问同时运行两台dask-ml计算机，而不是顺序运行

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from dask.distributed import Client

client = Client(n_workers=4)

from dask import delayed
import numpy as np
import pandas as pd
from dask_ml import impute

mean_imputer = impute.SimpleImputer(strategy='mean')
most_frequent_imputer = impute.SimpleImputer(strategy='most_frequent')

def fit_transform_mi(d):
    return mean_imputer.fit_transform(d)
def fit_transform_mfi(d):
    return most_frequent_imputer.fit_transform(d)
def setdf(a,b,df):
    df.iloc[:, [0,1]]=a
    df.iloc[:, [2]]=b
    return df

data = [[100, 2, 5], [np.nan, np.nan, np.nan], [70, 7, 5]]
df = pd.DataFrame(data, columns = ['Weight', 'Age', 'Height']) 
a = delayed(fit_transform_mi)(df.iloc[:,[0,1]])
b = delayed(fit_transform_mfi)(df.iloc[:,[2]])
c = delayed(setdf)(a,b,df)
df= c.compute()
print(df)
client.close()

import numpy as np
import pandas as pd

N = 10000000
weight = np.array([100, np.nan, 70]*N)
age = np.array([2, np.nan, 7]*N)
height = np.array([5, np.nan, 5]*N)
df = pd.DataFrame({'Weight': weight, 'Age': age, 'Height': height})

# Save ten large data frames to disk
for i in range(10):
    df.to_parquet(f'./df_to_impute_{i}.parquet', compression='gzip',
                  index=False)

import graphviz
import dask
import dask.dataframe as dd
from dask_ml.impute import SimpleImputer

# Read all files for imputation in a dask data frame from a specific directory
df = dd.read_parquet('./df_to_impute_*.parquet')

# Set up the imputers and columns
mean_imputer = SimpleImputer(strategy='mean')
mostfreq_imputer = SimpleImputer(strategy='most_frequent')
imputers = [mean_imputer, mostfreq_imputer]

mean_cols = ['Weight', 'Age']
freq_cols = ['Height']
columns = [mean_cols, freq_cols]

# Create a new data frame with imputed values, then visualize the computation.
df_list = []
for imputer, col in zip(imputers, columns):
    df_list.append(imputer.fit_transform(df.loc[:, col]))
imputed_df = dd.concat(df_list, axis=1)
imputed_df.visualize(filename='imputed.svg', rankdir='LR')

# Save the new data frame to disk
imputed_df.to_parquet('imputed_df.parquet', compression='gzip')

imputed_df.head()

    Weight  Age     Height
0   100.0   2.0     5.0
1   85.0    4.5     5.0
2   70.0    7.0     5.0
3   100.0   2.0     5.0
4   85.0    4.5     5.0


# Check the summary statistics make sense - 300M rows and stats as expected
imputed_df.describe().compute()

    Weight  Age     Height
count   3.000000e+08    3.000000e+08    300000000.0
mean    8.500000e+01    4.500000e+00    5.0
std     1.224745e+01    2.041241e+00    0.0
min     7.000000e+01    2.000000e+00    5.0
25%     7.000000e+01    2.000000e+00    5.0
50%     8.500000e+01    4.500000e+00    5.0
75%     1.000000e+02    7.000000e+00    5.0
max     1.000000e+02    7.000000e+00    5.0