Python / Python Deep Learning and Neural Networks Interview Questions

How do you use GPUs in PyTorch and what are the key patterns for writing device-agnostic code?

PyTorch's device abstraction allows the same code to run on CPU, single GPU, or multiple GPUs with minimal changes. The fundamental operations are moving tensors to a device with .to(device) or .cuda(), and ensuring model and data tensors always reside on the same device before any computation.

A critical performance concept: CPU–GPU data transfers are expensive (PCIe bandwidth is limited vs. GPU memory bandwidth). Minimise them by loading data onto the GPU once per batch, pre-computing dataset statistics on CPU, and avoiding frequent tensor transfers inside the training loop.

import torch
import torch.nn as nn

# Device-agnostic code pattern
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using: {device}')  # cuda / mps / cpu

# Move model to device
model = nn.Linear(10, 5).to(device)

# Move data to device in the training loop
for X_batch, y_batch in loader:
    X_batch = X_batch.to(device, non_blocking=True)
    y_batch = y_batch.to(device, non_blocking=True)
    y_hat = model(X_batch)
    # ...

# Check which device a tensor is on
t = torch.randn(3)
print(t.device)         # cpu
t_gpu = t.cuda()        # or t.to('cuda:0')
print(t_gpu.device)     # cuda:0

# Apple Silicon
device = torch.device('mps' if torch.backends.mps.is_available() else 'cpu')

# Memory diagnostics
print(torch.cuda.memory_allocated() / 1e9, 'GB allocated')
print(torch.cuda.max_memory_allocated() / 1e9, 'GB peak')
torch.cuda.empty_cache()  # release unused cached GPU memory

# Multi-GPU: DistributedDataParallel (DDP) preferred over DataParallel
model_ddp = nn.parallel.DistributedDataParallel(model, device_ids=[0, 1])

Take quiz

Why is non_blocking=True in tensor.to(device) beneficial during training?It prevents the CPU from running out of memory during transfer

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It allows the CPU-to-GPU transfer to happen asynchronously, overlapping with other CPU work (like preparing the next batch) rather than blocking until the transfer completes

✓ Correct! Well done.

It automatically pins the memory for faster transfer

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It tells PyTorch to skip the transfer if the tensor is already on the correct device

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What is the most important rule to avoid runtime errors when doing GPU computations in PyTorch?Always call torch.cuda.synchronize() before each forward pass

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The model and all input tensors must reside on the same device — operations between tensors on different devices (e.g. CPU and GPU) raise a RuntimeError

✓ Correct! Well done.

GPU tensors must always be created with torch.zeros rather than torch.randn

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The batch size must be a power of 2 for GPU operations

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Python / Python Deep Learning and Neural Networks Interview Questions

How do you use GPUs in PyTorch and what are the key patterns for writing device-agnostic code?

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