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December! Yikes!
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if __name__ == '__main__':
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vzfunc[0]: Zfunc
d1={'Zfunc': 2.5, 'Zfunc_function': 'plus_func', 'Zvals1': 1.0, 'Zvals2': 1.5}
d2={'Zfunc': 2.5, 'Zfunc_function': 'plus_func', 'Zvals1': 1.0, 'Zvals2': 1.5}
------------
vzfunc[1]: Zfunc
d1={'Zfunc': 4.5, 'Zfunc_function': 'div_func', 'Zvals1': 4.5, 'Zvals2': 1.0}
d2={'Zfunc': 4.5, 'Zfunc_function': 'div_func', 'Zvals1': 4.5, 'Zvals2': 1.0}
------------
vzfunc[2]: Zfunc
d1={'Zfunc': 3.5, 'Zfunc_function': 'mult_func', 'Zvals1': 1.0, 'Zvals2': 3.5}
d2={'Zfunc': 3.5, 'Zfunc_function': 'mult_func', 'Zvals1': 1.0, 'Zvals2': 3.5}
------------
vzfunc[3]: Zfunc
d1={'Zfunc': 7.5, 'Zfunc_function': 'plus_func', 'Zvals1': 3.5, 'Zvals2': 4.0}
d2={'Zfunc': 7.5, 'Zfunc_function': 'plus_func', 'Zvals1': 3.5, 'Zvals2': 4.0}
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* operator before an iterable to expand it within the function call. For example:
timeseries_list = [timeseries1 timeseries2 ...]
r = scikits.timeseries.lib.reportlib.Report(*timeseries_list)def plus_func(v1:float, v2:float) -> float:
return v1 + v2
def minus_func(v1:float, v2:float) -> float:
return v1 - v2
def mult_func(v1:float, v2:float) -> float:
return v1 * v2
def div_func(v1:float, v2:float) -> float:
return v1 / v2
if __name__ == '__main__':
func_array = [plus_func, minus_func, mult_func, div_func]
vf = EvolveAxis("func", ValueAxisType.FUNCTION, range_array=func_array)
v1 = EvolveAxis("X", ValueAxisType.FLOAT, parent=vf, min=-5, max=5, step=0.25)
v2 = EvolveAxis("Y", ValueAxisType.FLOAT, parent=vf, min=-5, max=5, step=0.25)
for f in func_array:
result = vf.get_random_val()
print("------------\nresult = {}\n{}".format(result, vf.to_string()))
------------ result = -1.0 func: cur_value = div_func X: cur_value = -1.75 Y: cur_value = 1.75 ------------ result = -2.75 func: cur_value = plus_func X: cur_value = -0.25 Y: cur_value = -2.5 ------------ result = 3.375 func: cur_value = mult_func X: cur_value = -0.75 Y: cur_value = -4.5 ------------ result = -5.0 func: cur_value = div_func X: cur_value = -3.75 Y: cur_value = 0.75
# create a set of functions that all take two arguments
func_array = [plus_func, minus_func, mult_func, div_func]
vf = EvolveAxis("func", ValueAxisType.FUNCTION, range_array=func_array)
v1 = EvolveAxis("X", ValueAxisType.FLOAT, parent=vf, min=-5, max=5, step=0.25)
v2 = EvolveAxis("Y", ValueAxisType.FLOAT, parent=vf, min=-5, max=5, step=0.25)
# create a single function that takes no arguments
vp = EvolveAxis("random", ValueAxisType.FUNCTION, range_array=[random.random])
# create a set of Axis from the previous function evolve args
axis_list = [vf, vp]
vv = EvolveAxis("meta", ValueAxisType.VALUEAXIS, range_array=axis_list)
# run four times
for i in range(4):
result = vv.get_random_val()
print("------------\nresult = {}\n{}".format(result, vv.to_string()))
------------ result = 0.03223958125899473 meta: cur_value = 0.8840652389671935 ------------ result = -0.75 meta: cur_value = -0.75 ------------ result = -3.5 meta: cur_value = -3.5 ------------ result = 0.7762888191296017 meta: cur_value = 0.13200324934487906
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import logging
import concurrent.futures
import threading
import time
def thread_function(name):
logging.info("Task %s: starting on thread %s", name, threading.current_thread().name)
time.sleep(2)
logging.info("Task %s: finishing on thread %s", name, threading.current_thread().name)
if __name__ == "__main__":
num_tasks = 5
num_gpus = 1
format = "%(asctime)s: %(message)s"
logging.basicConfig(format=format, level=logging.INFO,
datefmt="%H:%M:%S")
with concurrent.futures.ThreadPoolExecutor(max_workers=num_gpus) as executor:
result = executor.map(thread_function, range(num_tasks))
logging.info("Main : all done")
As you can see, it’s possible to have a thread for each gpu, while having them iterate over a larger set of tasks. Now I need to extract the gpu name from the thread info. In other words, ThreadPoolExecutor-0_0 needs to map to gpu:1.
import concurrent.futures
import threading
import time
from typing import List
import re
last_num_in_str_re = '(\d+)(?!.*\d)'
prog = re.compile(last_num_in_str_re)
def thread_function(args:List):
num = prog.search(threading.current_thread().name) # get the last number in a string
gpu_str = "gpu:{}".format(int(num.group(0))+1)
print("{}: starting on {}".format(args["name"], gpu_str))
time.sleep(2)
print("{}: finishing on {}".format(args["name"], gpu_str))
if __name__ == "__main__":
num_tasks = 5
num_gpus = 5
task_list = []
for i in range(num_tasks):
task = {"name":"task_{}".format(i), "value":2+(i/10)}
task_list.append(task)
with concurrent.futures.ThreadPoolExecutor(max_workers=num_gpus) as executor:
result = executor.map(thread_function, task_list)
print("Finished Main")
And that gives me:
task_0: starting on gpu:1 task_1: starting on gpu:2 task_0: finishing on gpu:1, after sleeping 2.0 seconds task_2: starting on gpu:1 task_1: finishing on gpu:2, after sleeping 2.1 seconds task_3: starting on gpu:2 task_2: finishing on gpu:1, after sleeping 2.2 seconds task_4: starting on gpu:1 task_3: finishing on gpu:2, after sleeping 2.3 seconds task_4: finishing on gpu:1, after sleeping 2.4 seconds Finished Main
So the only think left is to integrate this into TimeSeriesMl2
viztales 
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