Error at Multiprocessing - “Manage Tile Cache” tool in parallel processing

[MarkMindlin]

Hi,

We use the tool "Manage Tile Cache" on the local machine Desktop in ArcMap 10.5, for ArcGIS Server 10.3.

Parameter "area_of_interest" is the feature class created by CacheWorx "Coverage To Feature" Level 19

The help says: "For the fastest tile creation, your CPU should be working near 100% during the tile creation"

Having 16 cores CPU, and using ParallelProcessingFactor 100% or 16 

the CPU usage is very low - between 3 to 8 percents

In order to find a workaround I tried to use a script with multiprocessing.

Unfortunately, it has an error at the run time. Please help to make it working.

The script

#run “Manage Tile Cache” tool in parallel processing

import arcpy

import multiprocessing

import os

import glob

import sys

import traceback

from multiprocessing import Process, Queue, Pool, \

    cpu_count, current_process, Manager

arcpy.env.overwriteOutput = True

arcpy.env.scratchWorkspace = "in_memory"

AAA_Imagery = "D:/gisdata/imagery/public_AAA_Imagery/AAA_Imagery"

Manage_Mode = "RECREATE_ALL_TILES"

Scales__Pixel_Size___Estimated_Disk_Space_ = "564.248588"

Best_WebM = "D:/gisdata/imagery/BestMosaic_2017test.gdb/Best_WebM"

Level_20_coverage = "D:/gisdata/imagery/Levels_coverage.gdb/Level_20_coverage"

def execute_task(bundleAOI):

    try:

        result = arcpy.ManageTileCache_management(AAA_Imagery, Manage_Mode, "", Best_WebM, "ARCGISONLINE_SCHEME", "", Scales__Pixel_Size___Estimated_Disk_Space_, bundleAOI, "", "591657527.591555", "282.124294")

        print(arcpy.GetMessages())

        print "result: " + result

    except Exception, e:

        e.traceback = traceback.format_exc()

        raise

if __name__ == '__main__':

    #get individual bundles, add it to a dictonary

    bundles = {}

    count = 1

    for bundle in arcpy.da.SearchCursor(Level_20_coverage,["*"]):

        bundles[count] = bundle

        count += 1

    # create a process pool and pass dictonary of extent to execute task

    pool = Pool(processes=cpu_count())

    pool.map(execute_task, bundles.items())

    pool.close()

    pool.join()

The error (see the image)