Module 5 · PyTorch Deep Learning

Module 5: GPU Acceleration & CUDA

Move models to GPU using CUDA and Apple Silicon MPS device engines. Understand CPU and GPU data pipes optimization.

⏱ 15 Min Read • Author: GenAIWallah Team • Updated: May 2026

Day 9

CPU vs. GPU Compute Accelerations

Why this matters

CPU vs GPU: GPUs accelerate dense matrix math; training large models on CPU is often impractical.

CPUs excel at branching logic; GPUs excel at parallel matrix operations — the core of deep learning.

import torch
print(torch.cuda.is_available())
print(torch.cuda.device_count())

CPU training	GPU training
Small models, debugging	Large models, big batches
No CUDA setup	Needs NVIDIA driver + PyTorch CUDA build

Common mistakes

Forgetting optimizer.zero_grad() so gradients accumulate across batches.
Tensor shape mismatches (especially batch/channel dimensions for CNNs).
Training on GPU but leaving tensors on CPU (or vice versa).

Interview checkpoints

Q: Explain cpu vs gpu in PyTorch. A: One-sentence definition + shape/device note.
Q: Common bug? A: Gradients, shapes, or device mismatch.

Practice

Basic: Define CPU vs GPU and sketch a minimal code snippet.
Intermediate: Run a notebook cell demonstrating CPU vs GPU.
Advanced: Intentionally break CPU vs GPU and interpret the error.

Recap

You can explain cpu vs gpu clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: CUDA Devices

Day 10

Moving Models and Tensors to Devices

Why this matters

CUDA Devices: Explicit device management (.to('cuda')) prevents silent CPU/GPU tensor mismatches.

Move models and batches to the active device explicitly.

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)

for x, y in loader:
    x, y = x.to(device), y.to(device)
    # forward / backward on GPU

Check torch.cuda.is_available() and log torch.cuda.get_device_name(0) at job start.

Common mistakes

Forgetting optimizer.zero_grad() so gradients accumulate across batches.
Tensor shape mismatches (especially batch/channel dimensions for CNNs).
Training on GPU but leaving tensors on CPU (or vice versa).

Interview checkpoints

Q: Explain cuda devices in PyTorch. A: One-sentence definition + shape/device note.
Q: Common bug? A: Gradients, shapes, or device mismatch.

Practice

Basic: Define CUDA Devices and sketch a minimal code snippet.
Intermediate: Run a notebook cell demonstrating CUDA Devices.
Advanced: Intentionally break CUDA Devices and interpret the error.

Recap

You can explain cuda devices clearly.
You know one mistake to avoid.
You see how this connects to the next lesson.

Next: Optimizers

← Module 4: DataLoader Module 6: Optimizations →