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https://github.com/fastai/fastai

The fastai deep learning library
https://github.com/fastai/fastai

colab deep-learning fastai gpu machine-learning notebooks python pytorch

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The fastai deep learning library

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README 

        
# Welcome to fastai



[![CI](https://github.com/fastai/fastai/actions/workflows/main.yml/badge.svg)](https://github.com/fastai/fastai/actions/workflows/main.yml)

[![PyPI](https://img.shields.io/pypi/v/fastai?color=blue&label=pypi%20version.png)](https://pypi.org/project/fastai/#description)

[![Conda (channel

only)](https://img.shields.io/conda/vn/fastai/fastai?color=seagreen&label=conda%20version.png)](https://anaconda.org/fastai/fastai)

![docs](https://github.com/fastai/fastai/workflows/docs/badge.svg)



## Installing



You can use fastai without any installation by using [Google

Colab](https://colab.research.google.com/). 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](https://course.fast.ai/start_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](https://docs.conda.io/en/latest/miniconda.html) (or

miniforge). First install PyTorch using the conda line shown

[here](https://pytorch.org/get-started/locally/), and then run:



``` bash

conda install -c 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](https://github.com/fastai/fastcore) to go with it.) First

install PyTorch, and then:



    git clone https://github.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](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527),

and complete [the free course](https://course.fast.ai).



To see what’s possible with fastai, take a look at the [Quick

Start](https://docs.fast.ai/quick_start.html), 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](https://docs.fast.ai/tutorial.html) 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](https://www.mdpi.com/2078-2489/11/2/108/htm).



## 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.



Layered API



## 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](https://docs.fast.ai/examples/migrating_pytorch.html)

- [Ignite](https://docs.fast.ai/examples/migrating_ignite.html)

- [Lightning](https://docs.fast.ai/examples/migrating_lightning.html)

- [Catalyst](https://docs.fast.ai/examples/migrating_catalyst.html)



## 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](https://github.com/fastai/fastai/blob/master/nbs/examples/dataloader_spawn.py)

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](https://github.com/fastai/docker-containers#fastai).