-
Notifications
You must be signed in to change notification settings - Fork 112
/
profiler_gym_compat.py
81 lines (67 loc) · 2.44 KB
/
profiler_gym_compat.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at http://mozilla.org/MPL/2.0/.
# SPDX-License-Identifier: MPL-2.0
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.
"""
This file aims at profiling a case where the "simulate" function is heavily used.
"""
import grid2op
from grid2op.gym_compat import GymEnv
import warnings
try:
from lightsim2grid import LightSimBackend
bk_cls = LightSimBackend
nm_bk_used = "LightSimBackend"
print("LightSimBackend used")
except ImportError:
from grid2op.Backend import PandaPowerBackend
bk_cls = PandaPowerBackend
nm_bk_used = "PandaPowerBackend"
print("PandaPowerBackend used")
import os
import cProfile
import pdb
NB_SIMULATE = 10
ENV_NAME = "l2rpn_icaps_2021_small"
ENV_NAME = "l2rpn_idf_2023"
def make_env(env_name=ENV_NAME):
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
fake_env = grid2op.make(env_name, test=True)
param = fake_env.parameters
param.NO_OVERFLOW_DISCONNECTION = True
env = grid2op.make(env_name, backend=bk_cls(), param=param)
env.seed(0)
env.reset()
gym_env = GymEnv(env)
return gym_env, env
def run_env(gym_env, cp_gym_env, env, cp_env):
done = False
while not done:
act = {}
cp_gym_env.enable()
obs, reward, done, truncated, info = gym_env.step(act)
cp_gym_env.disable()
done = False
while not done:
act = env.action_space()
cp_env.enable()
obs, reward, done, info = env.step(act)
cp_env.disable()
if __name__ == "__main__":
gym_env, env = make_env()
cp_gym = cProfile.Profile()
cp_env = cProfile.Profile()
run_env(gym_env, cp_gym, env, cp_env)
nm_f, ext = os.path.splitext(__file__)
nm_out_gym = f"gym_{nm_f}_{nm_bk_used}_{ENV_NAME}_gymenv.prof"
nm_out_env = f"gym_{nm_f}_{nm_bk_used}_{ENV_NAME}_env.prof"
cp_gym.dump_stats(nm_out_gym)
cp_env.dump_stats(nm_out_env)
print("You can view profiling grid2op raw results with:\n\tsnakeviz {}".format(nm_out_env))
print("You can view profiling gym results with:\n\tsnakeviz {}".format(nm_out_gym))
# base: 66.7 s
# sans copy dans simulate: 65.2