Setting Up Python and PyTorch Environment

# CHAPTER 2

Setting Up Python and PyTorch Environment #

1. Introduction #

2. Learning Objectives #

• Install a compatible version of Python.

• Create and manage Python Virtual Environments.

• Navigate the PyTorch installation matrix.

• Install PyTorch using pip with or without GPU support.

• Set up Visual Studio Code (VS Code) and Jupyter Notebooks.

3. Installing Python #

Windows:

1. 1. Go to python.org/downloads and download the Windows installer.

1. 2. CRITICAL STEP: On the very first screen of the installer, check the box "Add Python to PATH". If you skip this, nothing will work in your terminal.

macOS:

1. 1. Open terminal and install Homebrew: /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

1. 2. Run: brew install python

Linux (Ubuntu): sudo apt update sudo apt install python3 python3-pip python3-venv

4. Virtual Environments #

1. 1. Open your terminal/command prompt.

1. 2. Create a folder for this course: mkdir pytorchcourse and enter it: cd pytorchcourse

1. 3. Create the virtual environment (named ptenv):

• Windows: python -m venv ptenv

• Mac/Linux: python3 -m venv ptenv

• Windows: ptenv\Scripts\activate

• Mac/Linux: source ptenv/bin/activate

5. Installing PyTorch #

Go to the official website: pytorch.org/get-started/locally/ You will see an installation matrix. Select:

• PyTorch Build: Stable

• Your OS: Windows / Mac / Linux

• Package: Pip

• Language: Python

• Compute Platform:

• If you have an NVIDIA GPU, select CUDA 11.8 or CUDA 12.1 (This downloads the GPU version).

• If you have a Mac, or a standard laptop with no NVIDIA graphics card, select CPU.

The website will generate a command for you. It will look something like this:

# Example for CPU only
pip install torch torchvision torchaudio

6. VS Code and Jupyter Notebook Setup #

1. 1. Download Visual Studio Code from code.visualstudio.com.

1. 2. Open VS Code, go to the Extensions tab (squares on the left menu).

1. 3. Search for and install the Python extension and the Jupyter extension.

1. 4. Open your pytorchcourse folder in VS Code.

1. 5. Create a new file named hellopytorch.ipynb.

1. 6. Open the file, click "Select Kernel" in the top right, and choose your pt_env environment.

7. Step-by-Step Implementation: Verify Installation #

import torch

print(f"PyTorch Version: {torch.__version__}")

# Check if CUDA (NVIDIA GPU) is available
if torch.cuda.is_available():
    print("SUCCESS: NVIDIA GPU detected! Training will be fast.")
# Check for Apple Silicon GPU (M1/M2/M3 Mac)
elif torch.backends.mps.is_available():
    print("SUCCESS: Apple Metal GPU detected!")
else:
    print("NOTICE: No GPU detected. PyTorch will run on the CPU.")

# Do some basic math
tensor = torch.tensor([10, 20, 30])
print(f"Tensor multiplied by 2: {tensor * 2}")

8. CUDA and GPU Basics (Advanced) #

• CPU: Has a few very powerful cores (4 to 16). Great for sequential tasks.

• GPU (Graphics Card): Has thousands of weaker cores. Great for doing thousands of simple math problems simultaneously.

9. Common Mistakes #

• Installing the wrong Compute Platform: If you have an NVIDIA GPU but accidentally select the "CPU" version on the PyTorch website, PyTorch will install successfully, but torch.cuda.isavailable() will return False. You must uninstall it and install the CUDA version.

• Forgetting to activate the environment: If you open a new terminal tomorrow and type import torch, it will fail unless you run ptenv\Scripts\activate first.

10. Best Practices #

• Google Colab: If you have a terrible laptop and cannot install PyTorch, or you don't have a GPU, use Google Colab (colab.research.google.com). It is a free Jupyter Notebook in the cloud that comes with PyTorch pre-installed and gives you free access to an NVIDIA GPU!

11. Exercises #

1. 1. Follow the matrix on the PyTorch website and install PyTorch into your virtual environment.

1. 2. Run the verification script to check your version and GPU status.

12. MCQ Quiz with Answers #

13. Interview Questions #

• Q: Explain why a GPU trains neural networks significantly faster than a high-end CPU.

• Q: What is the purpose of the PyTorch installation matrix on their official website?

14. FAQs #

15. Summary #

16. Next Chapter Recommendation #