lastest release Version:1.9.7.1002

32 bit: http://pan.baidu.com/s/1mgXobxe  

64 bit: http://pan.baidu.com/s/1pKjtk3T  

Release time: 2015-11-04

Changelog:
update ngx_lua module to 0.9.19
update headers-more-nginx-module to 0.28
update ngx_lua_upstream module to 0.04
update rds-csv-nginx-module to 0.07

update lua-cjson module to 2.1.0.3

update lua-rds-parser module to 0.06
update lua-redis-parser module to 0.12

update LuaJIT to 2.1.0 beta1 

nginx openresty 迁移到 windows 下的版本。

主要特性：

•  高性能

•  超过 20,000 连接并发 (特定cpu多核的机器上，server新版本的 35000并发)

•  多进程

•  各种 Nginx bug 修复

包括以下模块：

•  ngx-coolkit-module

•  echo-nginx-module

•  set-misc-nginx-module

•  ngx-postgres-module

•  ngx-lua-module

•  headers-more-nginx-module

•  rds-json-nginx-module

from http://docs.continuum.io/anaconda-cluster/examples/spark-caffe

Deep Learning (Spark, Caffe, GPU)

Description

To demonstrate the capability of running a distributed job in PySpark using a GPU, this example uses a neural network library, Caffe. Below is a trivial example of using Caffe on a Spark cluster; although this is redundant, it demonstrates the capability of training neural networks with GPUs.

For this example, we recommend the use of the AMI ami-2cbf3e44 and the instance type g2.2xlarge. An example profile (to be placed in ~/.acluster/profiles.d/gpu_profile.yaml) is shown below:

name: gpu_profile
node_id: ami-2cbf3e44 # Ubuntu 14.04 - IS HVM - Cuda 6.5
user: ubuntu
node_type: g2.2xlarge
num_nodes: 3
provider: aws
plugins:
  - spark-yarn
  - notebook

Download

To execute this example, download the: spark-caffe.py example script or spark-caffe.ipynbexample notebook.

Installation

The Spark + YARN plugin can be installed on the cluster using the following command:

$ acluster install spark-yarn

Once the Spark + YARN plugin is installed, you can view the YARN UI in your browser using the following command:

$ acluster open yarn

Dependencies

First, we need to bootstrap Caffe and its dependencies on all of the nodes. We provide a bash script that will install Caffe from source: bootstrap-caffe.sh. The following command can be used to upload the bootstrap-caffe.sh script to all of the nodes and execute it in parallel:

$ acluster submit bootstrap-caffe.sh --all

After a few minues, Caffe and its dependencies will be installed on the cluster nodes and the job can be started.

Running the Job

Here is the complete script to run the Spark + GPU with Caffe example in PySpark:

# spark-caffe.py from pyspark import SparkConf from pyspark import SparkContext  conf = SparkConf() conf.setMaster('yarn-client') conf.setAppName('spark-caffe') sc = SparkContext(conf=conf)   def noop(x):     import socket     return socket.gethostname()  rdd = sc.parallelize(range(2), 2) hosts = rdd.map(noop).distinct().collect() print hosts   def caffe_process(x):     import os     os.environ['PATH'] = '/usr/local/cuda/bin' + ':' + os.environ['PATH']     os.environ['LD_LIBRARY_PATH'] = '/usr/local/cuda/lib64:/home/ubuntu/pombredanne-https-gitorious.org-mdb-mdb.git-9cc04f604f80/libraries/liblmdb'     import subprocess     proc = subprocess.Popen('cd /home/ubuntu/caffe && bash ./examples/mnist/train_lenet.sh', shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)     out, err = proc.communicate()     return proc.returncode, out, err  rdd = sc.parallelize(range(2), 2) ret = rdd.map(caffe_process).distinct().collect() print ret 

You can submit the script to the Spark cluster using the submit command.

$ acluster submit spark-caffe.py 

After the script completes, the trained Caffe model can be found at/home/ubuntu/caffe/examples/mnist/lenet_iter_10000.caffemodel on all of the compute nodes.

Beanstalk 是一个简单、快速的消息队列。Beanstalkd之于RabbitMQ，就好比Nginx之于Apache，Varnish之于Squid。后面在项目中使用Beanstalkd的过程中，更发现其简单、轻量级、高性能、易使用等特点，以及优先级、多队列、持久化、分布式容错、超时控制等特性。

Beanstalkd 包含多种编程语言的客户端开发包，详情请看这里。Beanstalkd 同时也提供一些图形化的管理工具，详情请看这里。

示例代码：

旧版demo集合介绍和下载：http://yun.baidu.com/share/link?shareid=201029&uk=2485132527&third=null

2014--04-26  :http://pan.baidu.com/s/1mgKBloO 新版以及demo等文件

QtWebkit版本最新：

github: https://github.com/caidongyun/lomox.git

Cef版本筹备中.

当乐游戏pc

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