Meta framework for Deep Learning frameworks with PyTorch.

Getting Started

carefree-core requires:

carefree-core is not meant to be installed, but rather as a 'portable' package that integrates into your own project.

To use this library, follow the instructions below:

Copy the core folder to somewhere in your project.
Copy the install_requires listed in setup.py into your own setup.py.
Copy the setup.cfg file into your own project, if you want to follow the mypy style.

And that's all - the codes are yours, modify them as you wish!

Introductions

core/toolkit

Originates from carefree-toolkit.

This package is meant to hold the common classes / functions that are used across the whole library.

core/flow

Originates from carefree-workflow.

This package is a lightweight package for building arbitray workflows, here are the highlights:

Async: async is by design.
Parallel: nodes can be executed in parallel.
Powerful: complex locks / logics / dependencies can be handled.
- You can even perform a loop with loop backs in the workflow!
Automated:
- All nodes, as well as the workflow itself, can be automatically turned into RESTful APIs.
- Detailed documentation of the design / nodes / workflow / ... can be automatically generated, which makes this package and its extended versions agent-friendly.
  - That is to say, you can build a GPT-agent on top of this package by simply feed the auto-generated documentation to it. After which, you can interact with the agent via natural language and it will tell you how to build the workflow you want (it may even be able to give you the final workflow JSON directly)!
  - We even support auto-generating a 'RAG friendly' version of the documentation, which makes Retrieval-Augmented Generation easier. This version uses __RAG__ as the special separator, so you can chunk the documentation into suitable parts for RAG.
Extensible: you can easily extend the package with your own nodes.
Serializable: the workflow can be serialized into / deserialized from a single JSON file.
Human Readable: the workflow JSON file is human readable and easy to understand.
Lightweight: the package is lightweight (core implementation is ~500 lines of code in a single file core/flow/core.py) and easy to use.

core/learn

Originates from carefree-learn.

This package is the 'main' package of the PyTorch framework, here are some main design principles:

Data: Will use torch.utils.data.DataLoader as the data loader.
Model: Will be split into module and model:
- module is the key part of the Model, and should be self-contained at inference stage.
- model is the wrapper of module, and should contain the training / evaluation / ... logic.
Trainer: Will use the accelerate library.
Training Abstraction: Will use TrainStep for fine-grained control.

carefree-core is MIT licensed, as found in the LICENSE file.