forked from mit-han-lab/mcunet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsetup.py
55 lines (45 loc) · 2.01 KB
/
setup.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
#!/usr/bin/env python
import os, sys
import shutil
import datetime
from setuptools import setup, find_packages
from setuptools.command.install import install
readme = open('README.md').read()
readme = '''
# MCUNet: Tiny Deep Learning on IoT Devices
### [website](http://mcunet.mit.edu/) | [paper](https://arxiv.org/abs/2007.10319) | [demo](https://www.youtube.com/watch?v=YvioBgtec4U&feature=emb_logo)
## News
- **(2022/06)** We refactor the MCUNet repo as a standalone repo (previous repo: https://github.com/mit-han-lab/tinyml)
- **(2021/10)** Checkout our new paper **MCUNetV2**: https://arxiv.org/abs/2110.15352 !
- Our projects are covered by: [MIT News](https://news.mit.edu/2020/iot-deep-learning-1113), [WIRED](https://www.wired.com/story/ai-algorithms-slimming-fit-fridge/), [Morning Brew](https://www.morningbrew.com/emerging-tech/stories/2020/12/07/researchers-figured-fit-ai-ever-onto-internet-things-microchips), [Stacey on IoT](https://staceyoniot.com/researchers-take-a-3-pronged-approach-to-edge-ai/), [Analytics Insight](https://www.analyticsinsight.net/amalgamating-ml-and-iot-in-smart-home-devices/), [Techable](https://techable.jp/archives/142462), etc.
'''
VERSION = "0.1.1"
requirements = [
"torch",
"torchvision"
]
# import subprocess
# commit_hash = subprocess.check_output("git rev-parse HEAD", shell=True).decode('UTF-8').rstrip()
# VERSION += "_" + str(int(commit_hash, 16))[:8]
VERSION += "_" + datetime.datetime.now().strftime("%Y%m%d%H%M")
setup(
# Metadata
name="mcunet",
version=VERSION,
author="MTI HAN LAB ",
author_email="[email protected]",
url="https://github.com/mit-han-lab/mcunet",
description="MCUNet: Tiny Deep Learning on IoT Devices",
long_description=readme,
long_description_content_type="text/markdown",
license="MIT",
# Package info
packages=find_packages(exclude=("*test*",)),
#
zip_safe=True,
install_requires=requirements,
# Classifiers
classifiers=[
"Programming Language :: Python :: 3",
],
)