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README.en.md

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+# Welcome to fastai
+
+
+<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
+
+[![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)
+
+## 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://course19.fast.ai/start_colab.html) for more
+information.
+
+You can install fastai on your own machines with: `pip install fastai`.
+
+To ensure that you have the best available version of PyTorch on your
+machine, recommend
+[installing](https://pytorch.org/get-started/locally/) that first.
+
+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.
+
+<img alt="Layered API" src="images/layered.png" width="345">
+
+## 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).