AI_HUB
AI utils for developer. such as notice、send massage when model training is over.Bind WeChat Official Account（AI_HUB） 插入在代码里的小工具，可以在模型训练结束时通过公众号及时发送微信消息给自己，提高科研效率。 inferServer: server your ai model as a API and match the tianchi eval 简单的操作把你训练好的模型变为服务API，并且支持天池大赛的流评测。
INSTALL
pip install ai-hub
SAMPLE
NOTICE
from ai_hub import notice
#到AGIHub微信公众号获取个人openid如（oM8pVuBWl8Rw_vFz7rZNgeO4T8H8）,需替换为自己的openid
nc = notice("oM8pVuBWl8Rw_vFz7rZNgeO4T8H8")
#借助AGIHub公众号发送消息给自己
nc.sendmsg("hi,AI_HUB.I am su")
inferServer
'''
依赖：pip install ai-hub #(version>=0.1.7)
测试用例：
model为y=2*x
请求数据为json:{"img":3}
-----------
post请求：
curl localhost:8080/tccapi -X POST -d '{"img":3}'
返回结果 6
'''
from ai_hub import inferServer
import json
class myInfer(inferServer):
def __init__(self, model):
super().__init__(model)
print("init_myInfer")
#数据前处理
def pre_process(self, data):
print("my_pre_process")
#json process
json_data = json.loads(data.decode('utf-8'))
img = json_data.get("img")
print("processed data: ", img)
return img
#数据后处理
def post_process(self, data):
print("post_process")
processed_data = data
return processed_data
#模型预测：默认执行self.model(preprocess_data)，一般不用重写
#如需自定义，可覆盖重写
#def pridect(self, data)：
# ret = self.model(data)
# return ret
if __name__ == "__main__":
mymodel = lambda x: x * 2
my_infer = myInfer(mymodel)
my_infer.run(debuge=True) #默认为("127.0.0.1", 80)，可自定义端口，如用于天池大赛请默认即可，指定debuge=True可获得更多报错信息
TccProgressBar
from ai_hub import TccProgressBar
#定义progress，显示名为training，在竞赛平台TCC上显示该进度条（tccBar_show=false 不影响本地打印进度条）
progress = TccProgressBar(title="training", tccBar_show=True)
for j in progress(range(100)):
time.sleep(0.1)
TccTensorboard
from ai_hub import Logger
#Logger用法与tensorboard的logger包一致
info= {
'loss': loss.data[0],
'accuracy': accuracy.data[0]
}
for tag, value in info.items():
logger.scalar_summary(tag, value, step)
获取OPENID
1.扫描关注公众号AGIHub
2.发送“openid”给公众号 即可获得openid
1 maintainer
Metadata
1 maintainer
Release files for ai-hub 0.5.3
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