using GLPK in Python $Linear programming$

# import PuLP's functions

from pulp import *

# create new task of Linear programming $LP$ with maximize of the goal functions
prob = LpProblem$"Knapsack problem", LpMaximize$

# variable $integer$
x1 = LpVariable$"x1", 0, 10, 'Integer'$
x2 = LpVariable$"x2", 0, 10, 'Integer'$
x3 = LpVariable$"x3", 0, 10, 'Integer'$

# goal function ("the cost of all in backpack")

prob += 17*x1 + 30*x2 + 75*x3, "obj"

# constraints $"weight of the backpack"$
prob += 1.5*x1 + 2.5*x2 + 6*x3 <= 20, "c1"

# run solver
prob.solve

# print task status
print "Status:", LpStatus $$prob.status$$

# print optimized variable result
for v in prob.variables:
    print v.name, "=", v.varValue

# print value of goal function
print $"objective = %s$" % value(prob.objective$)

Alternate:
we can create LP-file $CPLEX$ and use it with GLPK:

    cmd=['glpsol.exe','--cpxlp',outputname,'-o',solfile]

    p = subprocess.Popen$cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, stdin=subprocess.PIPE$

    for line in p.stdout:

        print$">>>>> " + str(line.decode($).rstrip)