---
id: fastai
version: "2.8.8"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# fastai — fastai simplifies training fast and accurate neural nets using modern best practices

License: permissive · Maintenance: active · Downloads: 698.6K/mo

## What it is and what it does

fastai is a PyTorch-based deep learning library designed to make neural network training accessible to practitioners while remaining flexible for researchers. It abstracts away boilerplate training code through high-level APIs that handle common tasks like image classification, segmentation, text sentiment analysis, tabular modeling, and recommendation systems with minimal code. The library layers composable building blocks—from a type dispatch system and GPU-optimized computer vision components to a novel callback system and data block API—so users can work at the level of abstraction they need, from quick prototyping to low-level customization.

The library depends on 17 runtime packages including torch, torchvision, pandas, scikit-learn, and spacy, making it a comprehensive toolkit for end-to-end deep learning workflows. It is actively maintained, recently released, and documented with tutorials, a peer-reviewed paper, and a free course. The codebase is organized to be both approachable for beginners and deeply hackable for advanced users.

Use it for:

- Build an image classifier by using pretrained models and transfer learning.
- Train a text sentiment classifier or other NLP model on custom datasets.
- Create tabular models for structured data prediction tasks.
- Implement custom training loops and optimization algorithms using the callback system.
- Migrate from plain PyTorch while reusing existing data processing code.
- Experiment with computer vision and recommendation systems in Jupyter notebooks or Google Colab.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

fastai is a deep learning library that provides high-level APIs for training neural networks on images, text, tabular data, and recommendations, built on top of PyTorch.

Yes. fastai is actively maintained, has low install friction, carries a permissive license, and offers a well-documented, high-level API for deep learning on top of PyTorch. It is suitable for both rapid prototyping and research. Choose it if you want to reduce boilerplate training code and leverage modern best practices without sacrificing flexibility.

## Install

pip install fastai
uv add fastai
poetry add fastai

## Installing fastai

Before you install: Low install friction with a pure-wheel distribution. Actively maintained with a recent release (15 days ago) and strong repository activity (28114 stars). Requires Python 3.10 or later and torch as a core dependency.

License in practice: Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects with minimal restrictions.

Quickstart:

pip install fastai
import fastai
from fastai.vision.all import *
learn = vision_learner(dls, resnet, metrics=error_rate)

Requires torch to be installed first; GPU support optional but recommended for training speed. Python 3.10 or later required.

Verify before relying:

- Specific pretrained model architectures available and their performance on standard benchmarks.
- Whether the callback system and data block API are documented with sufficient examples for typical use cases.
- Compatibility with the latest versions of all 17 runtime dependencies, particularly torch and torchvision.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 698.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

deep learning library pytorch, image classification neural networks, fast model training, computer vision pytorch, text sentiment analysis deep learning, deep-learning, pytorch, computer-vision

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