diff --git a/README.md b/README.md
index be8ca5189a..8b6b363de3 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 ![MindSpore Logo](docs/MindSpore-logo.png "MindSpore logo")
 ============================================================
 
-- [What is MindSpore?](#what-is-MindSpore)
+- [What is MindSpore?](#what-is-mindspore)
     - [Automatic Differentiation](#automatic-differentiation)
     - [Automatic Parallel](#automatic-parallel)
 - [Installation](#installation)
@@ -53,7 +53,7 @@ The goal of MindSpore automatic parallel is to build a training method that comb
 
 <img src="docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
 
-At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is splited into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient.
+At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is splitted into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient.
 
 ## Installation
 
@@ -70,9 +70,9 @@ MindSpore offers build options across multiple backends:
 | GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
 | CPU | Ubuntu-x86 | ✔️ |
 
-For installation using pip, take `Ubuntu-x86` and `CPU` build version as an example:
+For installation using `pip`, take `Ubuntu-x86` and `CPU` build version as an example:
 
-1. Download whl from [MindSpore website](https://www.mindspore.cn/), and install the package.
+1. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
 
     ```
     pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/0.1.0-alpha/MindSpore/cpu/ubuntu-x86/mindspore-0.1.0-cp37-cp37m-linux_x86_64.whl
@@ -96,14 +96,60 @@ currently the containerized build options are supported as follows:
 | Hardware Platform | Docker Image URL |
 | :---------------- | :--------------- |
 | CPU | `mindspore/mindspore-cpu:0.1.0-alpha` |
-| GPU CUDA 9.2 | `mindspore/mindspore-cuda9.2:0.1.0-alpha` |
-| GPU CUDA 10.1 | `mindspore/mindspore-cuda10.1:0.1.0-alpha` |
+| GPU | `mindspore/mindspore-gpu:0.1.0-alpha` |
 | Ascend | <center>—</center> |
 
-Take `CPU` for example, you can directly pull the image using the below command:
-```
-docker pull mindspore/mindspore-cpu:0.1.0-alpha
-```
+* CPU
+
+    For `CPU` backend, you can directly pull and run the image using the below command:
+    ```
+    docker pull mindspore/mindspore-cpu:0.1.0-alpha
+    docker run -it mindspore/mindspore-cpu:0.1.0-alpha python -c 'import mindspore'
+    ```
+
+* GPU
+
+    For `GPU` backend, please make sure the `nvidia-container-toolkit` has been installed in advance, here are some install guidelines for Ubuntu users:
+    ```
+    DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID)
+    curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
+    curl -s -L https://nvidia.github.io/nvidia-docker/$DISTRIBUTION/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list
+
+    sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2
+    sudo systemctl restart docker
+    ```
+
+    Then you can pull and run the image using the below command:
+    ```
+    docker pull mindspore/mindspore-gpu:0.1.0-alpha
+    docker run -it --runtime=nvidia --privileged=true mindspore/mindspore-gpu:0.1.0-alpha /bin/bash
+    ```
+
+    To test if the docker image works, please execute the python code below and check the output:
+    ```python
+    import numpy as np
+    from mindspore import Tensor
+    from mindspore.ops import functional as F
+    import mindspore.context as context
+
+    context.set_context(device_target="GPU")
+    x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
+    y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
+    print(F.tensor_add(x, y))
+    ```
+    ```
+    [[[ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.]],
+
+    [[ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.]],
+
+    [[ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.],
+    [ 2.  2.  2.  2.]]]
+    ```
 
 If anyone wants to learn more about the build process of MindSpore docker images,
 please check out `docker` folder for the details.