Update readme.rst for pypi, change documentation style (#1663)

docs/python/README.rst

@@ -3,51 +3,10 @@ ONNX Runtime
 
 ONNX Runtime
 enables high-performance evaluation of trained machine learning (ML)
-models while keeping resource usage low.
+models while keeping resource usage low once converted into ONNX format.
-Building on Microsoft's dedication to the
+See `project information <https://github.com/microsoft/onnxruntime>`_
-`Open Neural Network Exchange (ONNX) <https://onnx.ai/>`_
+and `Python API documentation and examples <https://aka.ms/onnxruntime-python>`_
-community, it supports traditional ML models as well
+for further details.
-as Deep Learning algorithms in the
-`ONNX-ML format <https://github.com/onnx/onnx/blob/master/docs/IR.md>`_.
-Documentation is available at
-`Python Bindings for ONNX Runtime <https://aka.ms/onnxruntime-python>`_.
-
-Example
--------
-
-The following example demonstrates an end-to-end example
-in a very common scenario. A model is trained with *scikit-learn*
-but it has to run very fast in a optimized environment.
-The model is then converted into ONNX format and ONNX Runtime
-replaces *scikit-learn* to compute the predictions.
-
-::
-
-    # Train a model.
-    from sklearn.datasets import load_iris
-    from sklearn.model_selection import train_test_split
-    from sklearn.ensemble import RandomForestClassifier
-    iris = load_iris()
-    X, y = iris.data, iris.target
-    X_train, X_test, y_train, y_test = train_test_split(X, y)
-    clr = RandomForestClassifier()
-    clr.fit(X_train, y_train)
-
-    # Convert into ONNX format with onnxmltools
-    from skl2onnx import convert_sklearn
-    from skl2onnx.common.data_types import FloatTensorType
-    initial_type = [('float_input', FloatTensorType([1, 4]))]
-    onx = convert_sklearn(clr, initial_types=initial_type)
-    with open("rf_iris.onnx", "wb") as f:
-        f.write(onx.SerializeToString())
-
-    # Compute the prediction with ONNX Runtime
-    import onnxruntime as rt
-    import numpy
-    sess = rt.InferenceSession("rf_iris.onnx")
-    input_name = sess.get_inputs()[0].name
-    label_name = sess.get_outputs()[0].name
-    pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
 
 Changes
 -------
@@ -61,47 +20,3 @@ Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v0.5.0
 ^^^^^
 
 Release Notes : https://github.com/Microsoft/onnxruntime/releases/tag/v0.4.0
-
-0.3.1
-^^^^^
-
-Protobuf-lite, NuGet file fixes (patch to 0.3.0).
-
-0.3.0
-^^^^^
-
-C-API, Linux support for Dotnet Nuget package, Cuda 9.1 support.
-
-0.2.1
-^^^^^
-
-C-API, Linux support for Dotnet Nuget package, Cuda 10.0 support (patch to 0.2.0).
-
-0.2.0
-^^^^^
-
-C-API, Linux support for Dotnet Nuget package, Cuda 10.0 support
-
-0.1.5
-^^^^^
-
-GA release as part of open sourcing onnxruntime (patch to 0.1.4).
-
-0.1.4
-^^^^^
-
-GA release as part of open sourcing onnxruntime.
-
-0.1.3
-^^^^^
-
-Fixes a crash on machines which do not support AVX instructions.
-
-0.1.2
-^^^^^
-
-First release on Ubuntu 16.04 for CPU and GPU with Cuda 9.1 and Cudnn 7.0,
-supports runtime for deep learning models architecture such as AlexNet, ResNet,
-XCeption, VGG, Inception, DenseNet, standard linear learner,
-standard ensemble learners,
-and transform scaler, imputer.

docs/python/conf.py

@@ -16,7 +16,6 @@ import onnxruntime
 # markdown output: it requires two extensions available at:
 # https://github.com/xadupre/sphinx-docfx-yaml
 # https://github.com/xadupre/sphinx-docfx-markdown
-import sphinx_modern_theme
 import recommonmark
 
 # -- Project information -----------------------------------------------------
@@ -67,8 +66,7 @@ pygments_style = 'sphinx'
 
 # -- Options for HTML output -------------------------------------------------
 
-html_theme = "sphinx_modern_theme"
+html_theme = "pyramid"
-html_theme_path = [sphinx_modern_theme.get_html_theme_path()]
 html_logo = "../ONNX_Runtime_icon.png"
 html_static_path = ['_static']
 

requirements-doc.txt

@@ -1,3 +1,3 @@
 sphinx
 sphinx_gallery
-sphinx_modern_theme
+