Welcome to fastai
Installing
You can use fastai without any installation by using Google
Colab. In fact, every page of this
documentation is also available as an interactive notebook – click “Open
in colab” at the top of any page to open it (be sure to change the Colab
runtime to “GPU” to have it run fast!) See the fast.ai documentation on
Using Colab for more information.
You can install fastai on your own machines with conda (highly
recommended), as long as you’re running Linux or Windows (NB: Mac is not
supported). For Windows, please see the “Running on Windows” for
important notes.
We recommend using
miniconda (or
miniforge). First install PyTorch using the conda line shown
here, and then run:
conda install fastai::fastai
To install with pip, use: pip install fastai.
If you plan to develop fastai yourself, or want to be on the cutting
edge, you can use an editable install (if you do this, you should also
use an editable install of
fastcore to go with it.) First
install PyTorch, and then:
git clone https://gi**thub.*com/fastai/fastai
pip install -e \"fastai[dev]\"
Learning fastai
The best way to get started with fastai (and deep learning) is to read
the
book,
and complete the free course.
To see what’s possible with fastai, take a look at the Quick
Start, which shows how to use
around 5 lines of code to build an image classifier, an image
segmentation model, a text sentiment model, a recommendation system, and
a tabular model. For each of the applications, the code is much the
same.
Read through the Tutorials to
learn how to train your own models on your own datasets. Use the
navigation sidebar to look through the fastai documentation. Every
class, function, and method is documented here.
To learn about the design and motivation of the library, read the peer
reviewed paper.
About fastai
fastai is a deep learning library which provides practitioners with
high-level components that can quickly and easily provide
state-of-the-art results in standard deep learning domains, and provides
researchers with low-level components that can be mixed and matched to
build new approaches. It aims to do both things without substantial
compromises in ease of use, flexibility, or performance. This is
possible thanks to a carefully layered architecture, which expresses
common underlying patterns of many deep learning and data processing
techniques in terms of decoupled abstractions. These abstractions can be
expressed concisely and clearly by leveraging the dynamism of the
underlying Python language and the flexibility of the PyTorch library.
fastai includes:
- A new type dispatch system for Python along with a semantic type
hierarchy for tensors - A GPU-optimized computer vision library which can be extended in pure
Python - An optimizer which refactors out the common functionality of modern
optimizers into two basic pieces, allowing optimization algorithms to
be implemented in 4–5 lines of code - A novel 2-way callback system that can access any part of the data,
model, or optimizer and change it at any point during training - A new data block API
- And much more…
fastai is organized around two main design goals: to be approachable and
rapidly productive, while also being deeply hackable and configurable.
It is built on top of a hierarchy of lower-level APIs which provide
composable building blocks. This way, a user wanting to rewrite part of
the high-level API or add particular behavior to suit their needs does
not have to learn how to use the lowest level.
Migrating from other libraries
It’s very easy to migrate from plain PyTorch, Ignite, or any other
PyTorch-based library, or even to use fastai in conjunction with other
libraries. Generally, you’ll be able to use all your existing data
processing code, but will be able to reduce the amount of code you
require for training, and more easily take advantage of modern best
practices. Here are migration guides from some popular libraries to help
you on your way:
- Plain PyTorch
- Ignite
- Lightning
- Catalyst
Windows Support
Due to python multiprocessing issues on Jupyter and Windows,
num_workers of Dataloader is reset to 0 automatically to avoid
Jupyter hanging. This makes tasks such as computer vision in Jupyter on
Windows many times slower than on Linux. This limitation doesn’t exist
if you use fastai from a script.
See this
example
to fully leverage the fastai API on Windows.
We recommend using Windows Subsystem for Linux (WSL) instead – if you do
that, you can use the regular Linux installation approach, and you won’t
have any issues with num_workers.
Tests
To run the tests in parallel, launch:
nbdev_test
For all the tests to pass, you’ll need to install the dependencies
specified as part of dev_requirements in settings.ini
pip install -e .[dev]
Tests are written using nbdev, for example see the documentation for
test_eq.
Contributing
After you clone this repository, make sure you have run
nbdev_install_hooks in your terminal. This install Jupyter and git
hooks to automatically clean, trust, and fix merge conflicts in
notebooks.
After making changes in the repo, you should run nbdev_prepare and
make additional and necessary changes in order to pass all the tests.
Docker Containers
For those interested in official docker containers for this project,
they can be found
here.
