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Table of Contents
1. Install Python and pip
2. Create a virtual environment (optional)
3. Install PyTorch
Install the CPU version:
Install the GPU version (NVIDIA GPU and CUDA are required):
4. Install other dependencies
5. Write your PyTorch application
6. Run your application
7. Deploy to production environment (optional)
Create a web application using Flask or Django
Containerization with Docker
Home Operation and Maintenance Linux Operation and Maintenance How to deploy a PyTorch app on Ubuntu

How to deploy a PyTorch app on Ubuntu

May 29, 2025 pm 11:18 PM
python docker python script

Deploying a PyTorch application on Ubuntu can be done by following the following steps:

1. Install Python and pip

First, make sure that Python and pip are already installed on your system. You can install them using the following command:

 sudo apt update
sudo apt install python3 python3-pip

2. Create a virtual environment (optional)

To isolate your project environment, it is recommended to create a virtual environment:

 python3 -m venv myenv
source myenv/bin/activate

3. Install PyTorch

Select the appropriate PyTorch installation command based on your hardware configuration (CPU or GPU). You can find suitable installation commands on the PyTorch official website .

Install the CPU version:

 pip install torch torchvision torchaudio

Install the GPU version (NVIDIA GPU and CUDA are required):

 pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113

Please select the appropriate URL according to your CUDA version. For example, if you are using CUDA 11.3, use the above command.

4. Install other dependencies

Install other necessary Python libraries according to your application requirements:

 pip install numpy pandas matplotlib

5. Write your PyTorch application

Create a new Python file (such as app.py) and write your PyTorch code.

 import torch
import torch.nn as nn
import torch.optim as optim

# Define a simple neural network class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc = nn.Linear(784, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = self.fc(x)
        Return x

# Create a model instance model = SimpleNet()

# Define loss function and optimizer criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Sample data (part of the MNIST dataset)
inputs = torch.randn(64, 1, 28, 28)
labels = torch.randint(0, 10, (64,))

# Forward propagation outputs = model(inputs)
loss = criteria(outputs, labels)

# Backpropagation and optimization optimizer.zero_grad()
loss.backward()
optimizer.step()

print(f'Loss: <span>{loss.item()}'</span> )

6. Run your application

Run your Python script in the terminal:

 python app.py

7. Deploy to production environment (optional)

If you want to deploy your application to a production environment, consider the following methods:

Create a web application using Flask or Django

You can use Flask or Django to create a web application and integrate the PyTorch model into it.

Containerization with Docker

Using Docker can easily package your applications and their dependencies into a container for easy deployment and scaling.

 # Create Dockerfile
FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt

COPY . .

CMD ["python", "app.py"]
# requirements.txt
torch torchvision torchaudio
flask

Build and run the Docker container:

 docker build -t my-pytorch-app .
docker run -p 5000:5000 my-pytorch-app

Through the above steps, you can successfully deploy your PyTorch application on Ubuntu.

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