我尝试运行一个简单的示例,使用嵌套采样和dynesty
。你知道吗
我从github安装了dynesty
:https://github.com/joshspeagle/dynesty
操作系统:Mac OSX El Capitan(10.11.6)
CPU:8核
内存:16.0 GB
gcc:4.8.5通过conda install gcc
我运行了下面的代码(模拟数据、设置优先级/可能性、提交到dynesty
)。你知道吗
为了建立多处理,我使用了multiprocessing.Pool
、concurrent.futures.ProcessPoolExecutor
和concurrent.futures.ThreadPoolExecutor
。你知道吗
我尝试了Jupyter Lab
、ipython
和(脚本)python run_dynesty_test.py
中的代码。你知道吗
问题:整个脚本运行良好;但是dynesty/python开始使用所有核心;然后它慢慢开始使用越来越少的核心。最后,大约5分钟后,dynesty/python几乎正好使用1个内核。你知道吗
证据:htop
开始读取780%,然后是550%,然后是350%,然后是100%的CPU—在其余的操作中它保持在100%的CPU;除了每隔一分钟读取一次外,htop
将读取约250-300%的CPU。你知道吗
为什么dynesty/ThreadPoolExecutor/python/etc没有一直使用我的所有内核?你知道吗
with ThreadPoolExecutor(max_workers=cpu_count()-1) as executor:
sampler = dynesty.DynamicNestedSampler(
loglike,
prior,
ndim=ndims,
nparam=ndims,
bound='multi',
sample='unif',
pool=executor,
queue_size=cpu_count())
sampler.run_nested(nlive_init=100,nlive_batch=100)
res = sampler.results
from __future__ import absolute_import,\
unicode_literals, print_function
from multiprocessing import set_start_method
set_start_method('forkserver')
import dynesty
import math
import os
import threading, subprocess
from sys import platform
from numpy import pi, sin, cos, linspace
from pylab import *#;ion()
from multiprocessing import Pool, cpu_count
if not os.path.exists("chains"): os.mkdir("chains")
# plotting
import matplotlib
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def gaussian1Dp(cube):
center = cube[0]
width = cube[1]
height = cube[2]
return lambda y: height*np.exp(-0.5*(( (center - y) / width)**2))
np.random.seed(42)
param0a= -0.5
param0b= 0.5
param1a= 0.1
param1b= 0.1
param2a= 0.8
param2b= 0.8
yunc = 0.1
nPts = int(100)
nThPts= int(1e3)
xmin = -1
xmax = 1
dx = 0.1*(xmax - xmin)
yuncs = np.random.normal(yunc, 1e-2 * yunc, nPts)
thdata= np.linspace(xmin-dx, xmax+dx, nThPts)
xdata = np.linspace(xmin,xmax,nPts)
ydata = model([param0a,param1a,param2a])(xdata) \
+ model([param0b,param1b,param2b])(xdata)
yerr = np.random.normal(0, yuncs, nPts)
zdata = ydata + yerr
figure(figsize=(10,10))
plot(thdata, model([param0a,param1a,param2a])(thdata) \
+ model([param0b,param1b,param2b])(thdata))
errorbar(xdata, zdata, yunc*ones(zdata.size), fmt='o')
show()
def prior(cube):
cube[0] = cube[0]*2 - 1
cube[1] = cube[1]*2
cube[2] = cube[2]*2
return cube
def loglike(cube):
modelNow = model(cube)(xdata)
return -0.5*((modelNow - ydata)**2./yuncs**2.).sum()
from concurrent.futures import ThreadPoolExecutor,\
ProcessPoolExecutor
if __name__ == '__main__':
if not os.path.exists("chains"): os.mkdir("chains")
n_params = len(parameters)
ndims = 3
with ThreadPoolExecutor(max_workers=cpu_count()-1) as executor:
sampler = dynesty.DynamicNestedSampler(
loglike,
prior,
ndim=ndims,
nparam=ndims,
bound='multi',
sample='unif',
pool=executor,
queue_size=cpu_count())
sampler.run_nested(nlive_init=100, nlive_batch=100)
res = sampler.results
from dynesty import plotting as dyplot
# evidence check
fig, axes = dyplot.runplot(res, color='red',
lnz_truth=lnz_truth,
truth_color='black',
logplot=True)
fig.tight_layout()
joblib.dump(res,'dynesty_double_gaussian_test_results.joblib.save')
目前没有回答
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