# Reading Roadmap

Colossal-AI provides a collection of parallel training components for you. We aim to support you with your development
of distributed deep learning models just like how you write single-GPU deep learning models. ColossalAI provides easy-to-use
APIs to help you kickstart your training process. To better how ColossalAI works, we recommend you to read this documentation
in the following order.

- If you are not familiar with distributed system or have never used Colossal-AI, you should first jump into the `Concepts`
section to get a sense of what we are trying to achieve. This section can provide you with some background knowledge on
distributed training as well.
- Next, you can follow the `basics` tutorials. This section will cover the details about how to use Colossal-AI.
- Afterwards, you can try out the features provided in Colossal-AI by reading `features` section. We will provide a codebase for each tutorial. These tutorials will cover the
basic usage of Colossal-AI to realize simple functions such as data parallel and mixed precision training.
- Lastly, if you wish to apply more complicated techniques such as how to run hybrid parallel on GPT-3,  the
`advanced tutorials` section is the place to go!

**We always welcome suggestions and discussions from the community, and we would be more than willing to help you if you
encounter any issue. You can raise an [issue](https://github.com/hpcaitech/ColossalAI/issues) here or create a discussion
topic in the [forum](https://github.com/hpcaitech/ColossalAI/discussions).**