Cleanup README.md
CuPy consists of the core multi-dimensional array class, `cupy.ndarray`, and many functions on it.
It supports a subset of `numpy.ndarray` interface.
-## Requirements
-
-CuPy is tested on Ubuntu 14.04 and CentOS 7. We recommend them to use CuPy, though it may run on other systems as well.
-
-Minimum requirements:
-- Python 2.7.6+, 3.4.3+, 3.5.1+, 3.6.0+
-- NumPy 1.9, 1.10, 1.11, 1.12
-- Six 1.9
-
-Requirements for some features:
-- CUDA support
- - CUDA 7.0, 7.5, 8.0
- - g++ 4.8.4+
-- cuDNN support
- - cuDNN v4, v5, v5.1, v6
-- Testing utilities
- - Mock
- - Nose
-
## Installation
-### Minimum installation
-
-If you use old ``setuptools``, upgrade it:
+For detailed instructions on installing CuPy, see [the installation guide](https://docs-cupy.chainer.org/en/stable/install.html).
-```
-pip install -U setuptools
-```
+You can install CuPy using `pip`:
-Then, install CuPy via PyPI:
-```
+```sh
pip install cupy
```
-You can also install CuPy from the source code:
-```
-python setup.py install
-```
-
-
-### Installation with CUDA
-
-If you want to enable CUDA, first you have to install CUDA and set the environment variable `PATH` and `LD_LIBRARY_PATH` for CUDA executables and libraries.
-For example, if you are using Ubuntu and CUDA is installed by the official distribution, then CUDA is installed at `/usr/local/cuda`.
-In this case, you have to add the following lines to `.bashrc` or `.zshrc` (choose which you are using):
-```
-export PATH=/usr/local/cuda/bin:$PATH
-export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
-```
-
-
-If you want to enable cuDNN, add a directory containing `cudnn.h` to `CFLAGS`, and add a directory containing `libcudnn.so` to `LDFLAGS` and `LD_LIBRARY_PATH`:
-```
-export CFLAGS=-I/path/to/cudnn/include
-export LDFLAGS=-L/path/to/cudnn/lib
-export LD_LIBRARY_PATH=/path/to/cudnn/lib:$LD_LIBRARY_PATH
-```
-Do not forget to restart your terminal session (or `source` it) to enable these changes.
-And then, reinstall CuPy.
-
-
-### Multi-GPU Support
-
-Multi-GPU training is supported by MultiprocessParallelUpdater.
-If you want to use MultiprocessParallelUpdater, please install [NCCL](https://github.com/NVIDIA/nccl) by following the installation guide.
-
+Note that if you want to enable CUDA, cuDNN, and/or NCCL, they need to be set up before installation of CuPy.
## Run with Docker
