我正在尝试调整一些参数并且搜索空间非常大。到目前为止，我有 5 个维度，它可能会增加到大约 10 个。问题是，如果我能弄清楚如何对它进行多处理，我认为我可以获得显着的加速，但我找不到任何好的方法它。我正在使用 hyperopt，但我不知道如何让它使用 1 个以上的内核。这是我没有所有不相关内容的代码:
from numpy import random
from pandas import DataFrame
from hyperopt import fmin, tpe, hp, Trials
def calc_result(x):
huge_df = DataFrame(random.randn(100000, 5), columns=['A', 'B', 'C', 'D', 'E'])
total = 0
# Assume that I MUST iterate
for idx_and_row in huge_df.iterrows():
idx = idx_and_row[0]
row = idx_and_row[1]
# Assume there is no way to optimize here
curr_sum = row['A'] * x['adjustment_1'] + \
row['B'] * x['adjustment_2'] + \
row['C'] * x['adjustment_3'] + \
row['D'] * x['adjustment_4'] + \
row['E'] * x['adjustment_5']
total += curr_sum
# In real life I want the total as high as possible, but for the minimizer, it has to negative a negative value
total_as_neg = total * -1
print(total_as_neg)
return total_as_neg
space = {'adjustment_1': hp.quniform('adjustment_1', 0, 1, 0.001),
'adjustment_2': hp.quniform('adjustment_2', 0, 1, 0.001),
'adjustment_3': hp.quniform('adjustment_3', 0, 1, 0.001),
'adjustment_4': hp.quniform('adjustment_4', 0, 1, 0.001),
'adjustment_5': hp.quniform('adjustment_5', 0, 1, 0.001)}
trials = Trials()
best = fmin(fn = calc_result,
space = space,
algo = tpe.suggest,
max_evals = 20000,
trials = trials)
到目前为止，我有 4 个内核，但我基本上可以根据需要获得尽可能多的内核。我怎样才能让
hyperopt 使用 1 个以上的核心，或者是否有一个可以多处理的库？ 最佳答案
如果您有 Mac 或 Linux(或 Windows Linux 子系统)，您可以添加大约 10 行代码与 ray 并行执行此操作。如果您通过 latest wheels here 安装 ray，那么您只需稍作修改即可运行脚本，如下所示，以使用 HyperOpt 进行并行/分布式网格搜索。在较高级别上，它使用 tpe.suggest 运行 fmin 并以并行方式在内部创建 Trials 对象。
from numpy import random
from pandas import DataFrame
from hyperopt import fmin, tpe, hp, Trials
def calc_result(x, reporter): # add a reporter param here
huge_df = DataFrame(random.randn(100000, 5), columns=['A', 'B', 'C', 'D', 'E'])
total = 0
# Assume that I MUST iterate
for idx_and_row in huge_df.iterrows():
idx = idx_and_row[0]
row = idx_and_row[1]
# Assume there is no way to optimize here
curr_sum = row['A'] * x['adjustment_1'] + \
row['B'] * x['adjustment_2'] + \
row['C'] * x['adjustment_3'] + \
row['D'] * x['adjustment_4'] + \
row['E'] * x['adjustment_5']
total += curr_sum
# In real life I want the total as high as possible, but for the minimizer, it has to negative a negative value
# total_as_neg = total * -1
# print(total_as_neg)
# Ray will negate this by itself to feed into HyperOpt
reporter(timesteps_total=1, episode_reward_mean=total)
return total_as_neg
space = {'adjustment_1': hp.quniform('adjustment_1', 0, 1, 0.001),
'adjustment_2': hp.quniform('adjustment_2', 0, 1, 0.001),
'adjustment_3': hp.quniform('adjustment_3', 0, 1, 0.001),
'adjustment_4': hp.quniform('adjustment_4', 0, 1, 0.001),
'adjustment_5': hp.quniform('adjustment_5', 0, 1, 0.001)}
import ray
import ray.tune as tune
from ray.tune.hpo_scheduler import HyperOptScheduler
ray.init()
tune.register_trainable("calc_result", calc_result)
tune.run_experiments({"experiment": {
"run": "calc_result",
"repeat": 20000,
"config": {"space": space}}}, scheduler=HyperOptScheduler())
关于Python 和 HyperOpt : How to make multi-process grid searching?，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/49370879/