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FastAI

AI & Machine LearningML/AIPython
Official docs ↗GitHub ↗

What it is

FastAI is a high-level deep learning library built on top of PyTorch, designed to make training neural networks fast, accurate, and accessible. It provides abstractions and best practices for vision, text, tabular, and collaborative filtering tasks.

FastAI provides high-level APIs for building, training, and interpreting models with minimal boilerplate. It integrates with PyTorch for low-level control and includes utilities for data preprocessing, augmentation, and visualization.

Installation

pip install fastai

Getting started

The smallest useful thing you can do with it, and what each part means.

Image classification with a pre-trained model
from fastai.vision.all import *
path = untar_data(URLs.PETS)
dls = ImageDataLoaders.from_name_re(path, get_image_files(path/'images'), pat=r'(.+)_\d+.jpg$', item_tfms=Resize(224))
learn = vision_learner(dls, resnet34, metrics=accuracy)
learn.fine_tune(1)
Loads a pet image dataset, creates data loaders, defines a ResNet34 model, and fine-tunes it for one epoch.
Text classification
from fastai.text.all import *
dls = TextDataLoaders.from_csv(path, 'texts.csv', text_col='text', label_col='label')
learn = text_classifier_learner(dls, AWD_LSTM, metrics=accuracy)
learn.fine_tune(1)
Loads text data from CSV, creates a data loader, defines an AWD_LSTM model, and fine-tunes it.

Advanced usage

Where the library earns its place over a simpler alternative.

Tabular data modeling
from fastai.tabular.all import *
df = pd.read_csv('data.csv')
splits = RandomSplitter()(range_of(df))
tb = TabularPandas(df, y_names='target', cat_names=['cat1','cat2'], cont_names=['cont1','cont2'], procs=[Categorify, FillMissing, Normalize], splits=splits)
dls = tb.dataloaders()
learn = tabular_learner(dls, metrics=accuracy)
learn.fit_one_cycle(5)
Prepares tabular data with categorical and continuous columns, applies preprocessing, and trains a tabular model.
Collaborative filtering
from fastai.collab import *
df = pd.read_csv('ratings.csv')
dls = CollabDataLoaders.from_df(df, item_name='movie', user_name='user', rating_name='rating')
learn = collab_learner(dls, n_factors=50, y_range=(0,5.5))
learn.fit_one_cycle(5)
Uses FastAI to build a collaborative filtering model for predicting ratings.
Learning rate finder
learn.lr_find()
Plots a learning rate curve to help select an optimal learning rate for training.
Model interpretation
interp = ClassificationInterpretation.from_learner(learn)
interp.plot_confusion_matrix()
Generates a confusion matrix to analyze model predictions.

Errors and fixes

The failures you are most likely to hit, and what actually resolves them.

RuntimeError: CUDA out of memory
Reduce batch size or move computation to CPU if GPU memory is insufficient.
ValueError: DataLoader empty
Check that your dataset paths and preprocessing steps are correct.
ModuleNotFoundError: No module named 'fastai'
Install FastAI using pip or conda in your current Python environment.

Best practices

  • Use pre-trained models for transfer learning when possible.
  • Use `fit_one_cycle` for efficient and stable training.
  • Leverage FastAI's data block API for flexible data preprocessing.
  • Visualize results and errors using built-in interpretation methods.
  • Combine FastAI with PyTorch for full control over model architecture.

Background

Why it exists, and what it was reacting to.

FastAI was created by Jeremy Howard and Rachel Thomas in 2018 to simplify deep learning workflows while retaining flexibility. It emphasizes practical, hands-on learning, and is widely used in both research and production for rapid prototyping of AI models.

Related libraries

Others in AI & Machine Learning for Python.

← All Python librariesPython guide