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开源软件名称(OpenSource Name):fastai/fastai开源软件地址(OpenSource Url):https://github.com/fastai/fastai开源编程语言(OpenSource Language):Jupyter Notebook 98.6%开源软件介绍(OpenSource Introduction):Welcome to fastaiInstallingYou 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. If you’re using
miniconda
(recommended) then run (note that if you replace conda install -c fastchan fastai …or if you’re using Anaconda then run: conda install -c fastchan fastai anaconda To install with pip, use: 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:
Learning fastaiThe 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 fastaifastai 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:
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 librariesIt’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: Windows SupportWhen installing with Due to python multiprocessing issues on Jupyter and Windows,
See this example to fully leverage the fastai API on Windows. TestsTo run the tests in parallel, launch:
For all the tests to pass, you’ll need to install the dependencies specified as part of dev_requirements in settings.ini
Tests are written using ContributingAfter you clone this repository, please run Before submitting a PR, check that the local library and notebooks
match. The script
Docker ContainersFor those interested in official docker containers for this project, they can be found here. |
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