fix typo based on test v1.0a1

README.md

@@ -40,12 +40,12 @@ nn-Meter is a latency predictor of models with type of Tensorflow, PyTorch, Onnx
 |  Testing Model Type   |                       Requirements                      |
 | :-------------------: | :------------------------------------------------:     |
 |       Tensorflow      |  `tensorflow==1.15.0`                                  |
-|         Torch         |  `torch==1.7.1`, `torchvision==0.8.2`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni==2.4`]$^{[1]}$  |
+|         Torch         |  `torch==1.7.1`, `torchvision==0.8.2`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni==2.4`][1]  |
 |          Onnx         |  `onnx==1.9.0`                                         |
 |    nn-Meter IR graph  |   ---                                                  |
 |      NNI IR graph     |  `nni==2.4`                                            |
 
-${[1]}$ Please refer to [nn-Meter Usage](#torch-model-converters) for more information
+[1] Please refer to [nn-Meter Usage](#torch-model-converters) for more information.
 
 
 Please also check the versions of `numpy` and `scikit_learn`. The different versions may change the prediction accuracy of kernel predictors.

docs/quick_start.md

@@ -19,12 +19,12 @@ nn-Meter is a latency predictor of models with type of Tensorflow, PyTorch, Onnx
 |  Testing Model Type   |                       Requirements                      |
 | :-------------------: | :------------------------------------------------:     |
 |       Tensorflow      |  `tensorflow==1.15.0`                                  |
-|         Torch         |  `torch==1.7.1`, `torchvision==0.8.2`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni==2.4`]$^{[1]}$ |
+|         Torch         |  `torch==1.7.1`, `torchvision==0.8.2`, (alternative)[`onnx==1.9.0`, `onnx-simplifier==0.3.6`] or [`nni==2.4`][1] |
 |          Onnx         |  `onnx==1.9.0`                                         |
 |    nn-Meter IR graph  |   ---                                                  |
 |      NNI IR graph     |  `nni==2.4`                                            |
 
-${[1]}$ Please refer to [nn-Meter Usage](usage.md#torch-model-converters) for more information.
+[1] Please refer to [nn-Meter Usage](usage.md#torch-model-converters) for more information.
 
 Please also check the versions of `numpy` and `scikit_learn`. The different versions may change the prediction accuracy of kernel predictors.
 

nn_meter/__init__.py

@@ -1,5 +1,12 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
+
+try:
+    import pkg_resources  # part of setuptools
+    __version__ = pkg_resources.require("nn-meter")[0].version
+except ModuleNotFoundError:
+    __version__ = 'UNKNOWN'
+
 from .nn_meter import (
     nnMeter,
     load_latency_predictor,

setup.py

@@ -4,7 +4,7 @@ from setuptools import setup, find_packages
 
 
 setup(
-    name='nn_meter',
+    name='nn-meter',
     version='1.0',
     description='nn-Meter is a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices.',
     long_description = open('README.md', encoding='utf-8').read(),
@@ -13,7 +13,7 @@ setup(
     author_email='nn-meter-dev@microsoft.com',
     url='https://github.com/microsoft/nn-Meter',
     project_urls={
-        'prediction models and fusion rules': 'https://github.com/microsoft/nn-Meter/releases/tag/v1.0-data'
+        'Data of models': 'https://github.com/microsoft/nn-Meter/releases/tag/v1.0-data',
     },
     license = 'MIT',
     classifiers = [