Environment:Fastai Fastbook CUDA GPU Environment

Knowledge Sources	fastai/fastbook PyTorch CUDA
Domains	Infrastructure, Deep_Learning
Last Updated	2026-02-09 17:00 GMT

Overview

NVIDIA CUDA GPU environment recommended for accelerating PyTorch model training in all deep learning Fastbook chapters.

Description

While the Fastbook exercises can technically run on CPU, GPU acceleration is strongly recommended for any chapter involving model training (image classification, NLP, collaborative filtering, tabular deep learning). The codebase sets CUDA-specific configurations in `utils.py` including deterministic mode and cuDNN benchmark settings. PyTorch's CUDA backend is used transparently through fastai's device management.

Usage

Use this environment whenever running model training workflows. CPU-only mode is sufficient for data exploration, feature engineering, and random forest chapters. GPU is particularly important for:

Image Classification: CNN training with `cnn_learner` and `fine_tune`
NLP: Language model and text classifier fine-tuning (Ch10, Ch12)
Collaborative Filtering: Embedding-based models (Ch8)
From-Scratch Networks: Manual SGD and backpropagation exercises (Ch4, Ch17)

System Requirements

Category	Requirement	Notes
OS	Linux (recommended), Windows, macOS	macOS lacks CUDA; use CPU or MPS
Hardware	NVIDIA GPU with CUDA support	Minimum 4GB VRAM; 8GB+ recommended
Driver	NVIDIA driver compatible with CUDA toolkit	Match PyTorch CUDA version
CUDA	Compatible with PyTorch >= 1.6	CUDA 10.2+ for PyTorch 1.6; CUDA 11.x for newer PyTorch

Dependencies

System Packages

NVIDIA GPU driver (latest stable)
CUDA toolkit (version matching PyTorch build)
cuDNN (bundled with PyTorch conda install)

Python Packages

`pytorch` >= 1.6 (with CUDA support)
`torchvision` (CUDA-enabled build)

Credentials

No credentials required for GPU usage.

Quick Install

# Conda install with CUDA (recommended)
conda install pytorch torchvision cudatoolkit=11.8 -c pytorch

# Verify CUDA availability
python -c "import torch; print(torch.cuda.is_available())"

Code Evidence

CUDA deterministic settings from `utils.py:14-16`:

set_seed(42)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

These settings ensure reproducible results at the cost of slightly slower training. `cudnn.deterministic = True` forces deterministic algorithms, while `cudnn.benchmark = False` disables the auto-tuner that selects the fastest convolution algorithms for given input sizes.

GPU device references appear throughout the notebooks, e.g., from `05_pet_breeds` output:

# Model tensors are automatically placed on CUDA device
# Output shows: device='cuda:0'

Common Errors

Error Message	Cause	Solution
`torch.cuda.is_available()` returns `False`	No CUDA-capable GPU or wrong PyTorch build	Install CUDA-enabled PyTorch: `conda install pytorch torchvision cudatoolkit -c pytorch`
`CUDA out of memory`	Model or batch size too large for GPU VRAM	Reduce `bs` parameter in DataLoaders or use smaller model architecture
`RuntimeError: CUDA error: device-side assert triggered`	Invalid tensor values (e.g., label index out of range)	Check that labels and vocabulary sizes match; debug on CPU first

Compatibility Notes

macOS: CUDA is not available on macOS. Use CPU mode or Apple MPS (Metal Performance Shaders) with PyTorch >= 1.12.
Cloud platforms: Colab, Kaggle, and Paperspace provide free GPU access and come with CUDA pre-installed.
Reproducibility: The `cudnn.deterministic = True` setting in utils.py ensures reproducible results but may slow training by 10-20%.
Multi-GPU: The Fastbook exercises are designed for single-GPU training. Multi-GPU setups work via fastai's distributed training but are not covered in the book.

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