# fastai **Repository Path**: xcpuma/fastai ## Basic Information - **Project Name**: fastai - **Description**: The fastai deep learning library, plus lessons and and tutorials - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-04-14 - **Last Updated**: 2020-12-28 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README [![Build Status](https://dev.azure.com/fastdotai/fastai/_apis/build/status/fastai.fastai)](https://dev.azure.com/fastdotai/fastai/_build/latest?definitionId=1) [![pypi fastai version](https://img.shields.io/pypi/v/fastai.svg)](https://pypi.python.org/pypi/fastai) [![Conda fastai version](https://img.shields.io/conda/v/fastai/fastai.svg)](https://anaconda.org/fastai/fastai) [![Anaconda-Server Badge](https://anaconda.org/fastai/fastai/badges/platforms.svg)](https://anaconda.org/fastai/fastai) [![fastai python compatibility](https://img.shields.io/pypi/pyversions/fastai.svg)](https://pypi.python.org/pypi/fastai) [![fastai license](https://img.shields.io/pypi/l/fastai.svg)](https://pypi.python.org/pypi/fastai) # fastai The fastai library simplifies training fast and accurate neural nets using modern best practices. See the [fastai website](https://docs.fast.ai) to get started. The library is based on research into deep learning best practices undertaken at [fast.ai](http://www.fast.ai), and includes \"out of the box\" support for [`vision`](https://docs.fast.ai/vision.html#vision), [`text`](https://docs.fast.ai/text.html#text), [`tabular`](https://docs.fast.ai/tabular.html#tabular), and [`collab`](https://docs.fast.ai/collab.html#collab) (collaborative filtering) models. For brief examples, see the [examples](https://github.com/fastai/fastai/tree/master/examples) folder; detailed examples are provided in the full documentation. For instance, here's how to train an MNIST model using [resnet18](https://arxiv.org/abs/1512.03385) (from the [vision example](https://github.com/fastai/fastai/blob/master/examples/vision.ipynb)): ```python untar_data(MNIST_PATH) data = image_data_from_folder(MNIST_PATH) learn = create_cnn(data, tvm.resnet18, metrics=accuracy) learn.fit(1) ``` ## Note for [course.fast.ai](http://course.fast.ai) students If you are using `fastai` for any [course.fast.ai](http://course.fast.ai) course, you need to use `fastai 0.7.x`. Please ignore the rest of this document, which is written for `fastai 1.0.x`, and instead follow the installation instructions [here](https://forums.fast.ai/t/fastai-v0-install-issues-thread/24652). *Note: If you want to learn how to use fastai v1 from its lead developer, Jeremy Howard, he will be teaching it in the [Deep Learning Part I](https://www.usfca.edu/data-institute/certificates/deep-learning-part-one) course at the University of San Francisco from Oct 22nd, 2018.* ## Installation `fastai-1.x` can be installed with either `conda` or `pip` package managers and also from source. At the moment you can't just run *install*, since you first need to get the correct `pytorch` version installed - thus to get `fastai-1.x` installed choose one of the installation recipes below using your favourite python package manager. If your system has a [recent NVIDIA card](https://www.geforce.com/hardware/technology/cuda/supported-gpus) with the correctly configured NVIDIA driver please follow the GPU installation instructions. Otherwise, the CPU-ones. It's highly recommended you install `fastai` and its dependencies in a virtual environment ([`conda`](https://conda.io/docs/user-guide/tasks/manage-environments.html) or others), so that you don't interfere with system-wide python packages. It's not that you must, but if you experience problems with any dependency packages, please consider using a fresh virtual environment just for `fastai`. If you experience installation problems, please read about [installation issues](https://github.com/fastai/fastai/blob/master/README.md#installation-issues). ### Conda Install * GPU ```bash conda install -c pytorch pytorch-nightly cuda92 conda install -c fastai torchvision-nightly conda install -c fastai fastai ``` * CPU ```bash conda install -c pytorch pytorch-nightly-cpu conda install -c fastai torchvision-nightly-cpu conda install -c fastai fastai ``` Note that JPEG decoding can be a bottleneck, particularly if you have a fast CPU. You can optionally install an optimized JPEG decoder as follows (Linux): ```bash conda uninstall --force jpeg libtiff -y conda install -c conda-forge libjpeg-turbo CC="cc -mavx2" pip install --no-cache-dir -U --force-reinstall pillow-simd ``` ### PyPI Install * GPU ```bash pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cu92/torch_nightly.html pip install fastai ``` * CPU ```bash pip install torch_nightly -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html pip install fastai ``` NB: this set will also fetch `torchvision-nightly`, which supports `torch-1.x`. ### Developer Install First, follow the instructions above for either `PyPi` or `Conda`. Then uninstall the `fastai` package using the **same package manager you used to install it**, i.e. `pip uninstall fastai` or `conda uninstall fastai`, and then, replace it with a [pip editable install](https://pip.pypa.io/en/stable/reference/pip_install/#editable-installs). ```bash git clone https://github.com/fastai/fastai cd fastai tools/run-after-git-clone pip install -e .[dev] ``` You can test that the build works by starting the jupyter notebook: ```bash jupyter notebook ``` and executing an example notebook. For example load `examples/tabular.ipynb` and run it. Alternatively, you can do a quick CLI test: ```bash jupyter nbconvert --execute --ExecutePreprocessor.timeout=600 --to notebook examples/tabular.ipynb ``` Please refer to [CONTRIBUTING.md](https://github.com/fastai/fastai/blob/master/CONTRIBUTING.md) and [develop.md](https://github.com/fastai/fastai/blob/master/docs/develop.md) for more details on how to contribute to the `fastai` project. ### Building From Source If for any reason you can't use the prepackaged packages and have to build from source, this section is for you. 1. To build `pytorch` from source follow the [complete instructions](https://github.com/pytorch/pytorch#from-source). Remember to first install CUDA, CuDNN, and other required libraries as suggested - everything will be very slow without those libraries built into `pytorch`. 2. Next, you will also need to build `torchvision` from source: ```bash git clone https://github.com/pytorch/vision cd vision python setup.py install ``` 3. When both `pytorch` and `torchvision` are installed, first test that you can load each of these libraries: ```bash import torch import torchvision ``` to validate that they were installed correctly Finally, proceed with `fastai` installation as normal, either through prepackaged pip or conda builds or installing from source ("the developer install") as explained in the sections above. ## Installation Issues If the installation process fails, first make sure [your system is supported](https://github.com/fastai/fastai/blob/master/README.md#is-my-system-supported). And if the problem is still not addressed, please refer to the [troubleshooting document](https://docs-dev.fast.ai/troubleshoot.html). If you encounter installation problems with conda, make sure you have the latest `conda` client (`conda install` will do an update too): ```bash conda install conda ``` ### Is My System Supported? 1. Python: You need to have python 3.6 or higher 2. CPU or GPU The `pytorch` binary package comes with its own CUDA, CuDNN, NCCL, MKL, and other libraries so you don't have to install system-wide NVIDIA's CUDA and related libraries if you don't need them for something else. If you have them installed already it doesn't matter which NVIDIA's CUDA version library you have installed system-wide. Your system could have CUDA 9.0 libraries, and you can still use `pytorch` build with `cuda9.2` libraries without any problem, since the `pytorch` binary package is self-contained. The only requirement is that you have installed and configured the NVIDIA driver correctly. Usually you can test that by running `nvidia-smi`. While it's possible that this application is not available on your system, it's very likely that if it doesn't work, than your don't have your NVIDIA drivers configured properly. And remember that a reboot is always required after installing NVIDIA drivers. 3. Operating System: Since fastai-1.0 relies on pytorch-1.0, you need to be able to install pytorch-1.0 first. As of this moment pytorch.org's pre-1.0.0 version (`torch-nightly`) supports: | Platform | GPU | CPU | |----------|--------|--------| | linux | binary | binary | | mac | source | binary | | windows | source | source | Legend: `binary` = can be installed directly, `source` = needs to be built from source. This will change once `pytorch` 1.0.0 is released and installable packages made available for your system, which could take some time after the official release is made. Please watch for updates [here](https://pytorch.org/get-started/locally/). If there is no `pytorch` preview conda or pip package available for your system, you may still be able to [build it from source](https://pytorch.org/get-started/locally/). Alternatively, please consider installing and using the very solid "0.7.x" version of `fastai`. Please see the [instructions](https://github.com/fastai/fastai/tree/master/old). 4. How do you know which pytorch cuda version build to choose? It depends on the version of the installed NVIDIA driver. Here are the requirements for CUDA versions supported by pre-built `pytorch-nightly` releases: | CUDA Toolkit | NVIDIA (Linux x86_64) | |--------------|-----------------------| | CUDA 9.2 | >= 396.26 | | CUDA 9.0 | >= 384.81 | | CUDA 8.0 | >= 367.48 | So if your NVIDIA driver is less than 384, then you can only use `cuda80`. Of course, you can upgrade your drivers to more recent ones if your card supports it. You can find a complete table with all variations [here](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html). ## History A detailed history of changes can be found [here](https://github.com/fastai/fastai/blob/master/CHANGES.md). ## Copyright Copyright 2017 onwards, fast.ai, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.