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Performance of Flask, Tornado, GEvent, and their combinations

Wensheng Wang, 10/1/11

Source: http://blog.wensheng.org/2011/10/performance-of-flask-tornado-gevent-and.html

When choosing a web framework, I pretty much have eyes set on Tornado. But I heard good things about Flask and Gevent. So I tested the performance of each and combinations of the three. I chose something just a little more advanced than a "Hello World" program to write - one that use templates. Here are the codes:

1, Pure Flask (pure_flask.py)

Docker 치트 시트

한국어 번역(초벌): nacyot
같이 읽으면 좋은 문서들

왜 Docker를 사용해야하는가?

[공통] 마크다운 markdown 작성법

영어지만, 조금 더 상세하게 마크다운 사용법을 안내하고 있는
"Markdown Guide (https://www.markdownguide.org/)" 를 보시는 것을 추천합니다. ^^

아, 그리고 마크다운만으로 표현이 부족하다고 느끼신다면, HTML 태그를 활용하시는 것도 좋습니다.

1. 마크다운에 관하여

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

도커와 AWS를 활용한 클라우드 딥러닝 환경 구축

글쓴이: 김정주([email protected])

최근 딥러닝 관련 패키지들은 대부분 CPU와 GPU를 함께 지원하고 있습니다. GPU를 사용하면 보다 빠르게 학습 결과를 낼 수 있지만, GPU를 활용하기 위해서는 NVIDIA계열의 그래픽 카드, 드라이버 S/W 그리고 CUDA의 설치를 필요로 합니다.

이 글에서는 AWS의 GPU 인스턴스와 도커를 활용해 딥러닝 패키지(Caffe)를 편리하게 사용하는 방법을 소개합니다.

##VGG19 model for Keras

This is the Keras model of the 19-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

	"""
	Some python code for
	Markov Chain Monte Carlo and Gibs sampling
	by Bruce Walsh
	"""


	import numpy as np
	import numpy.linalg as npla

	'''This scripts implements Kim's paper "Convolutional Neural Networks for Sentence Classification"
	with a very small embedding size (20) than the commonly used values (100 - 300) as it gives better
	result with much less parameters.

	Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py

	Get to 0.853 test accuracy after 5 epochs. 13s/epoch on Nvidia GTX980 GPU.
	'''

	from __future__ import print_function