Add Chinese docs for GraphScope (#67)

Co-authored-by: DongZe Li <[email protected]> Co-authored-by: Tao He <[email protected]> Co-authored-by: dongze.ldz <[email protected]> Co-authored-by: qiaozi.zwb <[email protected]> Co-authored-by: siyuanzhang.zsy <[email protected]> Co-authored-by: Siyuan Zhang <[email protected]>
alibaba · Jan 7, 2021 · 53636b2 · 53636b2
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diff --git a/README-zh.md b/README-zh.md
diff --git a/README.md b/README.md
@@ -8,6 +8,7 @@
 [![GraphScope CI](https://github.com/alibaba/GraphScope/workflows/GraphScope%20CI/badge.svg)](https://github.com/alibaba/GraphScope/actions?workflow=GraphScope+CI)
 [![Docs](https://github.com/alibaba/GraphScope/workflows/Docs/badge.svg)](https://graphscope.io/docs)
 [![Coverage](https://codecov.io/gh/alibaba/GraphScope/branch/main/graph/badge.svg)](https://codecov.io/gh/alibaba/GraphScope)
+[![Translation](https://img.shields.io/badge/Translation-%E4%B8%AD%E6%96%87%E7%89%88-success)](README-zh.md)
 
 GraphScope is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster of computers through a user-friendly Python interface. GraphScope makes multi-staged processing of large-scale graph data on compute clusters simple by combining several important pieces of Alibaba technology: including [GRAPE](https://github.com/alibaba/libgrape-lite), [MaxGraph](interactive_engine/), and [Graph-Learn](https://github.com/alibaba/graph-learn) (GL) for analytics, interactive, and graph neural networks (GNN) computation, respectively, and the [vineyard](https://github.com/alibaba/libvineyard) store that offers efficient in-memory data transfers.
 
@@ -31,7 +32,6 @@ For Linux distributions, we provide a script to install the above dependencies a
 ```bash
 # run the environment preparing script.
 ./scripts/prepare_env.sh
-
 ```
 
 ### Installation
@@ -51,19 +51,19 @@ Please note that we have not hardened this release for production use and it lac
 
 [`ogbn-mag`](https://ogb.stanford.edu/docs/nodeprop/#ogbn-mag) is a heterogeneous network composed of a subset of the Microsoft Academic Graph. It contains 4 types of entities(i.e., papers, authors, institutions, and fields of study), as well as four types of directed relations connecting two entities.
 
-Given the heterogeneous ogbn-mag data, the task is to predict the class of each paper. Node classification can identify papers in multiple venues, which represent different groups of scientific work on different topics. We apply both the attribute and structural information to classify papers. In the graph, each paper node contains a 128-dimensional word2vec vector representing its content, which is obtained by averaging the embeddings of words in its title and abstract. The embeddings of individual words are pre-trained. The structural information is computed on-the-fly.
+Given the heterogeneous `ogbn-mag` data, the task is to predict the class of each paper. Node classification can identify papers in multiple venues, which represent different groups of scientific work on different topics. We apply both the attribute and structural information to classify papers. In the graph, each paper node contains a 128-dimensional word2vec vector representing its content, which is obtained by averaging the embeddings of words in its title and abstract. The embeddings of individual words are pre-trained. The structural information is computed on-the-fly.
 
 <div align="center">
     <img src="https://graphscope.io/docs/_images/how-it-works.png" width="600" alt="how-it-works" />
 </div>
 
-The figure shows the flow of execution when a client Python program is executed..
+The figure shows the flow of execution when a client Python program is executed.
 
 - *Step 1*. Create a session or workspace in GraphScope.
 - *Step 2*. Define schema and load the graph.
 - *Step 3*. Query graph data.
 - *Step 4*. Run graph algorithms.
-- *Step 5*. Run graph-based machine learing tasks.
+- *Step 5*. Run graph-based machine learning tasks.
 - *Step 6*. Close the session.
 
 ### Creating a session
@@ -97,7 +97,7 @@ Taking `ogbn-mag` as example, the figure below shows the model of the property g
     <img src="https://graphscope.io/docs/_images/sample_pg.png" width="600" alt="sample-of-property-graph" />
 </div>
 
-This graph has fours kinds of vertices, labeled as `paper`, `author`, `institution` and `field_of_study`. There are four kinds of edges connecting them, each kind of edges has a label and specifies the vertex labels for its two ends. For example, `cites` edges connect two vertices labeled `paper`. Another example is `writes`, it requires the source vertex is labeled `author` and the destination is a `paper` vertex. All the vertices and edges may have properties. e.g., `paper`  vertices have properties like features, publish year, subject label, etc.
+This graph has four kinds of vertices, labeled as `paper`, `author`, `institution` and `field_of_study`. There are four kinds of edges connecting them, each kind of edges has a label and specifies the vertex labels for its two ends. For example, `cites` edges connect two vertices labeled `paper`. Another example is `writes`, it requires the source vertex is labeled `author` and the destination is a `paper` vertex. All the vertices and edges may have properties. e.g., `paper`  vertices have properties like features, publish year, subject label, etc.
 
 To load this graph to GraphScope, one may use the code below with the [data files](https://graphscope.oss-accelerate.aliyuncs.com/ogbn_mag_small.tar.gz). Please download and extract it to the mounted dir on local(in this case, `~/test_data`).
 

diff --git a/analytical_engine/README-zh.md b/analytical_engine/README-zh.md
@@ -0,0 +1,18 @@
+# GraphScope 图分析引擎 - GRAPE
+
+[![Translation](https://img.shields.io/badge/Translation-English-success)](https://github.com/alibaba/GraphScope/tree/main/analytical_engine)
+
+
+GraphScope 中的图分析引擎继承自 **GRAPE**，该系统实现了论文 [Parallelizing Sequential Graph Computations](https://dl.acm.org/doi/10.1145/3282488) 中提出的不动点计算模型。
+
+与现有系统不同，GRAPE 通过自动并行化整体的单机顺序图算法，[即插即用](https://github.com/alibaba/libgrape-lite/blob/master/examples/analytical_apps/sssp/sssp_auto.h)已有的图算法程序，使其很容易的运行在分布式环境，高效处理大规模图。除了易于编程之外，**GRAPE** 还被设计为[高效](https://github.com/alibaba/libgrape-lite/blob/master/Performance.md)和[高度可拓展](https://github.com/alibaba/libgrape-lite/blob/master/examples/gnn_sampler)的，以应对现实图应用程序多变的规模，多样性和复杂性。
+
+GRAPE 的核心轻量版本以 [libgrape-lite](https://github.com/alibaba/libgrape-lite/) 开源。GraphScope 中的分析引擎扩展了 libgrape-lite 的功能，支持了可变子图，[vineyard](https://github.com/alibaba/libvineyard/) 支持以及引擎的服务模式等。
+
+## 论文列表
+
+- Wenfei Fan, Jingbo Xu, Wenyuan Yu, Jingren Zhou, Xiaojian Luo, Ping Lu, Qiang Yin, Yang Cao, and Ruiqi Xu. [Parallelizing Sequential Graph Computations](https://dl.acm.org/doi/10.1145/3282488). ACM Transactions on Database Systems (TODS) 43(4): 18:1-18:39.
+
+- Wenfei Fan, Jingbo Xu, Yinghui Wu, Wenyuan Yu, Jiaxin Jiang. [GRAPE: Parallelizing Sequential Graph Computations](http://www.vldb.org/pvldb/vol10/p1889-fan.pdf). The 43rd International Conference on Very Large Data Bases (VLDB), demo, 2017 (the Best Demo Award).
+
+- Wenfei Fan, Jingbo Xu, Yinghui Wu, Wenyuan Yu, Jiaxin Jiang, Zeyu Zheng, Bohan Zhang, Yang Cao, and Chao Tian. [Parallelizing Sequential Graph Computations](https://dl.acm.org/doi/10.1145/3035918.3035942). ACM SIG Conference on Management of Data (SIGMOD), 2017 (the Best Paper Award).
diff --git a/analytical_engine/README.md b/analytical_engine/README.md
@@ -1,5 +1,7 @@
 # GraphScope Analytical Engine - GRAPE
 
+[![Translation](https://img.shields.io/badge/translation-%E4%B8%AD%E6%96%87%E7%89%88-success)](README-zh.md)
+
 The analytical engine in GraphScope originated from **GRAPE**, a system that implemented the fix-point model proposed in the paper [Parallelizing Sequential Graph Computations](https://dl.acm.org/doi/10.1145/3282488). 
 
 GRAPE differs from prior systems in its ability to parallelize sequential graph algorithms as a whole by following the PIE programming model from the paper. Sequential algorithms can be easily ["plugged into"](https://github.com/alibaba/libgrape-lite/blob/master/examples/analytical_apps/sssp/sssp_auto.h) **GRAPE** with only minor changes and get parallelized to handle large graphs efficiently. In addition to the ease of programming, **GRAPE** is designed to be [highly efficient](https://github.com/alibaba/libgrape-lite/blob/master/Performance.md) and [flexible](https://github.com/alibaba/libgrape-lite/blob/master/examples/gnn_sampler), to cope the scale, variety and complexity from real-life graph applications.

diff --git a/docs/_templates/layout.html b/docs/_templates/layout.html
@@ -41,7 +41,7 @@
 {% block footer %}
   {{ footer }}
 
-  <div class="rst-versions" data-toggle="rst-versions" role="note" aria-label="versions">
+  <div class="rst-versions shift-up" data-toggle="rst-versions" role="note" aria-label="versions">
     <span class="rst-current-version" data-toggle="rst-current-version">
       <span class="fa fa-book"> Read the Docs</span>
       v: latest

diff --git a/docs/analytics_engine.rst b/docs/analytics_engine.rst
@@ -21,7 +21,7 @@ analysis, community detection, centrality computations.
 
 Built-in algorithms can be easily invoked over loaded graphs. For example, 
 
-.. code:: ipython
+.. code:: python
 
     from graphscope import pagerank
     from graphscope import lpa
@@ -68,7 +68,7 @@ Users may want to fetch the results to the client, or write to cloud or distribu
 
 There is a list of supported method to retrieve the results.
 
-.. code:: iPython
+.. code:: python
 
     # fetch to data structures
     result_pr.to_numpy()
@@ -89,7 +89,7 @@ There is a list of supported method to retrieve the results.
 In addition, as shown in the Getting_Started, computation results can add back to 
 the graph as a new property (column) of the vertices(edges).
 
-.. code:: iPython
+.. code:: python
 
     simple_g = sub_graph.project_to_simple(vlabel="paper", elabel="cites")
     ret = graphscope.kcore(simple_g, k=5)
@@ -104,7 +104,7 @@ also be a part of the result, e.g., the vertex id. We reserve three keywords for
 `r` represents the result, `v` and `e` for vertices and edges, respectively.
 Here are some examples for selectors on result processing.
 
-.. code:: iPython
+.. code:: python
 
     # get the results on the vertex
     result_pr.to_numpy('r')
@@ -141,7 +141,7 @@ programming model in a pure Python mode.
 
 To implement this, a user just need to fulfill this class.
 
-.. code:: ipython
+.. code:: python
 
     @graphscope.analytical.udf.pie
     class YourAlgorithm(AppAssets):
@@ -166,7 +166,7 @@ can be found in :ref:`Cython SDK API`.
 
 Let's take SSSP as example, a user defined SSSP in PIE model may be like this.
 
-.. code:: ipython
+.. code:: python
 
     @graphscope.analytical.udf.pie
     class SSSP:
@@ -238,7 +238,7 @@ In addition to the sub-graph based PIE model,
 `Pregel` model as well. 
 You may develop an algorithms in `Pregel` model by implementing this.
 
-.. code:: ipython
+.. code:: python
 
     @pregel(vd_type='double', md_type='double')
     class YourPregelAlgorithm(AppAssets):
@@ -259,7 +259,7 @@ Differ from the PIE model, the decorator for this class is @graphscope.analytica
 And the functions to be implemented is defined on vertex, rather than the fragment.
 Take SSSP as example, the algorithm in Pregel model looks like this.
 
-.. code:: ipython
+.. code:: python
 
     # decorator, and assign the types for vertex data, message data.
     @pregel(vd_type='double', md_type='double')
@@ -299,7 +299,7 @@ Run Your Own Algorithms
 
 To run your own algorithms, you may trigger it in place where you defined it.
 
-.. code:: ipython
+.. code:: python
 
     import graphscope
 
@@ -315,14 +315,14 @@ To run your own algorithms, you may trigger it in place where you defined it.
 
 After developing and testing, you may want to save it for the future use.
 
-.. code:: ipython
+.. code:: python
 
     SSSP_Pregel.to_gar("file:///var/graphscope/udf/my_sssp_pregel.gar")
 
 
 Later, you can load your own algorithm from the gar package.
 
-.. code:: ipython
+.. code:: python
 
     import graphscope
 

diff --git a/docs/deployment.rst b/docs/deployment.rst
@@ -10,7 +10,7 @@ A session encapsulates the control and state of the GraphScope engines.
 It serves as the entrance in the python client to GraphScope. A session
 allows users to deploy and connect GraphScope on a k8s cluster.
 
-.. code:: ipython
+.. code:: python
 
     import graphscope
 

diff --git a/docs/developer_guide.rst b/docs/developer_guide.rst
@@ -43,7 +43,7 @@ You may want to re-install the python client on local.
 
 To test the newly built binaries, manually open a session and assigned your image:
 
-.. code:: ipython
+.. code:: python
 
     import graphscope
     

diff --git a/docs/getting_started.rst b/docs/getting_started.rst
@@ -51,7 +51,7 @@ Launching Session
 
 To use GraphScope, we need to establish a :ref:`Session` in a python interpreter.
 
-.. code:: ipython
+.. code:: python
 
     import graphscope
     sess = graphscope.session()
@@ -88,7 +88,7 @@ Paper vertices have properties like features, publish year, subject label, etc.
 
 To load this graph to GraphScope, one may use the code below.
 
-.. code:: ipython
+.. code:: python
 
     g = sess.load_from(
         vertices={
@@ -144,7 +144,7 @@ In this example, we use graph queries to find citation counts
 for a particular author, and to derive a subgraph by
 extracting publications in specific time out of the entire graph.
 
-.. code:: ipython
+.. code:: python
 
     # get the entrypoint for submitting Gremlin queries on graph g.
     interactive = sess.gremlin(g)
@@ -173,7 +173,7 @@ you may want to project the property graph to a simple graph at first.
 Continue our example, we run k-core decomposition and triangle counting
 to generate the structural features of each paper node.
 
-.. code:: ipython
+.. code:: python
 
     # exact a subgraph of publication within a time range
     sub_graph = interactive.subgraph("g.V().has('year', inside(2014, 2020)).outE('cites')")
@@ -207,7 +207,7 @@ each of which represents a venue (e.g. pre-print and conference).
 To achieve this, first we launch a learning engine and build
 a graph with features following the last step.
 
-.. code:: ipython
+.. code:: python
 
     # define the features for learning
     paper_features = []
@@ -227,7 +227,7 @@ a graph with features following the last step.
 
 Then we define the training and testing process, and run it.
 
-.. code:: ipython
+.. code:: python
 
     from graphscope.learning.examples import GCN
     from graphscope.learning.graphlearn.python.model.tf.trainer import LocalTFTrainer
@@ -287,7 +287,7 @@ Closing Session
 
 At last, we close the session after processing all graph tasks.
 
-.. code:: ipython
+.. code:: python
 
     sess.close()
 

diff --git a/docs/index.rst b/docs/index.rst
@@ -36,14 +36,16 @@ and the vineyard store that offers efficient in-memory data transfers.
    reference/python_index
    reference/analytical_engine_index
 
+.. toctree::
+   :maxdepth: 2
+   :hidden:
+
+   zh/index
+
 Indices and tables
 ==================
 
 * :ref:`genindex`
 * :ref:`modindex`
 * :ref:`search`
 
-.. toctree::
-   :hidden:
-
-   zh/index.rst
diff --git a/docs/installation.rst b/docs/installation.rst
@@ -21,9 +21,6 @@ Alternatively, You may want to install `WSL2 <https://docs.microsoft.com/zh-cn/w
 
 .. code:: bash
 
-    # if on WSL2, we need to enable systemd first. Otherwise, skip this step.
-    ./script/wsl/enable_systemd.sh
-
     # run the environment preparing script.
     ./scripts/prepare_env.sh