A PyTorch-based library for fast prototyping and sharing of deep neural network models.
If this is your first time meeting classy, don't worry! We have plenty of resources to help you learn how it works and what it can do for you.
For starters, have a look at our amazing website and our documentation!
If you want to get your hands dirty right away, have a look at our base classy template.
Also, we have a few examples that you can look at to get to know classy!
For a more in-depth installation guide (covering also installing from source and through docker), please visit our installation page.
If you are using one of our templates, there is a handy setup.sh script you can use that will execute the commands to create the environment and install classy for you.
We strongly recommend using Conda as the environment manager when dealing with deep learning / data science / machine learning. It's also recommended that you install the PyTorch ecosystem before installing classy by following the instructions on pytorch.org
If you already have a Python 3 environment you want to use, you can skip to the Installing the library and dependencies section.
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Create a Conda environment with Python 3.8+:
conda create -n classy python=3.8 -
Activate the Conda environment:
conda activate classy
Simply execute
pip install classy-coreand voilà! You're all set.
Looking for some adventures? Install nightly releases directly from pypi! You will not regret it :)
Once it is installed, classy is available as a command line tool. It offers a wide variety of subcommands, all listed below. Detailed guides and references for each command is available in the documentation.
Every one of classy's subcommands have a -h|--help flag available which details the various arguments & options you can use (e.g., classy train -h).
In its simplest form, classy train lets you train a transformer-based neural network for one of the tasks supported by classy (see the documentation).
classy train sentence-pair path/to/dataset/folder-or-file -n my-modelThe command above will train a model to predict a label given a pair of sentences as input (e.g., Natural Language Inference or NLI) and save it under experiments/my-model. This same model can be further used by all other classy commands which require a classy model (predict, evaluate, serve, demo, upload).
classy predict actually has two subcommands: interactive and file.
The first loads the model in memory and lets you try it out through the shell directly, so that you can test the model you trained and see what it predicts given some input. It is particularly useful when your machine cannot open a port for classy demo.
The second, instead, works on a file and produces an output where, for each input, it associates the corresponding predicted label. It is very useful when doing pre-processing or when you need to evaluate your model (although we offer classy evaluate for that).
classy evaluate lets you evaluate your model on standard metrics for the task your model was trained upon. Simply run classy evaluate my-model path/to/file -o path/to/output/file and it will dump the evaluation at path/to/output/file
classy serve <model> loads the model in memory and spawns a REST API you can use to query your model with any REST client.
classy demo <model> spawns a Streamlit interface which lets you quickly show and query your model.
classy describe <task> --dataset path/to/dataset runs some common metrics on a file formatted for the specific task. Great tool to run before training your model!
classy upload <model> lets you upload your classy-trained model on the HuggingFace Hub and lets other users download / use it. (NOTE: you need a HuggingFace Hub account in order to upload to their hub)
Models uploaded via classy upload will be available for download by other classy users by simply executing classy download username@model.
classy download <model> downloads a previously uploaded classy-trained model from the HuggingFace Hub and stores it on your machine so that it is usable with any other classy command which requires a trained model (predict, evaluate, serve, demo, upload).
Models uploaded via classy upload are available by doing classy download username@model.
To install shell completion, activate your conda environment and then execute
classy --install-autocompleteFrom now on, whenever you activate your conda environment with classy installed, you are going to have autocompletion when pressing [TAB]!
You are more than welcome to file issues with either feature requests, bug reports, or general questions. If you already found a solution to your problem, don't hesitate to share it. Suggestions for new best practices and tricks are always welcome!
We warmly welcome contributions from the community. If it is your first time as a contributor, we recommend you start by reading our CONTRIBUTING.md guide.
Small contributions can be made directly in a pull request. For contributing major features, we recommend you first create a issue proposing a design, so that it can be discussed before you risk wasting time.
Pull requests (PRs) must have one approving review and no requested changes before they are merged.
As classy is primarily driven by SunglassesAI, we reserve the right to reject or revert contributions that we don't think are good additions or might not fit into our roadmap.