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Installation

Basic installation

Please refer to README for bulk of the instructions

CPU build

Generally, pytorch GPU build should work fine on machines that don't have a CUDA-capable GPU, and will just use the CPU. However, you can install CPU-only versions of Pytorch if needed:

  • conda

    conda install -c pytorch pytorch-cpu torchvision
conda install -c fastai fastai

  • pip

    pip install http://download.pytorch.org/whl/cpu/torch-1.0.0-cp36-cp36m-linux_x86_64.whl
pip install fastai

Jupyter notebook dependencies

The fastai library doesn't require the jupyter environment to work, therefore those dependencies aren't included. So if you are planning on using fastai in the jupyter notebook environment, e.g. to run the fastai course lessons and you haven't already setup the jupyter environment, here is how you can do it.

  • conda

    conda install jupyter notebook
conda install -c conda-forge jupyter_contrib_nbextensions

    Some users also seem to need this conda package to be able to choose the right kernel environment, however, most likely you won't need this package.

    conda install nb_conda

  • pip

    pip install jupyter notebook jupyter_contrib_nbextensions

Custom dependencies

If for any reason you don't want to install all of fastai's dependencies, since, perhaps, you have limited disk space on your remote instance, here is how you can install only the dependencies that you need.

  1. First, install fastai without its dependencies using either pip or conda:

    # pip
pip install --no-deps fastai
# conda
conda install --no-deps -c fastai fastai

  2. The rest of this section assumes you're inside the fastai git repo, since that's where setup.py resides. If you don't have the repository checked out, do:

    git clone https://github.com/fastai/fastai
cd fastai
tools/run-after-git-clone

  3. Next, find out which groups of dependencies you want:

    python setup.py -q deps

    You should get something like:

    Available dependency groups: core, text, vision

    You need to use at least the core group.

    Do note that the deps command is a custom distutils extension, i.e. it only works in the fastai setup.

  4. Finally, install the custom dependencies for the desired groups.

    For the sake of this demonstration, let's say you want to get the core dependencies (core), plus dependencies specific to computer vision (vision). The following command will give you the up-to-date dependencies for these two groups:

    python setup.py -q deps --dep-groups=core,vision

    It will return something like:

    Pillow beautifulsoup4 bottleneck dataclasses;python_version<'3.7' fastprogress>=0.1.18 matplotlib numexpr numpy>=1.12 nvidia-ml-py3 packaging pandas pyyaml requests scipy torch>=1.0.0 torchvision typing

    which can be fed directly to pip install:

    pip install $(python setup.py -q deps --dep-groups=core,vision)

    Since conda uses a slightly different syntax/package names, to get the same output suitable for conda, add --dep-conda:

    python setup.py -q deps --dep-groups=core,vision --dep-conda

    If your shell doesn't support $() syntax, it most likely will support backticks, which are deprecated in modern bash. (The two are equivalent, but $() has a superior flexibility). If that's your situation, use the following syntax instead:

    pip install `python setup.py -q deps --dep-groups=core,vision`

  • Manual copy-n-paste case:

    If, instead of feeding the output directly to pip or conda, you want to do it manually via copy-n-paste, you need to quote the arguments, in which case add the --dep-quote option, which will do it for you:

    # pip:
python setup.py -q deps --dep-groups=core,vision --dep-quote
# conda:
python setup.py -q deps --dep-groups=core,vision --dep-quote --dep-conda

    So the output for pip will look like:

    "Pillow" "beautifulsoup4" "bottleneck" "dataclasses;python_version<'3.7'" "fastprogress>=0.1.18" "matplotlib" "numexpr" "numpy>=1.12" "nvidia-ml-py3" "packaging" "pandas" "pyyaml" "requests" "scipy" "torch>=1.0.0" "torchvision" "typing"

  • Summary:

    pip selective dependency installation:

    pip install --no-deps fastai
pip install $(python setup.py -q deps --dep-groups=core,vision)

    same for conda:

    conda install --no-deps -c fastai fastai
conda install -c pytorch -c fastai $(python setup.py -q deps --dep-conda --dep-groups=core,vision)

    adjust the --dep-groups argument to match your needs.

  • Full usage:

    # show available dependency groups:
python setup.py -q deps

# print dependency list for specified groups
python setup.py -q deps --dep-groups=core,vision

# see all options:
python setup.py -q deps --help

Virtual environment

It's highly recommended to use a virtual python environment for the fastai project, first because you could experiment with different versions of it (e.g. stable-release vs. bleeding edge git version), but also because it's usually a bad idea to install various python package into the system-wide python, because it's so easy to break the system, if it relies on python and its 3rd party packages for its functionality.

There are several implementations of python virtual environment, and the one we recommend is conda (anaconda), because we release our packages for this environment and pypi, as well. conda doesn't have all python packages available, so when that's the case we use pip to install whatever is missing.

You will find the instructions for installing conda on each platform here. Once you followed the instructions and installed anaconda, you're ready to build you first environment. For the sake of this example we will use an environment name fastai, but you can name it whatever you'd like it to be.

The following will create a fastai env with python-3.6:

conda create -n fastai python=3.6

Now any time you'd like to work in this environment, just execute:

conda activate fastai

It's very important that you activate your environment before you start the jupyter notebook if you're using fastai notebooks.

Say, you'd like to have another env to test fastai with python-3.7, then you'd create another one with:

conda create -n fastai-py37 python=3.7

and to activate that one, you'd call:

conda activate fastai-py37

If you'd like to exit the environment, do:

conda deactivate

To list out the available environments

conda env list

Also see bash-git-prompt which will help you tell at any moment which environment you're in.