Quick Start of Distributed ZOOpt
Distributed ZOOpt is the distributed version of ZOOpt. In order to improve the efficiency of handling distributed computing, we use Julia language to code the client end for its high efficiency and Python-like features ( ZOOclient ). Meanwhile, the servers are still coded in Python (ZOOsrv) . Therefore, a user can program the objective function in Python as usual, and only need to change a few lines of the client Julia codes (just as easy to understand as Python).
Two zeroth-order optimization methods are implemented in Distributed ZOOpt release 0.1, respectively are Asynchronous Sequential RACOS (ASRacos) method and parallel pareto optimization for subset selection method (PPOSS, IJCAI'16)
Distributed ZOOpt contains two parts: ZOOclient and ZOOsrv.
The client only needs to be installed in the client node. The client is written in Julia scripts, if you have not done so already, download and install Julia (Any version starting with 0.6 should be fine)
To install ZOOclient, start Julia and run:
Pkg.add("ZOOclient")This will download ZOOclient support codes and all of its dependencies.
We have two type of servers, the control server and the evaluation server. Only one control server is needed in the network, and every computing node needs to run an evaluation server. The two servers are both in the ZOOsrv package. The easiest way to get ZOOsrv is to type pip install zoosrv in you terminal/command line.
If you want to install ZOOsrv by source code, download this project and sequentially run following commands in your terminal/command line.
$ python setup.py build
$ python setup.py install
In this example, we only have one machine. Thus the client, control server, and evaluation server are all run in the machine.
Write a simple start_control_server.py, including the following codes (Example code in ZOOsrv)
from zoosrv import control_server
control_server.start(20000)where the parameter 20000 is the listening port of the control server. Then, run the codes as in command line
python start_control_server.py
Write a start_evaluation_server.py, including the following codes (Example code in ZOOsrv)
from zoosrv import evaluation_server
evaluation_server.start("evaluation_server.cfg")where evaluation_server.cfg is the configuration file.
Then, write the evaluation_server.cfg file including the following lines: (Example code in ZOOsrv)
[evaluation server]
shared fold = /path/to/project/ZOOsrv/example/objective_function/
control server ip_port = 127.0.0.1:20000
evaluation processes = 10
starting port = 60003
ending port = 60020
where shared fold is the fold storing the objective function files. control server ip_port is the address of the control server, and the last three lines state we want to start 10 evaluation processes by choosing 10 available ports from 60003 to 60020.
Finally, launch the evaluation server in command line
python start_evaluation_server.py
We try to optimize the Ackley function.
Write fx.py including the following codes (Example code in ZOOsrv)
import numpy as np
def ackley(solution):
x = solution.get_x()
bias = 0.2
value = -20 * np.exp(-0.2 * np.sqrt(sum([(i - bias) * (i - bias) for i in x]) / len(x))) - \
np.exp(sum([np.cos(2.0*np.pi*(i-bias)) for i in x]) / len(x)) + 20.0 + np.e
return value where shared fold is the directory the fx.py stores.
Write client.jl including the following codes (Example code in ZOOsrv)
using ZOOclient
using PyPlot
# define a Dimension object
dim_size = 100
dim_regs = [[-1, 1] for i = 1:dim_size]
dim_tys = [true for i = 1:dim_size]
mydim = Dimension(dim_size, dim_regs, dim_tys)
# define an Objective object
obj = Objective(mydim)
# define a Parameter Object, the five parameters are indispensable.
# budget: number of calls to the objective function
# evalueation_server_num: number of evaluation cores user requires
# control_server_ip_port: the ip:port of the control server
# objective_file: objective funtion is defined in this file
# func: name of the objective function
par = Parameter(budget=10000, evaluation_server_num=10, control_server_ip_port="127.0.0.1:20000",
objective_file="fx.py", func="ackley")
# perform optimization
sol = zoo_min(obj, par)
# print the Solution object
sol_print(sol)
# visualize the optimization progress
history = get_history_bestsofar(obj)
plt[:plot](history)
plt[:savefig]("figure.png")Now, we can run the client file to perform the optimization
$ ./julia -p 4 /absolute/path/to/your/file/client.jl
where julia -p n provides n processes for the client on the local machine. Generally it makes sense for n to equal the number of CPU cores on the machine.
For a few seconds, the optimization is done and we will get the result.
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Visualized optimization progress looks like:
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