, allow SSH (22) and Custom TCP (8888) for JupyterLab access.
- To allow port 8888:
- After VM creation, go to the VM’s Networking tab in the Azure portal.
- Click Add inbound port rule.
- Set Service to Custom, Port ranges to
8888, Protocol to TCP, and Action to Allow. - For security, restrict Source to your team’s IP ranges.
- To allow port 8888:
Review and create the VM, saving the SSH key if generated.
Note the public IP address from the VM’s overview page.
Connect to the VM via SSH: ssh azureuser@<public-ip> (add -i path/to/key.pem for key-based authentication).
Step 2: Install Anaconda on the Ubuntu VM
- Update the system:
sudo apt update && sudo apt upgrade -y
- Install required utilities:
sudo apt install wget curl git -y
- Download the latest Anaconda installer:
wget https://repo.anaconda.com/archive/Anaconda3-2025.06-1-Linux-x86_64.sh
- Verify the installer (optional):
sha256sum Anaconda3-2025.06-1-Linux-x86_64.sh
Compare the checksum with the official value from Anaconda’s website.
- Run the installer:
#Default is ~/anaconda3, but you can change it to /opt/anaconda3 for system-wide use.
bash Anaconda3-2025.06-1-Linux-x86_64.sh
Accept the license and install in a shared location like /opt/anaconda3 for team access.
- Set permissions:
#ensures the 'users' group has control of Anaconda’s directory.
sudo chown -R :users /opt/anaconda3
#allows all users in the 'users' group to install/update packages without sudo
sudo chmod -R g+w /opt/anaconda3
- Initialize conda:
#Initialize conda for your shell
/opt/anaconda3/bin/conda init
# Reload your shell configuration file
source ~/.bashrc
- Verify the installation:
conda --version
python --version
- Create a shared conda environment:
conda create --name teamenv python=3.11
conda activate teamenv
Step 3: Configure a Secure JupyterLab Server
JupyterLab is configured on port 8888 with a password to secure access, ensuring only authorized team members can log in.
-
Install JupyterLab:
conda activate teamenv conda install jupyterlab -
Generate a configuration file:
jupyter lab --generate-config -
Set a password to secure the server:
from jupyter_server.auth import passwd passwd()Enter a password (e.g.,
mysecurepassword) and copy thesha1:...hash. This password is critical to prevent unauthorized access tohttp://<public-ip>:8888. Edit
~/.jupyter/jupyter_lab_config.py:
c.ServerApp.ip = '0.0.0.0' # Allow access from any IP
c.ServerApp.port = 8888 # Default port
c.ServerApp.open_browser = False
c.ServerApp.password = 'sha1:<hashed-password>' # Paste the hashed password
c.ServerApp.allow_remote_access = True
c.ServerApp.root_dir = '/opt/shared_notebooks' # Shared directory
- Create a shared notebook directory:
#create a directory
sudo mkdir /opt/shared_notebooks
# give ownership to the users group
sudo chown azureuser:users /opt/shared_notebooks
# give groups write rights in the notebook
sudo chmod g+w /opt/shared_notebooks
Note: If you want all users who run JupyterLab to access this folder, you need to make sure they’re added to the users group.:
sudo usermod -aG users <username>
- Start JupyterLab in the background:
conda activate teamenv
nohup jupyter lab
The nohup command ensures JupyterLab continues running after you exit the SSH session, maintaining access at http://:8888.
Access JupyterLab at http://<public-ip>:8888 and log in with the password. Use HTTPS if configured.
Collaboration Notes:
- Users share the
teamenvenvironment and/opt/shared_notebooks. - Add team members to the
usersgroup (e.g.,sudo adduser teamuser1; sudo usermod -aG users teamuser1) and share the password securely. - Avoid conflicts by using Git or subdirectories in
/opt/shared_notebooks.
Step 4: Test with a Mini Project
Test with a JupyterLab notebook fetching cryptocurrency data from CoinGecko.
Install dependencies:
conda activate teamenv
conda install requests pandas
Create a notebook in /opt/shared_notebooks and add:
import requests
import pandas as pd
from datetime import datetime
url = "https://api.coingecko.com/api/v3/coins/markets"
params = {
"vs_currency": "usd",
"ids": "bitcoin,ethereum,cardano,solana",
"order": "market_cap_desc",
"per_page": 10,
"page": 1,
"sparkline": False
}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
df = pd.DataFrame(data, columns=["id", "symbol", "current_price", "market_cap", "total_volume"])
df["timestamp"] = datetime.now()
display(df)
else:
print("Error fetching data:", response.status_code)
Run and share the notebook via /opt/shared_notebooks or Git.
This content originally appeared on DEV Community and was authored by Joy Akinyi
Joy Akinyi | Sciencx (2025-08-26T14:24:04+00:00) Deploying Anaconda with JupyterLab on an Azure VM for Team Collaboration. Retrieved from https://www.scien.cx/2025/08/26/deploying-anaconda-with-jupyterlab-on-an-azure-vm-for-team-collaboration/
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