fastai
fastai simplifies training fast and accurate neural nets using modern best practices
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires torch to be installed first; GPU support optional but recommended for training speed.
- Python 3.10 or later required.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing use in commercial and proprietary projects with minimal restrictions.
last release 2026-07-30 (15 days) · last repo commit 2026-07-30 · 28,114 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 698,617 downloads/mo, #5,298 on PyPI
Alternatives
Verify before relying
pip install fastai
import fastai
from fastai.vision.all import *
learn = vision_learner(dls, resnet, metrics=error_rate)- 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.
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 on this page. Verify before relying on it.
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
fastai on PyPI
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.
Requires torch to be installed first; GPU support optional but recommended for training speed. Python 3.10 or later required.
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)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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 17 packagesfastdownloadfastcorefasttransformtorchvisionmatplotlibpandasrequestspyyamlfastprogresspillowscikit-learnscipyspacypackagingplum-dispatchcloudpickletorch |
| Maintenance | Actively maintained 15 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 698,617 / month, #5,298 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: Only |
Evidence: fastai-2.8.8-py3-none-any.whl
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