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Installation

tip

We strongly recommend using Conda as the environment manager when dealing with deep learning / data science / machine learning.

classy requires Python 3.8 or later, and is built on PyTorch Lightning. It's recommended that you install the PyTorch ecosystem before installing classy by following the instructions on pytorch.org.

Or, simply put:

conda install pytorch cudatoolkit=CUDA_VERSION -c pytorch
tip

Don't know what CUDA_VERSION you have? Check this link.

The preferred way to install classy is via pip. Just run pip install classy-core.

Installing via pip​

Setting up a virtual environment​

Conda can be used set up a virtual environment with the version of Python required for classy. If you already have a Python 3 environment you want to use, you can skip to the Installing the library and dependencies section.

  1. Download and install Conda.

  2. Create a Conda environment with Python 3.8+:

    conda create -n classy python=3.8

  3. Activate the Conda environment:

    conda activate classy

Installing the library and dependencies​

Simply execute

pip install classy-core

and voilà! You're all set.

Looking for some adventures? Install nightly releases directly from pypi! You will not regret it :)

Installing from source​

You can also install classy by cloning this repository:

git clone https://github.com/sunglasses-ai/classy.git
cd classy

Follow the steps at setting up a virtual environment and then install classy by

pip install -e .

This will make classy available in your environment, but using the sources of the cloned repository.

Using classy via docker​

Alternatively, we also release and mantain a Docker image with everything already set up. In order to use it locally, you need to install:

Once set up, you can just run docker run --gpus all -it poccio/classy bash (or checkout poccio/classy for specific version tags) and you'll get a shell on a fully-setup container, with a classy conda environment already created.

Alternatively, if you use external cloud computing platforms that support docker images (e.g. vast.ai), just providing the image name (poccio/classy) should suffice (it's hosted on Dockerhub).