PyTorch Installation & VSCode setup

PyTorch is a popular open-source machine learning library developed by Facebook’s AI Research lab. It is widely used for various applications such as computer vision, natural language processing, and deep learning. In this tutorial, we will go through the process of installing PyTorch and setting up a development environment in Visual Studio Code (VSCode).

Installing PyTorch

Before we can start using PyTorch, we need to install it on our system. PyTorch can be installed using pip, a package management system for Python. You can install PyTorch by running the following command in your terminal:

pip install torch torchvision

Once PyTorch is installed, you can verify the installation by importing the library in a Python script and checking for any errors.

Setting up VSCode for PyTorch

Visual Studio Code is a lightweight and powerful code editor that is highly extensible and customizable. It provides a great environment for developing and debugging PyTorch applications. To set up VSCode for PyTorch development, follow these steps:

1. Install the Python extension for VSCode, which provides support for Python in the editor.
2. Create a new Python environment for your project using a tool like virtualenv or conda.
3. Install the necessary Python packages for PyTorch development using the command pip install numpy matplotlib.
4. Create a new Python file in VSCode and start writing your PyTorch code.

Writing Your First PyTorch Program

Now that you have PyTorch installed and your development environment set up in VSCode, you can start writing your first PyTorch program. Here is a simple example of how to create a tensor in PyTorch:


import torch

# Create a 2x3 tensor of zeros
x = torch.zeros(2, 3)
print(x)


Save this code in a Python file and run it in VSCode. You should see the tensor printed in the output window.

Conclusion

In this tutorial, we covered the process of installing PyTorch and setting up a development environment in VSCode. With these steps completed, you are now ready to start building and experimenting with machine learning models using PyTorch. Good luck!