README.md

-# Autotuning and Predictive Autotuning
-
-`predtuner` performs autotuning on program approximation knobs using an error-predictive proxy
-in place of the original program, to greatly speedup autotuning while getting results
-comparable in quality. `current_version == 0.3`.
-
-## Requirements
-
-`predtuner` requires `python >= 3.7` and `pip`, preferrably `pip >= 20`.
-To install from PyPI (currently TestPyPI), use
-
-```bash
-python -m pip install -i https://test.pypi.org/simple/ predtuner
-```
-
-### Install from Source
-
-Alternatively, you can install this package from source.
-At the root directory of this repository, do:
-
-```bash
-python -m pip install -e ./
-```
-
-With the flag `-e`, any changes to code in this repo is reflected on the installed version automatically.
-It can be omitted if you don't intend to modify the code in this package.
-
-## Getting Started
-
-The documentation page contains a full tutorial.
-Build the documentation by:
-
-```bash
-pip install sphinx sphinx_rtd_theme sphinx_autodoc_typehints
-cd doc
-make html
-```
-
-The documentation page will be created as `doc/build/html/index.html`.
-You can open this in the browser and browse to "Getting Started" section.
-
-### Model Data for Example / Testing
-
-`predtuner` contains 10 demo models which are also used in tests.
-
-- Download and extract [this](https://drive.google.com/file/d/1V_yd9sKcZQ7zhnO5YhRpOsaBPLEEvM9u/view?usp=sharing) file containing all 10 models, for testing purposes.
-- The "Getting Started" example on the documentation page only uses VGG16-CIFAR10.
-  If you don't need the other models, get the data for VGG16-CIFAR10
-  [here](https://drive.google.com/file/d/1Z84z-nsv_nbrr8t9i28UoxSJg-Sd_Ddu/view?usp=sharing).
-
-In either case, there should be a `model_params/` folder at the root of repo after extraction.
-
-## Tuning with HPVM Binary
-
-This branch (`hpvm`) contains beta support for HPVM binaries.
-Please refer to `examples/tune_hpvm_bin.py` for an example with explanations.

README.rst

+Autotuning and Predictive Autotuning
+====================================
+
+``predtuner`` performs autotuning on program approximation knobs using an error-predictive proxy
+in place of the original program, to greatly speedup autotuning while getting results
+comparable in quality. ``current_version == 0.3``.
+
+Requirements
+------------
+
+``predtuner`` requires ``python >= 3.7`` and ``pip``, preferrably ``pip >= 20``.
+To install from PyPI (currently TestPyPI), use
+
+.. code-block:: bash
+
+   python -m pip install -i https://test.pypi.org/simple/ predtuner
+
+Install from Source
+^^^^^^^^^^^^^^^^^^^
+
+Alternatively, you can install this package from source.
+At the root directory of this repository, do:
+
+.. code-block:: bash
+
+   python -m pip install -e ./
+
+With the flag ``-e``, any changes to code in this repo is reflected on the installed version automatically.
+It can be omitted if you don't intend to modify the code in this package.
+
+Getting Started
+---------------
+
+The documentation page contains a full tutorial.
+Build the documentation by:
+
+.. code-block:: bash
+
+   pip install sphinx sphinx_rtd_theme sphinx_autodoc_typehints
+   cd doc
+   make html
+
+The documentation page will be created as ``doc/build/html/index.html``.
+You can open this in the browser and browse to "Getting Started" section.
+
+Model Data for Example / Testing
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+``predtuner`` contains 10 demo models which are also used in tests.
+
+
+* Download and extract `this <https://drive.google.com/file/d/1V_yd9sKcZQ7zhnO5YhRpOsaBPLEEvM9u/view?usp=sharing>`_ file containing all 10 models, for testing purposes.
+* The "Getting Started" example on the documentation page only uses VGG16-CIFAR10.
+  If you don't need the other models, get the data for VGG16-CIFAR10
+  `here <https://drive.google.com/file/d/1Z84z-nsv_nbrr8t9i28UoxSJg-Sd_Ddu/view?usp=sharing>`_.
+
+In either case, there should be a ``model_params/`` folder at the root of repo after extraction.
+
+Tuning with HPVM Binary
+-----------------------
+
+This branch (``hpvm``) contains beta support for HPVM binaries.
+Please refer to ``examples/tune_hpvm_bin.py`` for an example with explanations.

setup.py

 import setuptools
 
-with open("README.md", "r", encoding="utf-8") as fh:
+with open("README.rst", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
 setuptools.setup(
@@ -10,9 +10,13 @@ setuptools.setup(
     author_email="yifanz16@illinois.edu",
     description="A package for predictive and empirical approximation autotuning",
     long_description=long_description,
-    long_description_content_type="text/markdown",
+    long_description_content_type="text/x-rst",
     url="https://github.com/Evan-Zhao/predictive-tuner",
-    packages=["predtuner"],
+    packages=setuptools.find_packages(),
+    package_data={
+        "predtuner.approxes": ["default_approx_params.json"]
+    },
+    include_package_data=True,
     install_requires=[
         "matplotlib>=3.3",
         "networkx>=2.5",