FastAI

Language: Python

ML/AI

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.

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.

Installation

pip: pip install fastai
conda: conda install -c fastai fastai

Usage

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.

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.

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.

Error Handling

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.

Official DocsGithub