


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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