Skip to main content

Getting Started: Your First PyTorch Script

Hello there! Welcome to the world of PyTorch, a leading deep learning framework that is both powerful and flexible. Let's get our hands dirty by walking through your first PyTorch script.

What is PyTorch?​

PyTorch is an open-source machine learning library based on the Torch library. It's primarily developed by Facebook's artificial intelligence research group and is noted for its simplicity, easiness to learn, and dynamic computational graph.

Setting Up PyTorch​

Before we start, make sure that PyTorch is installed in your environment. If you haven't done so, you can install it using pip:

pip install torch torchvision

Or with Anaconda:

conda install pytorch torchvision -c pytorch

Your First PyTorch Script​

Now, let's start with a simple script. We'll create two tensors and perform a simple arithmetic operation (addition) on them.

# Import PyTorch
import torch

# Initialize two tensors
tensor1 = torch.tensor([1.0, 2.0])
tensor2 = torch.tensor([3.0, 4.0])

# Add the tensors
result = tensor1 + tensor2

# Print the result
print(result)

When you run this script, you should see the following output:

tensor([4., 6.])

Understanding The Script​

Let's break down the script to understand each part.

  1. Import PyTorch: The first line of the script imports the PyTorch library.

    import torch

  2. Initialize Tensors: Tensors in PyTorch are similar to NumPy arrays, with the addition of strong support for GPU computation. We create two tensors using the torch.tensor function, each containing two floating-point numbers.

    tensor1 = torch.tensor([1.0, 2.0])
    tensor2 = torch.tensor([3.0, 4.0])

  3. Tensor Operation: We add the two tensors together using the + operator. PyTorch overloads standard Python operators, allowing us to use them on tensors.

    result = tensor1 + tensor2

  4. Print the Result: Finally, we print the result. The print function shows the result of our tensor addition.

    print(result)

And that's it! You've just written and understood your first PyTorch script.

Next Steps​

You have now taken your first steps into the world of PyTorch! As you continue to explore, you'll encounter many more complex and exciting functionalities of this powerful library such as GPU computing, automatic differentiation, and deep neural networks. Remember, the key to mastering PyTorch (like anything else) is consistent practice and exploration.

Stay curious, and happy coding!

Note: This tutorial provides a basic introduction to PyTorch and doesn't cover many of the more advanced features. As you become more comfortable with PyTorch, you can explore these advanced features to fully leverage the power of this library.