# How to Install and Use Jupyter Notebook on a Atlantic Cloud GPU Server > URL: https://www.atlantic.net/gpu-server-hosting/how-to-install-and-use-jupyter-notebook-on-a-atlantic-cloud-gpu-server/ | Published: 2024-12-10 | Updated: 2024-12-12 | Author: Hitesh Jethva Jupyter Notebook is an open-source, interactive computing environment that supports data visualization, analysis, and machine learning workflows. When paired with a GPU-enabled Atlantic.Net server, it allows for accelerated computations, making it an excellent choice for tasks like deep learning and large-scale data processing. This guide will walk you through setting up and using Jupyter Notebook on a GPU-enabled Atlantic.Net server. ## Prerequisites - An Ubuntu 22.04 GPU Server - CUDA Toolkit and cuDNN Installed - A root or sudo privileges ## Step 1: Update the Server To ensure the latest updates and security patches are included, run: ```bash apt update -y apt upgrade -y ``` This updates the package repository and upgrades outdated packages. ## Step 2: Install Python and Required Packages Jupyter Notebook is built on Python, and its ecosystem relies on Python libraries for everything from data visualization to machine learning. Install Python and pip. ```bash apt install python3 python3-pip -y ``` Check the versions of Python and pip to confirm the installation: ```bash python3 --version pip3 --version ``` Install libraries required for machine learning and data analysis: ```bash pip install tensorflow torch matplotlib pandas numpy ``` These packages include popular frameworks like TensorFlow and PyTorch, along with essential libraries for data manipulation and visualization. ## Step 3: Install and Configure Jupyter Notebook Kernel A kernel is the engine that executes the code in your notebooks. Configuring a kernel ensures it has access to the installed libraries and GPU resources. Install the ipykernel package. ```bash pip install ipykernel ``` Create a custom kernel for Jupyter. ```bash python3 -m ipykernel install --user --name=gpu_env --display-name "Python (GPU)" ``` Output. ```bash Installed kernelspec gpu_env in /root/.local/share/jupyter/kernels/gpu_env ``` This kernel links your Python environment to Jupyter Notebook, ensuring it recognizes the GPU-enabled libraries. ## Step 4: Install Jupyter Notebook Install Jupyter Notebook using pip. ```bash pip install notebook ``` Verify the installation by checking its version. ```bash jupyter notebook --version ``` Output. ```bash 7.2.2 ``` Generate a configuration file to enable remote access. ```bash jupyter notebook --generate-config ``` Edit the generated configuration file to enable access over the network. ```bash nano ~/.jupyter/jupyter_notebook_config.py ``` Add the following lines: ```bash c.ServerApp.ip = '0.0.0.0' c.ServerApp.open_browser = False c.ServerApp.port = 8888 ``` ## Step 5: Test Jupyter Notebook for GPU Support Before launching Jupyter Notebook, verify that your GPU is accessible: Open a Python shell. ```bash python3 ``` Check PyTorch GPU availability. ```bash >>>import torch >>>print("PyTorch GPU Support:", torch.cuda.is_available()) ``` Output. ```bash PyTorch GPU Support: True ``` Test TensorFlow GPU support. ```bash >>>import tensorflow as tf >>>print("TensorFlow GPU Devices:", tf.config.list_physical_devices('GPU')) ``` Output. ```bash TensorFlow GPU Devices: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] ``` These tests confirm that your GPU is ready for computation. ## Step 6: Launch Jupyter Notebook Start Jupyter Notebook on your server with the following command. ```bash jupyter notebook --no-browser --port=8888 --ip=your-server-ip --allow-root ``` Replace **your-server-ip** with the server’s public IP address. The output will display a URL to access the notebook: ```bash To access the server, open this file in a browser: file:///root/.local/share/jupyter/runtime/jpserver-8084-open.html Or copy and paste one of these URLs: http://45.77.182.191:8888/tree?token=8e99e04461348b61490e1a51559a8be313840260787e7e17 http://127.0.0.1:8888/tree?token=8e99e04461348b61490e1a51559a8be313840260787e7e17 ``` ## Step 7: Run a PyTorch Model in Jupyter Notebook Open your web browser and access the Jupyter Notebook using the URL **http://your-server-ip:8888/tree?token=8e99e04461348b61490e1a51559a8be313840260787e7e17.** ![](https://www.atlantic.net/wp-content/uploads/2024/11/p1-3.png) Click on **File** > **New** > **Notebook.** You will be asked to select the Kernel. ![](https://www.atlantic.net/wp-content/uploads/2024/11/p2-2.png) Select the **“Python (GPU)”** kernel and click **Select** to create a new notebook. Add the following code to train a simple PyTorch model. ```bash import torch import torch.nn as nn import torch.optim as optim # Check GPU availability device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print(f"Using device: {device}") # Simple linear model model = nn.Linear(10, 1).to(device) print(model) # Sample data inputs = torch.randn(100, 10).to(device) targets = torch.randn(100, 1).to(device) # Loss and optimizer criterion = nn.MSELoss() optimizer = optim.SGD(model.parameters(), lr=0.01) # Training loop for epoch in range(5): optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, targets) loss.backward() optimizer.step() print(f"Epoch {epoch+1}, Loss: {loss.item()}") ``` ![](https://www.atlantic.net/wp-content/uploads/2024/11/p3.png) Now, click on the **run** symbol to run your Notebook. This will print the loss for each epoch, confirming the GPU is being used if device is cuda. ![](https://www.atlantic.net/wp-content/uploads/2024/11/p4.png) ## Step 8: Monitor GPU Usage For the above examples, you can use the **nvidia-smi** command in the server’s terminal to monitor GPU utilization: ```bash nvidia-smi ``` If the models are correctly using the GPU, you will see GPU memory and utilization metrics updating during training. ```bash Mon Nov 25 08:18:16 2024 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 550.90.07 Driver Version: 550.90.07 CUDA Version: 12.4 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA A16-2Q On | 00000000:06:00.0 Off | 0 | | N/A N/A P0 N/A / N/A | 1398MiB / 2048MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 8257 C /usr/bin/python3 1397MiB | +-----------------------------------------------------------------------------------------+ ``` ## Conclusion By following this guide, you’ve set up a powerful Jupyter Notebook environment on an [**Atlantic.Net GPU Server**](https://www.atlantic.net/gpu-server-hosting/). This setup empowers you to perform high-performance data analysis and machine learning tasks with ease. Make sure to explore additional Jupyter features like extensions and plugins to further enhance your productivity.