fastai makes deep learning with PyTorch faster, more accurate, and easier
The fastai library simplifies training fast and accurate neural nets using modern best practices. See the fastai website to get started. The library is based on research into deep learning best practices undertaken at fast.ai, and includes "out of the box" support for
collab (collaborative filtering) models. For brief examples, see the examples folder; detailed examples are provided in the full documentation. For instance, here's how to train an MNIST model using resnet18 (from the vision example):
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 students
If you are using
fastai for any course.fast.ai course, you need to use
fastai 0.7. Please ignore the rest of this document, which is written for
fastai v1, and instead follow the installation instructions here.
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 course at the University of San Francisco from Oct 22nd, 2018.
NB: fastai v1 currently supports Linux only, and requires PyTorch v1 (currently in preview) and Python 3.6 or later. Once pytorch v1 packages are available for Windows, we will work to support Windows as soon as possible. Since Macs don't currently have good Nvidia GPU support, we do not currently prioritize Mac development.
fastai-1.x can be installed with either
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. Note that PyTorch v1 and Python 3.6 are the minimal version requirements.
If your system has a recent NVIDIA card 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 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
If you experience installation problems, please read about installation issues.
More advanced installation issues, such as installing only partial dependencies are covered in a dedicated installation doc.
conda install -c pytorch pytorch torchvision conda install -c fastai fastai
Note that JPEG decoding can be a bottleneck, particularly if you have a fast GPU. You can optionally install an optimized JPEG decoder as follows (Linux):
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
pip install torch torchvision pip install fastai
First, follow the instructions above for either
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.
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:
and executing an example notebook. For example load
examples/tabular.ipynb and run it.
Alternatively, you can do a quick CLI test:
jupyter nbconvert --execute --ExecutePreprocessor.timeout=600 --to notebook examples/tabular.ipynb
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.
pytorchfrom source follow the complete instructions. Remember to first install CUDA, CuDNN, and other required libraries as suggested - everything will be very slow without those libraries built into
Next, you will also need to build
git clone https://github.com/pytorch/vision cd vision python setup.py install
torchvisionare installed, first test that you can load each of these libraries:
import torch import torchvision
to validate that they were installed correctly
Finally, proceed with
fastaiinstallation as normal, either through prepackaged pip or conda builds or installing from source ("the developer install") as explained in the sections above.
If you encounter installation problems with conda, make sure you have the latest
conda client (
conda install will do an update too):
conda install conda
Is My System Supported?
Python: You need to have python 3.6 or higher
CPU or GPU
pytorchbinary 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
cuda9.2libraries without any problem, since the
pytorchbinary 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.
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 1.0 version supports:
Platform GPU CPU linux binary binary mac source binary windows binary binary
binary= can be installed directly,
source= needs to be built from source.
If there is no
pytorchpreview conda or pip package available for your system, you may still be able to build it from source.
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
CUDA Toolkit NVIDIA (Linux x86_64) CUDA 10.0 >= 410.00 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.
In order to update your environment, simply install
fastai in exactly the same way you did the initial installation.
Top level files
environment-cpu.yml belong to the old fastai (0.7).
conda env update is no longer the way to update your
fastai-1.x environment. These files remain because the fastai course-v2 video instructions rely on this setup. Eventually, once fastai course-v3 p1 and p2 will be completed, they will probably be moved to where they belong - under
A detailed history of changes can be found here.
Copyright 2017 onwards, fast.ai, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this project's files except in compliance with the License. A copy of the License is provided in the LICENSE file in this repository.
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