[Doc] Prepare v0.5 release (#311)

THUDM · Nov 20, 2021 · c9c6dde · c9c6dde
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diff --git a/README.md b/README.md
@@ -21,7 +21,7 @@ We summarize the contributions of CogDL as follows:
 
 ## ❗ News
 
-- The new **v0.5.0-alpha0 pre-release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. 
+- The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. 🎉
 
 - The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. 🎉
 

diff --git a/README_CN.md b/README_CN.md
@@ -21,7 +21,7 @@ CogDL的特性包括：
 
 ## ❗ 最新
 
-- 最新的 **v0.5.0-alpha0 pre-release** 为图神经网络的训练设计了一套统一的流程. 这个版本去除了原先的`Task`类，引入了`DataWrapper`来准备training/validation/test过程中所需的数据，引入了`ModelWrapper`来定义模型training/validation/test的步骤.
+- 最新的 **v0.5.0 release** 为图神经网络的训练设计了一套统一的流程. 这个版本去除了原先的`Task`类，引入了`DataWrapper`来准备training/validation/test过程中所需的数据，引入了`ModelWrapper`来定义模型training/validation/test的步骤. 🎉
 
 - 最新的 **v0.4.1 release** 增加了深层GNN的实现和推荐任务。这个版本同时提供了新的一些pipeline用于直接获取图表示和搭建推荐应用。欢迎大家参加我们在KDD 2021上的tutorial，时间是8月14号上午10:30 - 12:00（北京时间）。 更多的内容可以查看 https://kdd2021graph.github.io/. 🎉
 

diff --git a/cogdl/__init__.py b/cogdl/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "0.5.0-alpha1"
+__version__ = "0.5.0"
 
 from .experiments import experiment
 from .oag import oagbert

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -17,7 +17,7 @@ We summarize the contributions of CogDL as follows:
 ❗ News
 ------------
 
-- The new **v0.5.0-alpha0 pre-release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. 
+- The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. 
 - The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. 🎉
 - The new **v0.4.0 release** refactors the data storage (from ``Data`` to ``Graph``) and provides more fast operators to speed up GNN training. It also includes many self-supervised learning methods on graphs. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see this `link <https://kdd2021graph.github.io/>`_ for more details. 🎉
 - The new **v0.3.0 release** provides a fast spmm operator to speed up GNN training. We also release the first version of `CogDL paper <https://arxiv.org/abs/2103.00959>`_ in arXiv. You can join `our slack <https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg>`_ for discussion. 🎉🎉🎉