Setting Up Your Keras Environment

Now that we have a conceptual understanding of deep learning and the role Keras plays, let's get our hands dirty and prepare the necessary software environment. Having a consistent and correctly configured environment is essential for running the examples and exercises in this course smoothly. We'll focus on installing Keras 3 and its preferred backend, PyTorch.

The Importance of Isolated Environments

Before installing any packages, it's highly recommended to create an isolated Python environment for this project. This prevents conflicts between package dependencies required by different projects. Tools like Python's built-in venv module or the conda package manager are excellent choices for this purpose.

Using venv:

1. Navigate to your project directory in your terminal.
2. Create a virtual environment (e.g., named .venv):

   python -m venv .venv
   

3. Activate the environment:
   • On macOS/Linux: source .venv/bin/activate
   • On Windows (Command Prompt): .venv\Scripts\activate.bat
   • On Windows (PowerShell): .venv\Scripts\Activate.ps1

Your terminal prompt should now indicate that you are inside the virtual environment.

Using conda:

1. Create a new environment (e.g., named keras-env) with a specific Python version:

   conda create --name keras-env python=3.9 # Or your preferred Python 3 version
   

2. Activate the environment:

   conda activate keras-env
   

All subsequent package installations will be confined to this active environment.

Installing Keras and PyTorch

With your virtual environment activated, you can now install the necessary libraries using pip, the Python package installer. Keras 3 is designed to work with multiple backends (PyTorch, TensorFlow, JAX). For this course, we will primarily use the PyTorch backend.

Install Keras and PyTorch with the following command:

pip install keras torch torchvision torchaudio

This command installs:

• keras: The core Keras library (version 3 or later).
• torch: The PyTorch library, which will serve as the computational backend.
• torchvision, torchaudio: PyTorch companion libraries often useful for data loading and transformations, especially in vision and audio tasks.

Keras 3 is typically configured to automatically detect and use an available backend like PyTorch. If you have multiple backends installed (e.g., TensorFlow) and want to ensure PyTorch is used, you can set the KERAS_BACKEND environment variable before running your Python scripts or notebooks:

• On macOS/Linux:

  export KERAS_BACKEND="torch"
  python your_script.py
  

• On Windows (Command Prompt):

  set KERAS_BACKEND=torch
  python your_script.py
  

• On Windows (PowerShell):

  $env:KERAS_BACKEND="torch"
  python your_script.py
  

You can also configure this within your Python code before importing Keras, although setting the environment variable is often preferred:

import os
os.environ["KERAS_BACKEND"] = "torch"
import keras
# Now Keras will use the PyTorch backend

Note on GPU Acceleration (Optional)

Deep learning often involves computationally intensive operations that can be significantly accelerated using Graphics Processing Units (GPUs), particularly NVIDIA GPUs. If you have a compatible NVIDIA GPU and want to leverage it:

1. Install NVIDIA Drivers: Ensure you have the latest drivers for your specific GPU installed from the NVIDIA website.
2. Install CUDA Toolkit and cuDNN: PyTorch requires specific versions of the CUDA Toolkit and the cuDNN library. It's often easiest to install a PyTorch version that bundles these. Visit the PyTorch website and use their configuration tool to get the correct pip or conda command for your system and desired CUDA version. The command might look slightly different from the basic one above, specifying CUDA versions.
3. Verify Installation: After installation, PyTorch should automatically detect and use the GPU if the setup is correct. You can verify this within Python (we'll cover checks shortly).

While GPU acceleration dramatically speeds up training for large models and datasets, it's not strictly required for following the fundamental concepts in this course. Most examples will run reasonably well on a modern CPU.

With Keras and its PyTorch backend installed, your environment is now ready. In the next section, we'll perform a quick verification step to confirm that Keras is installed correctly and can communicate with the backend.