diff --git a/README.md b/README.md
index 1a4518d5a..5780cfed7 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@
 
 FederatedScope is a comprehensive federated learning platform that provides convenient usage and flexible customization for various federated learning tasks in both academia and industry.  Based on an event-driven architecture, FederatedScope integrates rich collections of functionalities to satisfy the burgeoning demands from federated learning, and aims to build up an easy-to-use platform for promoting learning safely and effectively.
 
-A detailed tutorial is provided on our [website](https://federatedscope.io/).
+A detailed tutorial is provided on our [website](https://federatedscope.io/), and you can try FederatedScope in [FederatedScope Playground](https://try.federatedscope.io/) or [Google Colab](https://colab.research.google.com/github/alibaba/FederatedScope).
 
 ## News
 - [07-30-2022] We release FederatedScope v0.2.0! 
diff --git a/materials/notebook/(CN)CIKMCUP2022.ipynb b/materials/notebook/(CN)CIKMCUP2022.ipynb
new file mode 100644
index 000000000..b58256812
--- /dev/null
+++ b/materials/notebook/(CN)CIKMCUP2022.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step-by-step Guidance for CIKM 2022 AnalytiCup Competition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 1. 安装FederatedScope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:22.036523Z",
+     "start_time": "2022-07-26T09:23:21.642646Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 拷贝FederatedScope并切换到比赛所使用的分支cikm22competition\n",
+    "\n",
+    "!cp -r fs_latest FederatedScope\n",
+    "\n",
+    "# 如果不在playground中请输入命令`cd FederatedScope`\n",
+    "import os\n",
+    "os.chdir('FederatedScope')\n",
+    "\n",
+    "!git checkout cikm22competition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2. 搭建运行环境"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:28.796456Z",
+     "start_time": "2022-07-26T09:23:22.038843Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 安装 FS\n",
+    "!pip install -e . --user"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3. 下载比赛数据集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:29.189922Z",
+     "start_time": "2022-07-26T09:23:28.798365Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 比赛数据集已经提前下载到`data`目录下，通过如下的命令进行解压:\n",
+    "!mkdir -p data\n",
+    "!cp -r ../data/CIKM22Competition ./data/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "现在比赛数据集已经被放置在`FederatedScope/data/CIKM22Competition`目录下，遵循`CIKM22Competition/${client_id}`的方式进行组织，其中`${client_id}`是client的序号并从1开始计数。每个client的目录下包含训练（train.pt），测试（test.pt）和验证（val.pt）三个部分的数据。可以通过`torch.load`的方式来查看数据："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:29.823058Z",
+     "start_time": "2022-07-26T09:23:29.192273Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "# 加载序号为1的client的训练数据\n",
+    "train_data_client1 = torch.load('./data/CIKM22Competition/1/train.pt')\n",
+    "# 查看训练数据中的第一个训练样例\n",
+    "print(train_data_client1[0])\n",
+    "# 查看训练样例的label\n",
+    "print(train_data_client1[0].y)\n",
+    "# 查看训练样例的序号，即${sample_id}\n",
+    "print(train_data_client1[0].data_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4. 在比赛数据上运行baseline算法"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "FederatedScope为比赛数据内置了两个baseline算法：\"isolated training\"和\"FedAvg\"。假设你已经成功的搭建好FederatedScope的运行环境，并下载了比赛数据，那么可以通过以下的命令运行两个baseline算法"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:29.824268Z",
+     "start_time": "2022-07-26T09:23:29.824254Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# 作为示例，我们在这里仅运行3轮，用户可以按照自己的需求更改运行轮数\n",
+    "# 按照如下命令运行isolated training\n",
+    "!python federatedscope/main.py --cfg federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup.yaml --client_cfg federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup_per_client.yaml federate.total_round_num 3\n",
+    "\n",
+    "# 按照如下命令运行FedAvg\n",
+    "!python federatedscope/main.py --cfg federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup.yaml --client_cfg federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup_per_client.yaml federate.total_round_num 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "其中参数--cfg xxxx.yaml指定了全局的参数，--client_cfg xxx.yaml为每一个client单独指定了参数"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5. 保存并提交预测结果"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 提交格式\n",
+    "如CIKM 2022 AnalytiCup Competition介绍中所述，参赛者需要将所有client的预测结果保存到一个csv文件中进行提交。在这个csv文件中，每一行代表一个测试样例的预测结果，并且由`${client_id}`和`${sample_id}`标识所预测的测试样例。其中，`${client_id}`从1开始计数，`${sample_id}`需要与比赛数据中的测试样例的序号保持一致（可以通过测试样例的`data_index`属性获得测试样例的序号）。\n",
+    "\n",
+    "需要注意的是，分类任务和多维度回归任务分别遵循以下不同的格式：\n",
+    "* 对分类任务，每一行应当遵循如下的格式(`${category_id}`从0开始计数)：\n",
+    "\n",
+    "```\n",
+    "${client_id},${sample_id},${category_id}\n",
+    "```\n",
+    "\n",
+    "* 对于N维的回归任务，每一行应当遵循如下的格式：\n",
+    "```\n",
+    "${client_id},${sample_id},${prediction_1st_dimension},…,${prediction_N-th_dimension}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 通过FederatedScope保存预测结果\n",
+    "FederatedScope中的\"cikm22competition\"分支目前支持在训练结束时保存预测结果（相关代码见`federatedscope/gfl/trainer/graphtrainer.py`和`federatedscope/core/trainers/torch_trainer.py`）。\n",
+    "预测结果将会被保存在一个名为prediction.csv的文件中，该文件位于`outdir`参数所指定的目录中。每次运行时，如果`outdir`所指定的目录已经存在，则会使用一个时间戳后缀用以区分。\n",
+    "在训练结束时，FederatedScope的训练记录也将标识出预测结果所在的目录，如下所示\n",
+    "![Finish saving prediction results](https://img.alicdn.com/imgextra/i1/O1CN01L68gve1nGu3BNp5vL_!!6000000005063-2-tps-4766-404.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 提交预测结果\n",
+    "最后，参赛选手可以将预测结果下载到本地，并上传到[天池](https://tianchi.aliyun.com/competition/entrance/532008/introduction)。"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.2 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
+
diff --git a/materials/notebook/(EN)CIKMCUP2022.ipynb b/materials/notebook/(EN)CIKMCUP2022.ipynb
new file mode 100644
index 000000000..0fa04913c
--- /dev/null
+++ b/materials/notebook/(EN)CIKMCUP2022.ipynb
@@ -0,0 +1,227 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Step-by-step Guidance for CIKM 2022 AnalytiCup Competition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step1. Install FederatedScope"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:22.036523Z",
+     "start_time": "2022-07-26T09:23:21.642646Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Copy FederatedScope and switch to the stable branch cikm22competitionfor competition\n",
+    "\n",
+    "!cp -r fs_latest FederatedScope\n",
+    "\n",
+    "# `cd FederatedScope` if you are NOT in the playground.\n",
+    "import os\n",
+    "os.chdir('FederatedScope')\n",
+    "\n",
+    "!git checkout cikm22competition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 2. Setup the running environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:28.796456Z",
+     "start_time": "2022-07-26T09:23:22.038843Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Install FS as follows\n",
+    "!pip install -e . --user"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 3. Download contest data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:29.189922Z",
+     "start_time": "2022-07-26T09:23:28.798365Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# We have downloaded the data in advance in `data`, unzip the contest data as follows:\n",
+    "!mkdir -p data\n",
+    "!cp -r ../data/CIKM22Competition ./data/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then you can access the contest data with the directory FederatedScope/data/CIKM22Competition. The contest data is organized by the index of the client CIKM22Competition/${client_id}(counts from 1), and the data of each client contains the train, test and validate splitted parts. You can load it by torch.loadas follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:29.823058Z",
+     "start_time": "2022-07-26T09:23:29.192273Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "# The train split of client 1\n",
+    "train_data_client1 = torch.load('./data/CIKM22Competition/1/train.pt')\n",
+    "# Check the first sample\n",
+    "print(train_data_client1[0])\n",
+    "# Check the label of the first sample\n",
+    "print(train_data_client1[0].y)\n",
+    "# Check the index of the first sample as ${sample_id}\n",
+    "print(train_data_client1[0].data_index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 4. Execute baselines on the contest data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Within FederatedScope, we build in two baselines for the contest data, \"isolated training\" and \"FedAvg\". Suppose you have successfully built the running environment, and downloaded the contest data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-07-26T09:23:29.824268Z",
+     "start_time": "2022-07-26T09:23:29.824254Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# We only run 3 round in playground, you can modify it by yourself.\n",
+    "# Run the following command to execute the isolated training\n",
+    "!python federatedscope/main.py --cfg federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup.yaml --client_cfg federatedscope/gfl/baseline/isolated_gin_minibatch_on_cikmcup_per_client.yaml federate.total_round_num 3\n",
+    "\n",
+    "# Run the following command to execute the FedAvg solution\n",
+    "!python federatedscope/main.py --cfg federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup.yaml --client_cfg federatedscope/gfl/baseline/fedavg_gin_minibatch_on_cikmcup_per_client.yaml federate.total_round_num 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "where the argument--cfg xxxx.yaml specifies the global configuration, and --client_cfg xxx.yaml specifies the client-wise hyper parameters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Step 5. Save and submit the prediction results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submission format\n",
+    "As stated in the introduction of CIKM 2022 AnalytiCup Competition, participants are required to submit the prediction results for all clients within one csv file. Within the file, each line records one prediction and is identified by `${client_id}` and `${sample_id}`. The `${client_id}` counts from 1 and `${sample_id}` should be consistent with the contest data (You can access it by the attribute data_index).\n",
+    "\n",
+    "The classification and multi-dimensional regression tasks follow different formats as follows\n",
+    "* For classification tasks, each line follows (`${category_id}` counts from 0)\n",
+    "\n",
+    "```\n",
+    "${client_id},${sample_id},${category_id}\n",
+    "```\n",
+    "\n",
+    "For N-dimensional regression task, each line follows\n",
+    "```\n",
+    "${client_id},${sample_id},${prediction_1st_dimension},…,${prediction_N-th_dimension}\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Saving prediction results\n",
+    "By FederatedScope\n",
+    "The \"cikm22competition\" branch in FederatedScope supports to save prediction results at the end of training. You can refer to code in federatedscope/gfl/trainer/graphtrainer.py and federatedscope/core/trainers/torch_trainer.py.\n",
+    "The prediction results will be saved in a csv file named prediction.csv. For the convenience of users to conduct multiple experiments (e.g., for HPO), prediction.csv of each experimental run with a spcific configuration will be placed in this experiment's output directory (specified by outdir), which will be automatically appended with a suffix of timestamp if the specified directory has been there.\n",
+    "The training log will report the directory of the prediction results. Taking FedAvg as an example, at the end of training FederatedScope will report the path of prediction results as follows.\n",
+    "![Finish saving prediction results](https://img.alicdn.com/imgextra/i1/O1CN01L68gve1nGu3BNp5vL_!!6000000005063-2-tps-4766-404.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit prediction results\n",
+    "Finally, you can submit your prediction results and get your score in [Tianchi](https://tianchi.aliyun.com/competition/entrance/532008/introduction)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.2 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/materials/notebook/01_quick_start.ipynb b/materials/notebook/01_quick_start.ipynb
index 1d60bacf0..7cb91fcee 100644
--- a/materials/notebook/01_quick_start.ipynb
+++ b/materials/notebook/01_quick_start.ipynb
@@ -23,11 +23,7 @@
    },
    "source": [
     "## Configurations\n",
-    "**FederatedScope** organize the configuration through
-    `federatedscope.core.configs.config.CN` and can be found in
-    `federatedscope.configs`. Please refer to the
-    [official documentation](https://federatedscope.io/refs/index) for specific
-     instructions on how to configure `cfg`."
+    "**FederatedScope** organize the configuration through `yacs.config.cfgNode` and can be found in `federatedscope.configs`. Please refer to the [official documentation](https://federatedscope.io/refs/index) for specific instructions on how to configure `cfg`."
    ]
   },
   {
@@ -287,4 +283,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/materials/notebook/02_start_your_own_case.ipynb b/materials/notebook/02_start_your_own_case.ipynb
index 9a1c11d36..8235be3ab 100644
--- a/materials/notebook/02_start_your_own_case.ipynb
+++ b/materials/notebook/02_start_your_own_case.ipynb
@@ -1031,4 +1031,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/materials/notebook/03_personalized_FL.ipynb b/materials/notebook/03_personalized_FL.ipynb
index 438e1e1d0..9502a96c0 100644
--- a/materials/notebook/03_personalized_FL.ipynb
+++ b/materials/notebook/03_personalized_FL.ipynb
@@ -49,9 +49,7 @@
     "For more larger datasets, please refer examples in `scripts/personalization_exp_scripts`.\n",
     "\n",
     "### Configuration\n",
-    "**FederatedScope** organizes the configuration through an extension of
-    `federatedscope.core.configs.config.CN`. Please refer to our [official
-    documentation](https://federatedscope.io/docs/own-case/) for specific instructions on how to configure the initial `global_cfg` and customize your own configuration. By default, we provide built-in personalization-related configurations in `federatedscope/core/configs/`."
+    "**FederatedScope** organizes the configuration through an extension of `yacs.config.cfgNode`. Please refer to our [official documentation](https://federatedscope.io/docs/own-case/) for specific instructions on how to configure the initial `global_cfg` and customize your own configuration. By default, we provide built-in personalization-related configurations in `federatedscope/core/configs/`."
    ]
   },
   {
@@ -371,4 +369,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/materials/notebook/Welcome.ipynb b/materials/notebook/Welcome.ipynb
index 23e7a6520..a0679af90 100644
--- a/materials/notebook/Welcome.ipynb
+++ b/materials/notebook/Welcome.ipynb
@@ -61,13 +61,14 @@
     "# Installation is REQUIRED in the playground.\n",
     "# You should execute the following code to install the stable version of FederatedScope.\n",
     "\n",
-    "!git clone -b playground/v0.0.2 https://github.com/alibaba/FederatedScope.git\n",
+    "# `git clone https://github.com/alibaba/FederatedScope.git` if you are NOT in the playground.\n",
+    "!cp -r fs_latest FederatedScope\n",
     "\n",
     "# `cd FederatedScope` if you are NOT in the playground.\n",
     "import os\n",
     "os.chdir('FederatedScope')\n",
     "\n",
-    "!python setup.py install"
+    "!pip install -e . --user"
    ]
   },
   {
@@ -150,4 +151,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
\ No newline at end of file
+}