From 398f9314db564a67def0336af09c09fbc344f1f2 Mon Sep 17 00:00:00 2001 From: Yukuo Cen Date: Wed, 1 Jun 2022 20:03:54 +0800 Subject: [PATCH] [Doc] Prepare v0.5.3 release (#351) --- README.md | 6 ++++-- README_CN.md | 6 ++++-- cogdl/__init__.py | 2 +- docs/source/index.rst | 1 + 4 files changed, 10 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index e1aed065..677526e3 100644 --- a/README.md +++ b/README.md @@ -21,20 +21,22 @@ We summarize the contributions of CogDL as follows: ## ❗ News +- The new **v0.5.3 release** supports mixed-precision training by setting \textit{fp16=True} and provides a basic [example](https://github.com/THUDM/cogdl/blob/master/examples/jittor/gcn.py) written by [Jittor](https://github.com/Jittor/jittor). It also updates the tutorial in the document, fixes downloading links of some datasets, and fixes potential bugs of operators. + - 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/. 🎉 -
News History
+- 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. 🎉 - 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! diff --git a/README_CN.md b/README_CN.md index 2f62ecc4..ca79fef4 100644 --- a/README_CN.md +++ b/README_CN.md @@ -21,20 +21,22 @@ CogDL的特性包括： ## ❗ 最新 +- 最新的 **v0.5.3 release** 支持混合精度(fp16)训练，提供了[Jittor](https://github.com/Jittor/jittor)的初步支持（见[example](https://github.com/THUDM/cogdl/blob/master/examples/jittor/gcn.py)）。这个版本更新了文档中的使用教程，修复了一部分数据集的下载链接，修复了某些算子在不同环境下可能的问题。 + - 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/. 🎉 -
历史
+- 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/). 🎉 - 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! diff --git a/cogdl/__init__.py b/cogdl/__init__.py index 35b09c96..8469a601 100644 --- a/cogdl/__init__.py +++ b/cogdl/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.5.2" +__version__ = "0.5.3" from .experiments import experiment from .pipelines import pipeline diff --git a/docs/source/index.rst b/docs/source/index.rst index 966ee358..de8d5b0e 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -16,6 +16,7 @@ We summarize the contributions of CogDL as follows: ❗ News ------------ +- The new **v0.5.3 release** supports mixed-precision training by setting \textit{fp16=True} and provides a basic [example](https://github.com/THUDM/cogdl/blob/master/examples/jittor/gcn.py) written by [Jittor](https://github.com/Jittor/jittor). It also updates the tutorial in the document, fixes downloading links of some datasets, and fixes potential bugs of operators. - 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.