Jupyter Notebook is a browser-based interface that allows users to interact directly with code and data through a user-friendly web UI. It is commonly used in AI tasks such as data exploration, feature extraction, model building, and experimentation. 

This guide provides a quick walkthrough for deploying GPU Notebooks on FPT AI Factory—from infrastructure setup to accessing and running AI notebooks for tasks like data analysis, feature engineering, model training, and inference. 

I. Service Requirements

To deploy a GPU Notebook on FPT AI Factory, users need to: 

• Register an account at https://id.fptcloud.com  

• Contact the sales team to subscribe to the FPT AI Factory – AI Infrastructure service. 

Once registered, the technical team will provision the necessary resources for service access. 

II. Setting Up and Accessing the GPU Notebook

The environment setup involves two virtual machines within the same VPC: 

• Jump Server: acts as an SSH gateway for external access. 

• GPU VM: the main virtual machine for running the notebook and handling AI workloads. 

Step 1: Create GPU VM 

Create a GPU VM with H100 configuration using the recommended template (16 CPUs, 192 GB RAM, 80 GB GPU RAM).
Reference: https://fptcloud.com/en/documents/gpu-virtual-machine-en/?doc=quick-start 

Network configuration: assign a public IP, open notebook ports, and configure access permissions via Security Group. 

Step 2: Environment Setup 

Update the system and install the GPU driver: 

sudo apt update && sudo apt upgrade -y 

sudo apt install -y nvidia-driver-565 

nvidia-smi  # kiểm tra trạng thái GPU 

Install Docker following the official guide:
https://docs.docker.com/engine/install/ubuntu/ 

Install NVIDIA Container Toolkit:
https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html 

Step 3: Launch Jupyter Notebook Container 

image="quay.io/jupyter/tensorflow-notebook:cuda-python-3.11" 

docker run -p 8888:8888 \ 

  -v ~/work:/home/jovyan/work \ 

  --detach \ 

  --name notebook \ 

  --gpus all \ 

$image 

Step 4: Retrieve Access Token 

docker ps            # lấy container ID 

docker logs -f <ID>  # tìm token trong log 

Step 5: Access Notebook via SSH Tunnel 

Open a browser and go to http://localhost:13888 using the token retrieved in Step 4. 

ssh -L 13888:127.0.0.1:8888 -J <user_jump>@<jump_ip> <user_vm>@<vm_ip> 

III. Running Basic Notebooks 

After successfully accessing Jupyter Notebook, users can run notebooks to validate the setup: 

1. Check GPU with TensorFlow

import tensorflow as tf 

tf.config.list_physical_devices() 

2. Directly test GPU driver 

• Run mnist-example notebook

3. Try Stable Diffusion (Optional)

https://github.com/nebuly-ai/learning-hub/blob/main/notebooks/notebooks/stable-diffusion.ipynb 

Conclusion 

This guide outlines a step-by-step process for deploying a GPU Notebook environment on FPT Smart Cloud’s AI Factory infrastructure. It enables users to easily spin up virtual machines, configure the environment, and run basic AI models such as TensorFlow or GPU-based inference. 

The deployment model using a Jump Server ensures secure external access while offering flexibility for scaling and experimenting with more advanced AI workloads. This platform is ideal for research teams, product development, or enterprises aiming to rapidly prototype and test AI models without upfront hardware investment.