[doc] migrate the markdown files (#2652)

docs/source/en/get_started/installation.md

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+# Setup
+
+## Download From PyPI
+
+You can install Colossal-AI with
+
+```shell
+pip install colossalai
+```
+
+If you want to build PyTorch extensions during installation, you can use the command below. Otherwise, the PyTorch extensions will be built during runtime.
+
+```shell
+CUDA_EXT=1 pip install colossalai
+```
+
+
+## Download From Source
+
+> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :)
+
+```shell
+git clone https://github.com/hpcaitech/ColossalAI.git
+cd ColossalAI
+
+# install dependency
+pip install -r requirements/requirements.txt
+
+# install colossalai
+pip install .
+```
+
+If you don't want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):
+
+```shell
+CUDA_EXT=1 pip install .
+```

docs/source/en/get_started/reading_roadmap.md

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+# 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).**

docs/source/en/get_started/run_demo.md

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+# Quick Demo
+
+Colossal-AI is an integrated large-scale deep learning system with efficient parallelization techniques. The system can
+accelerate model training on distributed systems with multiple GPUs by applying parallelization techniques. The system
+can also run on systems with only one GPU. Quick demos showing how to use Colossal-AI are given below.
+
+## Single GPU
+
+Colossal-AI can be used to train deep learning models on systems with only one GPU and achieve baseline
+performances. We provided an example to [train ResNet on CIFAR10 dataset](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/resnet)
+with only one GPU. You can find the example in [ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI-Examples).
+Detailed instructions can be found in its `README.md`.
+
+## Multiple GPUs
+
+Colossal-AI can be used to train deep learning models on distributed systems with multiple GPUs and accelerate the
+training process drastically by applying efficient parallelization techniques. When we have several parallelism for you
+to try out.
+
+#### 1. data parallel
+
+You can use the same [ResNet example](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/resnet) as the
+single-GPU demo above. By setting `--nproc_per_node` to be the number of GPUs you have on your machine, the example
+is turned into a data parallel example.
+
+#### 2. hybrid parallel
+
+Hybrid parallel includes data, tensor, and pipeline parallelism. In Colossal-AI, we support different types of tensor
+parallelism (i.e. 1D, 2D, 2.5D and 3D). You can switch between different tensor parallelism by simply changing the configuration
+in the `config.py`. You can follow the [GPT example](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/gpt).
+Detailed instructions can be found in its `README.md`.
+
+#### 3. MoE parallel
+
+We provided [an example of WideNet](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/image/widenet) to demonstrate
+MoE parallelism. WideNet uses mixture of experts (MoE) to achieve better performance. More details can be found in
+[Tutorial: Integrate Mixture-of-Experts Into Your Model](../advanced_tutorials/integrate_mixture_of_experts_into_your_model.md)
+
+#### 4. sequence parallel
+
+Sequence parallel is designed to tackle memory efficiency and sequence length limit problems in NLP tasks. We provided
+[an example of BERT](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/bert/sequene_parallel) in
+[ColossalAI-Examples](https://github.com/hpcaitech/ColossalAI-Examples). You can follow the `README.md` to execute the code.