Merge pull request intel-analytics#16 from Oscilloscope98/refactor-la…

…nding-page

Revise landing page

docs/readthedocs/source/conf.py

@@ -42,9 +42,9 @@
             "type": "fontawesome",
         }
    ],
-   "navbar_start": ["navbar-logo.html", "version_badge.html"]
+   "navbar_start": ["navbar-logo.html", "version_badge.html"],
+   "navbar_end": ["navbar-icon-links.html"], # remove dark mode for now
 }
-html_logo = "../image/bigdl_logo.png"
 
 # add search bar to side bar
 html_sidebars = {
@@ -54,6 +54,14 @@
     ],
     "**": ["sidebar_backbutton.html","sidebar-nav-bs.html"]
 }
+
+# remove dark mode for now
+html_context = {
+    "default_mode": "light" 
+}
+
+html_logo = "../image/bigdl_logo.png"
+
 # hard code it for now, may change it to read from installed bigdl
 release = "latest"
 
@@ -105,7 +113,8 @@
     'sphinx_external_toc',
     'sphinx_design',
     'nbsphinx',
-    'nbsphinx_link'
+    'nbsphinx_link',
+    'sphinx.ext.graphviz' # for embedded graphviz diagram
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -266,4 +275,7 @@ def setup(app):
     app.add_transform(AutoStructify)
 
 # disable notebook execution
-nbsphinx_execute = 'never'
+nbsphinx_execute = 'never'
+
+# make output of graphviz diagram to svg
+graphviz_output_format = 'svg'

docs/readthedocs/source/index.rst

@@ -1,14 +1,50 @@
 BigDL Documentation
 ===========================
 
+BigDL seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries:
+
+- `Orca <doc/Orca/index.html>`_: Distributed Big Data & AI (TF & PyTorch) Pipeline on Spark and Ray
+- `Nano <doc/Nano/index.html>`_: Transparent Acceleration of Tensorflow & PyTorch Programs
+- `DLlib <doc/DLlib/index.html>`_ "Equivalent of Spark MLlib" for Deep Learning
+- `Chronos <doc/Chronos/index.html>`_: Scalable Time Series Analysis using AutoML
+- `Friesian <doc/Friesian/index.html>`_: End-to-End Recommendation Systems
+- `PPML <doc/PPML/index.html>`_ (experimental): Secure Big Data and AI (with SGX Hardware Security)
+
 ------
 
-`BigDL <https://github.com/intel-analytics/BigDL/>`_ makes it easy for data scientists and data engineers to build end-to-end, distributed AI applications. The **BigDL 2.0** release combines the `original BigDL <https://github.com/intel-analytics/BigDL/tree/branch-0.14>`_ and `Analytics Zoo <https://github.com/intel-analytics/analytics-zoo>`_ projects, providing the following features:
+Choosing the right BigDL library
+---------------------------
+
+.. graphviz::
 
-* `DLlib <doc/DLlib/Overview/dllib.html>`_: distributed deep learning library for Apache Spark
-* `Orca <doc/Orca/Overview/orca.html>`_: seamlessly scale out TensorFlow and PyTorch pipelines for distributed Big Data
-* `RayOnSpark <doc/Ray/Overview/ray.html>`_: run Ray programs directly on Big Data clusters
-* `Chronos <doc/Chronos/Overview/chronos.html>`_: scalable time series analysis using AutoML
-* `PPML <doc/PPML/Overview/ppml.html>`_: privacy preserving big data analysis and machine learning (*experimental*)
-* `Nano <doc/Nano/Overview/nano.html>`_: automatically accelerate TensorFlow and PyTorch pipelines by applying modern CPU optimizations
--------
+    digraph BigDLDecisionTree {
+        node [color="#459db9" shape="box" fontname="Arial" fontsize=14]
+        edge [fontname="Arial" fontsize=12]
+
+        Feature1 [label="Hardware Secured Big Data & AI?"]
+        Feature2 [label="Python vs. Scala/Java?"]
+        Feature3 [label="What type of application?"]
+        Feature4 [label="Domain?"]
+
+        Orca[href="../doc/Orca/index.html" target="_blank" target="_blank" style="rounded,filled" fillcolor="#459db9" fontcolor="#ffffff"]
+        Nano[href="../doc/Nano/index.html" target="_blank" target="_blank" style="rounded,filled" fillcolor="#459db9" fontcolor="#ffffff"]
+        DLlib1[label="DLlib" href="../doc/DLlib/index.html" target="_blank" style="rounded,filled" fillcolor="#459db9" fontcolor="#ffffff"]
+        DLlib2[label="DLlib" href="../doc/DLlib/index.html" target="_blank" style="rounded,filled" fillcolor="#459db9" fontcolor="#ffffff"]
+        Chronos[href="../doc/Chronos/index.html" target="_blank" style="rounded,filled" fillcolor="#459db9" fontcolor="#ffffff"]
+        Friesian[href="../doc/Friesian/index.html" target="_blank" style="rounded,filled" fillcolor="#459db9" fontcolor="#ffffff"]
+        PPML[href="../doc/PPML/index.html" target="_blank" style="rounded,filled" fillcolor="#459db9" fontcolor="#ffffff"]
+
+        Feature1 -> Feature2[label="No"]
+        Feature1 -> PPML[label="Yes"]
+
+        Feature2 -> Feature3[label="Python"]
+        Feature2 -> DLlib1[label="Scala/Java"]
+
+        Feature3 -> Orca[label="Distributed Big Data \n + \n  AI (TF/PyTorch)"]
+        Feature3 -> Nano[label="Accelerate \n TensorFlow / PyTorch"]
+        Feature3 -> DLlib2[label="DL for Spark MLlib"]
+        Feature3 -> Feature4[label="High Level \n  App Framework"]
+
+        Feature4 -> Chronos[label="Time Series"]
+        Feature4 -> Friesian[label="Recommender System"]
+    }