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)
course.fast.ai studentsNote for
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 and Python 3.6 or later. Windows support is at an experimental stage: it should work fine but we haven't thoroughly tested it. 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 -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 --no-binary :all: --compile pillow-simd
If you only care about faster JPEG decompression, it can be
pillow-simd in the last command above, the latter speeds up other image processing operations. For the full story see Pillow-SIMD.
pip install fastai
If a bug fix was made in git and you can't wait till a new release is made, you can install the bleeding edge version of
pip install git+https://github.com/fastai/fastai.git
The following instructions will result in a pip editable install, so that you can
git pull at any time and your environment will automatically get the updates:
git clone https://github.com/fastai/fastai cd fastai tools/run-after-git-clone pip install -e ".[dev]"
Note that this will install the
pytorch build via default dependencies. If you need a higher or lower
cudaXX build, following the instructions here, to install the desired
Next, 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.
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
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 9.0 >= 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.
If you use NVIDIA driver 410+, you most likely want to install the
cuda100pytorch variant, via:
conda install -c pytorch pytorch cuda100
or if you need a lower version (
cuda90is installed by default), use:
conda install -c pytorch pytorch cuda80
For other options refer to the complete list of the available pytorch variants.
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
We use GitHub issues for tracking requests and bugs, so please see fastai forum for general questions and discussion.
The fastai project strives to abide by generally accepted best practices in open-source software development:
A detailed history of changes can be found here.
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