TensorFlow computations can be executed on different types of hardware processors, primarily Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Understanding the differences and when to use each is important for efficient model development and training. While TensorFlow runs seamlessly on CPUs out of the box after installation, leveraging a GPU can dramatically accelerate training for many machine learning tasks, especially deep learning.

CPUs: The Generalists

Your computer's CPU is a versatile processor designed for a wide range of tasks, excelling at sequential operations and complex logic. TensorFlow can perform all its operations using only the CPU.

How it works: TensorFlow operations, including mathematical computations like matrix multiplication or calculating gradients, are executed by the CPU cores.
Pros:
- Universality: Every computer has a CPU, making it the default execution device. No special hardware or drivers are needed beyond the standard TensorFlow installation.
- Simplicity: Easier setup and less prone to configuration issues compared to GPU setups.
- Suitability for certain tasks: Adequate for smaller models, tasks dominated by complex control flow rather than massive parallel computation, initial prototyping, and running inference on devices without dedicated GPUs.
Cons:
- Performance bottleneck: CPUs typically have a small number of powerful cores (e.g., 4, 8, 16). Deep learning relies heavily on large matrix and tensor operations, which involve many independent calculations that can be performed simultaneously. CPUs are relatively slow at this kind of massively parallel work, leading to very long training times for complex models.

GPUs: The Parallel Powerhouses

GPUs were initially designed for rendering graphics, a task requiring vast numbers of parallel calculations. This architecture makes them exceptionally well suited for the mathematical operations at the heart of deep learning.

How it works: GPUs contain hundreds or thousands of simpler cores compared to CPUs. They are optimized for performing the same operation simultaneously across large amounts of data (Single Instruction, Multiple Data, or SIMD). This parallelism directly maps to the needs of tensor operations like matrix multiplications and convolutions found in neural networks.
Pros:
- Massive Speedup: For typical deep learning workloads, training on a compatible GPU can be 10x to 100x faster (or even more) than training on a CPU alone. This difference becomes significant when working with large datasets and complex models.
- Efficiency for Parallel Tasks: Ideal for the type of numerical computations prevalent in deep learning.
Cons:
- Hardware Requirement: You need a specific type of GPU, typically an NVIDIA GPU that supports CUDA (Compute Unified Device Architecture).
- Software Setup: Requires installing the NVIDIA drivers, the CUDA Toolkit, and the cuDNN library. Ensuring version compatibility between TensorFlow, CUDA, and cuDNN can sometimes be challenging (refer back to the "Setting Up Your Development Environment" section for installation guidance).
- Cost and Power: GPUs can be expensive and consume significantly more power than CPUs.

Let's visualize the potential difference in training time. The following chart shows a hypothetical comparison for training a moderately complex image classification model.

Relative training time for a sample deep learning task. Actual speedup depends heavily on the model, data, and specific hardware.

How TensorFlow Utilizes GPUs

If you have correctly installed TensorFlow with GPU support along with the necessary NVIDIA software (CUDA Toolkit and cuDNN), TensorFlow will typically prioritize using an available GPU for most operations automatically. It handles the allocation of computations to the GPU to accelerate performance.

You don't usually need to explicitly tell TensorFlow to use the GPU for every operation. However, it's good practice to verify that TensorFlow can detect your GPU after installation. You can do this using functions provided by TensorFlow, which we cover in the "Verifying Your Installation" section. TensorFlow also provides mechanisms for finer control over device placement (e.g., running specific operations on the CPU even if a GPU is available), but automatic placement works well for most common scenarios.

Making the Choice

Start with CPU: For learning TensorFlow basics, experimenting with small models, or tasks not involving heavy numerical computation, the CPU is perfectly adequate and simpler to manage.
Move to GPU for Serious Training: If you plan to train deep neural networks, work with large datasets (like images or sequences), or find your CPU training times becoming impractically long, investing time (and potentially money) into a GPU setup is highly recommended.
Consider Cloud Options: If a local GPU isn't feasible due to cost or setup complexity, cloud platforms like Google Colab (often providing free GPU access), Google Cloud AI Platform, AWS SageMaker, or Azure Machine Learning offer powerful GPU (and even TPU) resources on demand.

For this course, while many examples will run reasonably fast on a modern CPU, you will see significant performance benefits in later chapters involving model training if you have a GPU-enabled environment. The setup instructions in the previous section provide guidance for both CPU-only and GPU-accelerated installations.