Understand the project
Practical overview
When it fits
Use fastai when you want a concise, higher-level route to common deep-learning workflows while preserving access to composable PyTorch-based components for customisation.
Who it is for
- Python and PyTorch developers
- Deep-learning researchers
- Machine-learning learners using Jupyter notebooks
- Teams prototyping models in Google Colab or local environments
Documented uses
- Build image-classification and image-segmentation models.
- Train text sentiment, recommendation and tabular models.
- Create configurable training workflows with data blocks, callbacks and optimisers.
- Develop educational or experimental Jupyter notebooks.
- Adapt existing PyTorch-based code into shorter training workflows.
Before you choose
Requirements & limits
The repository documents use through Google Colab without local installation and local installation with pip after installing PyTorch. It is public and licensed under Apache-2.0.
- Google Colab provides the documented no-local-installation route and can use a GPU runtime.
- Local setup requires PyTorch first, followed by fastai installation.
- Windows Jupyter workflows reset DataLoader workers to zero because of multiprocessing issues, which can reduce computer-vision performance.
- The layered API supports both concise high-level workflows and lower-level customisation.
- The supplied migration documentation covers plain PyTorch, Ignite, Lightning and Catalyst.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 1 October 2026
- Original website title
- fastai/fastai · Official homepage title · retained in its source language