[Doc] Prepare v0.5.2 release (#322)

Author: cenyk1230

README.md

@@ -20,20 +20,22 @@ We summarize the contributions of CogDL as follows:
 
 ## ❗ News
 
+- The new **v0.5.2 release** adds a GNN example for ogbn-products and updates geom datasets. It also fixes some potential bugs including setting devices, using cpu for inference, etc.
+
 - The new **v0.5.1 release** adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification which can be found in [this link](./cogdl/datasets/rd2cd_data.py). 🎉
 
 - 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. 🎉
-
 <details>
 <summary>
 News History
 </summary>
 <br/>
 
+- 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. 🎉
+
 - CogDL supports GNN models with Mixture of Experts (MoE). You can install [FastMoE](https://github.com/laekov/fastmoe) and try **[MoE GCN](./cogdl/models/nn/moe_gcn.py)** in CogDL now!
 
 - 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. 🎉🎉🎉

README_CN.md

@@ -20,20 +20,22 @@ CogDL的特性包括：
 
 ## ❗ 最新
 
+- 最新的 **v0.5.2 release** 给ogbn-products数据集添加了GNN样例，更新了geom数据集。这个版本同时修复了一些潜在的问题，包括设置不同device，使用cpu进行预测等。
+
 - 最新的 **v0.5.1 release** 添加了一些高效的算子，包括cpu版本的SpMM和cuda版本的scatter_max。这个版本同时增加了很多用于节点分类的[数据集](./cogdl/datasets/rd2cd_data.py)。 🎉
 
 - 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/. 🎉
 
-- 最新的 **v0.4.0版本** 重构了底层的数据存储（从`Data`类变为`Graph`类），并且提供了更多快速的算子来加速图神经网络的训练。这个版本还包含了很多图自监督学习的算法。同时，我们很高兴地宣布我们将在8月份的KDD 2021会议上给一个CogDL相关的tutorial。具体信息请参见[这个链接](https://kdd2021graph.github.io/). 🎉
-
 <details>
 <summary>
 历史
 </summary>
 <br/>
 
+- 最新的 **v0.4.0版本** 重构了底层的数据存储（从`Data`类变为`Graph`类），并且提供了更多快速的算子来加速图神经网络的训练。这个版本还包含了很多图自监督学习的算法。同时，我们很高兴地宣布我们将在8月份的KDD 2021会议上给一个CogDL相关的tutorial。具体信息请参见[这个链接](https://kdd2021graph.github.io/). 🎉
+
 - CogDL支持图神经网络模型使用混合专家模块（Mixture of Experts, MoE）。 你可以安装[FastMoE](https://github.com/laekov/fastmoe)然后在CogDL中尝试 **[MoE GCN](./cogdl/models/nn/moe_gcn.py)** 模型!
 
 - 最新的 **v0.3.0版本** 提供了快速的稀疏矩阵乘操作来加速图神经网络模型的训练。我们在arXiv上发布了 **[CogDL paper](https://arxiv.org/abs/2103.00959)** 的初版. 你可以加入[我们的slack](https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg)来讨论CogDL相关的内容。🎉

cogdl/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.5.1.post1"
+__version__ = "0.5.2"
 
 from .experiments import experiment
 from .pipelines import pipeline

docs/source/index.rst

@@ -9,14 +9,14 @@ CogDL is a graph representation learning toolkit that allows researchers and dev
 
 We summarize the contributions of CogDL as follows:
 
-- **High Efficiency**: CogDL utilizes well-optimized operators to speed up training and save GPU memory of GNN models.
-- **Easy-to-Use**: CogDL provides easy-to-use APIs for running experiments with the given models and datasets using hyper-parameter search.
+- **Efficiency**: CogDL utilizes well-optimized operators to speed up training and save GPU memory of GNN models.
+- **Ease of Use**: CogDL provides easy-to-use APIs for running experiments with the given models and datasets using hyper-parameter search.
 - **Extensibility**: The design of CogDL makes it easy to apply GNN models to new scenarios based on our framework.
-- **Reproducibility**: CogDL provides reproducible leaderboards for state-of-the-art models on most of important tasks in the graph domain.
 
 ❗ News
 ------------
 
+- The new **v0.5.2 release** adds a GNN example for ogbn-products and updates geom datasets. It also fixes some potential bugs including setting devices, using cpu for inference, etc.
 - The new **v0.5.1 release** adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification. 🎉
 - 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/. 🎉
@@ -34,7 +34,7 @@ Please cite `our paper <https://arxiv.org/abs/2103.00959>`_ if you find our code
 ::
 
    @article{cen2021cogdl,
-      title={CogDL: An Extensive Toolkit for Deep Learning on Graphs},
+      title={CogDL: Toolkit for Deep Learning on Graphs},
       author={Yukuo Cen and Zhenyu Hou and Yan Wang and Qibin Chen and Yizhen Luo and Xingcheng Yao and Aohan Zeng and Shiguang Guo and Peng Zhang and Guohao Dai and Yu Wang and Chang Zhou and Hongxia Yang and Jie Tang},
       journal={arXiv preprint arXiv:2103.00959},
       year={2021}