fastai

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What fastai is

fastai is a free Python library for training neural networks, with high-level tools for practitioners and lower-level components researchers can combine into new approaches. Its quick start covers image classification and segmentation, text sentiment, recommendation, and tabular models. The library includes a Python type dispatch system, GPU-optimized computer vision tools, an optimizer, callbacks, and a data block API. It can be installed with `pip install fastai`, with PyTorch recommended first, or used in Google Colab without local installation. Documentation pages are available as interactive notebooks, and fastai can work alongside other PyTorch-based libraries. Migration guides cover plain PyTorch, Ignite, Lightning, and Catalyst. The library supports local training, GPU acceleration, and distributed training. In Jupyter on Windows, fastai sets DataLoader `num_workers` to 0 to avoid hangs, which can make computer vision tasks many times slower than on Linux. The documentation recommends Windows Subsystem for Linux; this Jupyter limitation does not apply when fastai runs from a script. The repository lists the Apache-2.0 license.

Who it suits

fastai suits Python users training neural networks, from practitioners applying existing components to researchers assembling new approaches. Its free Practical Deep Learning course is designed for people with some coding experience who want to apply machine learning to practical problems.

What it does well

Quick start covers vision, text, recommendations, and tabular models.
Supports GPU acceleration and distributed training.
Can be used in Google Colab without installation.
Works with other PyTorch-based libraries.

What to know first

Jupyter on Windows can make computer vision tasks much slower.
PyTorch is recommended before installing fastai.

Plans

Plan	Billed and limits	Price
fastai	Python deep learning library; install with pip or use Google Colab	Free

As fast.ai lists them, read 10 Oct 2026. The maker's own page has the final word.

Getting started

Install PyTorch first, as the documentation recommends.
Install fastai on your machine with `pip install fastai`.
Alternatively, use fastai in Google Colab without local installation.
Open documentation pages as interactive notebooks to work through them.

Verdict

fastai offers a broad set of Python components for training models and can run locally or in Google Colab. Windows Jupyter users should account for the documented slowdown in computer vision tasks.

Questions

How much does fastai cost?

The listed plan is free.

Is there a free trial?

No. fastai is listed as free, with no free trial.

Which platforms does fastai support?

The listed platforms are Linux, macOS, self-hosted, web, and Windows.

What language does fastai use?

The supported language listed is Python.

Is fastai open source?

The fastai GitHub repository identifies its license as Apache-2.0.

Can I use fastai without installing it?

Yes. The documentation says it can be used through Google Colab without installation.

Facts from fastai's own pages

Audience: The fast.ai site says it works to make deep learning easier to use and involve more people from all backgrounds through free courses, a software library, research, and community. · fast.ai, 10 Oct 2026
Compatibility: The documentation provides migration guides for plain PyTorch, Ignite, Lightning, and Catalyst, and says fastai can be used with other PyTorch-based libraries. · docs.fast.ai, 10 Oct 2026
Course: The Practical Deep Learning course is free and designed for people with some coding experience who want to apply deep learning and machine learning to practical problems. · course.fast.ai, 10 Oct 2026
Course integrations: The course says learners use PyTorch, fastai, Hugging Face Transformers, and Gradio. · course.fast.ai, 10 Oct 2026
Installation: The documentation says to install fastai on a machine with `pip install fastai` and recommends installing PyTorch first. · docs.fast.ai, 10 Oct 2026
Key components: fastai includes a Python type dispatch system, GPU-optimized computer vision library, optimizer, callback system, and data block API. · docs.fast.ai, 10 Oct 2026
License: The fastai GitHub repository identifies its license as Apache-2.0. · github.com, 10 Oct 2026
Model tasks: Its quick start demonstrates image classification, image segmentation, text sentiment, recommendation, and tabular models. · docs.fast.ai, 10 Oct 2026
Notebook use: The documentation says fastai can be used without installation through Google Colab, and each documentation page is available as an interactive notebook. · docs.fast.ai, 10 Oct 2026
Practitioners and researchers: The library provides high-level components for practitioners and low-level components researchers can combine to build new approaches. · docs.fast.ai, 10 Oct 2026
Purpose: fastai simplifies training fast and accurate neural networks using modern best practices. · docs.fast.ai, 10 Oct 2026
Support: The fast.ai forums include a category for help installing and using the fastai library for users at any level. · forums.fast.ai, 10 Oct 2026
Windows limitation: In Jupyter on Windows, fastai resets DataLoader `num_workers` to 0 to avoid hanging, which can make computer vision tasks many times slower than on Linux. · docs.fast.ai, 10 Oct 2026
Windows workaround: The documentation recommends Windows Subsystem for Linux; it says the Jupyter limitation does not apply when using fastai from a script. · docs.fast.ai, 10 Oct 2026

Where fastai stands on Freedom251

Deep Learning Software#6 of 35