# JupyterLab Workspace Deploys a Instance with Python and JupyterLab pre-installed. Accessible through a browser. Useful for data science, ML experimentation, or quick Python development. #### Architecture: * 1 Instance (StellarSurge — 4 vCPU, 16 GB RAM) * 1 private network (10.200.0.0/24) * 1 floating IP (public access) * 60 GB boot volume * Security group allows SSH, port 8888 #### What is automated: * Instance creation with selected flavor and image * Private network, subnet, and router * Floating IP assigned to Instance * Security group with ports 22, 8888 * Python 3 venv created under dedicated `jupyter` user * Installed: JupyterLab, NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Transformers, PyTorch (CPU) * JupyterLab configured with random password * Runs as systemd service (auto-restart on reboot) * Credentials saved to `/root/.jupyter_credentials` #### Before you launch: Update these parameters in the template or at launch time: | Parameter | Default | What to change | | ---------------- | ---------------------- | ------------------------------------------------------------------------------ | | `key_name` | `stack-test` | **Required.** Replace with your SSH keypair name from **Compute - Key Pairs**. | | `image` | `Ubuntu 22.04 Updated` | Change only if you need a different OS image. | | `flavor` | `StellarSurge` | **Minimum StellarSurge (4 vCPU, 16 GB).** Do not use smaller flavors. | | `volume_size` | `60` | Increase if you plan to work with large datasets (in GB). | | `public_network` | `Public` | Do not change unless your cloud has a different external network name. | {% hint style="info" %} **key\_name is the only parameter you must change before launching.** Everything else works with defaults. {% endhint %} #### JupyterLab Template Save as `jupyterlab.yaml` and upload via Orchestration or Past Direct. ``` heat_template_version: 2021-04-16 description: > NeevCloud: JupyterLab AI Workspace with Python data science stack. Flavor minimum: StellarSurge (4 vCPU, 16 GB RAM). JupyterLab accessible on port 8888. parameters: image: type: string default: Ubuntu 22.04 Updated flavor: type: string default: StellarSurge volume_size: type: number description: Boot volume size in GB default: 60 public_network: type: string default: Public key_name: type: string default: stack-test resources: net: type: OS::Neutron::Net properties: name: jupyter-net subnet: type: OS::Neutron::Subnet properties: network: { get_resource: net } cidr: 10.200.0.0/24 dns_nameservers: [8.8.8.8, 8.8.4.4] router: type: OS::Neutron::Router properties: external_gateway_info: network: { get_param: public_network } router_interface: type: OS::Neutron::RouterInterface properties: router: { get_resource: router } subnet: { get_resource: subnet } sg: type: OS::Neutron::SecurityGroup properties: name: jupyter-sg rules: - { direction: ingress, protocol: tcp, port_range_min: 22, port_range_max: 22, remote_ip_prefix: 0.0.0.0/0 } - { direction: ingress, protocol: tcp, port_range_min: 8888, port_range_max: 8888, remote_ip_prefix: 0.0.0.0/0 } - { direction: ingress, protocol: icmp, remote_ip_prefix: 0.0.0.0/0 } server: type: OS::Nova::Server properties: name: jupyterlab-server flavor: { get_param: flavor } key_name: { get_param: key_name } networks: - network: { get_resource: net } security_groups: - { get_resource: sg } block_device_mapping_v2: - boot_index: 0 delete_on_termination: true image: { get_param: image } volume_size: { get_param: volume_size } user_data_format: RAW user_data: | #!/bin/bash set -e export DEBIAN_FRONTEND=noninteractive JUPYTER_PASS=$(openssl rand -hex 12) apt-get update -y apt-get install -y python3 python3-pip python3-venv # Create dedicated user and venv useradd -m -s /bin/bash jupyter su - jupyter -c "python3 -m venv /home/jupyter/venv" su - jupyter -c "/home/jupyter/venv/bin/pip install --upgrade pip" su - jupyter -c "/home/jupyter/venv/bin/pip install jupyterlab numpy pandas scikit-learn matplotlib seaborn transformers torch --extra-index-url https://download.pytorch.org/whl/cpu" # Generate hashed password HASH=$(su - jupyter -c "/home/jupyter/venv/bin/python3 -c \"from jupyter_server.auth import passwd; print(passwd('${JUPYTER_PASS}'))\"") # JupyterLab config mkdir -p /home/jupyter/.jupyter cat > /home/jupyter/.jupyter/jupyter_lab_config.py < /etc/systemd/system/jupyterlab.service < /root/.jupyter_credentials <:8888 EOF chmod 600 /root/.jupyter_credentials fip: type: OS::Neutron::FloatingIP properties: floating_network: { get_param: public_network } fip_assoc: type: OS::Neutron::FloatingIPAssociation properties: floatingip_id: { get_resource: fip } port_id: { get_attr: [server, addresses, { get_resource: net }, 0, port] } outputs: jupyterlab_url: description: JupyterLab URL value: str_replace: template: http://HOST:8888 params: HOST: { get_attr: [fip, floating_ip_address] } floating_ip: description: Public IP value: { get_attr: [fip, floating_ip_address] } credentials_note: description: JupyterLab password location value: "SSH into server and run: cat /root/.jupyter_credentials" ``` #### Access: * JupyterLab: * SSH: ssh ubuntu\@floating-ip * Password: `sudo cat /root/.jupyter_credentials` #### Deploy: 1. Go to [Orchestration](/neevcloud-products/orchestration-infrastructure-as-code/web-server-stack-template.md) 2. Upload jupyterlab.yaml 3. Select keypair, flavor, image 4. Launch stack 5. Open `http://floating-ip:8888` and enter the password from credentials file {% hint style="info" %} **Note:** First boot takes 5–8 minutes for pip packages to install. If port 8888 is not responding, wait and retry. {% endhint %} *** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.neevcloud.com/neevcloud-guide/stack-automation-library/development-and-tools/jupyterlab-workspace.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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