[DOCS] Migrate some markdowns to rst, fix sphinx3 warnings

docs/api/python/runtime.rst

@@ -23,28 +23,3 @@ tvm.runtime
    :imported-members:
    :exclude-members: NDArray
    :autosummary:
-
-
-.. autoclass:: tvm.runtime.PackedFunc
-   :members:
-   :inherited-members:
-
-.. autofunction:: tvm.register_func
-
-.. autofunction:: tvm.get_global_func
-
-
-.. autoclass:: tvm.runtime.Module
-   :members:
-
-.. autofunction:: tvm.runtime.load_module
-
-.. autofunction:: tvm.runtime.system_lib
-
-.. autofunction:: tvm.runtime.enabled
-
-
-.. autoclass:: tvm.runtime.Object
-   :members:
-
-.. autofunction:: tvm.register_object

docs/deploy/android.rst

@@ -0,0 +1,42 @@
+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+..    http://www.apache.org/licenses/LICENSE-2.0
+
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+Deploy to Android
+=================
+
+Build model for Android Target
+------------------------------
+
+Relay compilation of model for android target could follow same approach like android_rpc.
+The code below will save the compilation output which is required on android target.
+
+
+.. code:: python
+
+    lib.export_library("deploy_lib.so", ndk.create_shared)
+    with open("deploy_graph.json", "w") as fo:
+        fo.write(graph.json())
+    with open("deploy_param.params", "wb") as fo:
+        fo.write(relay.save_param_dict(params))
+
+deploy_lib.so, deploy_graph.json, deploy_param.params will go to android target.
+
+TVM Runtime for Android Target
+------------------------------
+
+Refer `here <https://github.com/apache/incubator-tvm/blob/master/apps/android_deploy/README.md#build-and-installation>`_ to build CPU/OpenCL version flavor TVM runtime for android target.
+From android java TVM API to load model & execute can be referred at this `java <https://github.com/apache/incubator-tvm/blob/master/apps/android_deploy/app/src/main/java/org/apache/tvm/android/demo/MainActivity.java>`_ sample source.

docs/deploy/cpp_deploy.rst

@@ -0,0 +1,56 @@
+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+..    http://www.apache.org/licenses/LICENSE-2.0
+
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+
+Deploy TVM Module using C++ API
+===============================
+
+We provide an example on how to deploy TVM modules in `apps/howto_deploy <https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy>`_
+
+To run the example, you can use the following command
+
+
+.. code:: bash
+
+    cd apps/howto_deploy
+    ./run_example.sh
+
+
+Get TVM Runtime Library
+-----------------------
+
+The only thing we need is to link to a TVM runtime in your target platform.
+TVM provides a minimum runtime, which costs around 300K to 600K depending on how much modules we use.
+In most cases, we can use ``libtvm_runtime.so`` that comes with the build.
+
+If somehow you find it is hard to build ``libtvm_runtime``, checkout
+`tvm_runtime_pack.cc <https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/tvm_runtime_pack.cc>`_.
+It is an example all in one file that gives you TVM runtime.
+You can compile this file using your build system and include this into your project.
+
+You can also checkout `apps <https://github.com/apache/incubator-tvm/tree/master/apps/>`_ for example applications build with TVM on iOS, Android and others.
+
+Dynamic Library vs. System Module
+---------------------------------
+TVM provides two ways to use the compiled library.
+You can checkout `prepare_test_libs.py <https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/prepare_test_libs.py>`_
+on how to generate the library and `cpp_deploy.cc <https://github.com/apache/incubator-tvm/tree/master/apps/howto_deploy/cpp_deploy.cc>`_ on how to use them.
+
+- Store library as a shared library and dynamically load the library into your project.
+- Bundle the compiled library into your project in system module mode.
+
+Dynamic loading is more flexible and can load new modules on the fly. System module is a more ``static`` approach.  We can use system module in places where dynamic library loading is banned.

docs/deploy/integrate.rst

@@ -0,0 +1,69 @@
+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+..    http://www.apache.org/licenses/LICENSE-2.0
+
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+Integrate TVM into Your Project
+===============================
+
+TVM's runtime is designed to be lightweight and portable.
+There are several ways you can integrate TVM into your project.
+
+This article introduces possible ways to integrate TVM
+as a JIT compiler to generate functions on your system.
+
+
+DLPack Support
+--------------
+
+TVM's generated function follows the PackedFunc convention.
+It is a function that can take positional arguments including
+standard types such as float, integer, string.
+The PackedFunc takes DLTensor pointer in `DLPack <https://github.com/dmlc/dlpack>`_ convention.
+So the only thing you need to solve is to create a corresponding DLTensor object.
+
+
+
+Integrate User Defined C++ Array
+--------------------------------
+
+The only thing we have to do in C++ is to convert your array to DLTensor and pass in its address as
+``DLTensor*`` to the generated function.
+
+
+## Integrate User Defined Python Array
+
+Assume you have a python object ``MyArray``. There are three things that you need to do
+
+- Add ``_tvm_tcode`` field to your array which returns ``tvm.TypeCode.ARRAY_HANDLE``
+- Support ``_tvm_handle`` property in your object, which returns the address of DLTensor in python integer
+- Register this class by ``tvm.register_extension``
+
+.. code:: python
+
+   # Example code
+   import tvm
+
+   class MyArray(object):
+       _tvm_tcode = tvm.TypeCode.ARRAY_HANDLE
+
+       @property
+       def _tvm_handle(self):
+           dltensor_addr = self.get_dltensor_addr()
+           return dltensor_addr
+
+       # You can put registration step in a separate file mypkg.tvm.py
+       # and only optionally import that if you only want optional dependency.
+  tvm.register_extension(MyArray)