Profiling of maze_dataset serializing/loading/saving/reading¶
In [1]:
import copy
import itertools
import warnings
from typing import Any, Callable, Sequence
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from muutils.statcounter import StatCounter
from muutils.timeit_fancy import FancyTimeitResult, timeit_fancy
from maze_dataset import (
MazeDataset,
MazeDatasetConfig,
set_serialize_minimal_threshold,
)
from maze_dataset.generation.generators import GENERATORS_MAP
Generate Datasets¶
In [2]:
cfgs: list[MazeDatasetConfig] = [
MazeDatasetConfig(
name="test",
grid_n=grid_n,
n_mazes=n_mazes,
maze_ctor=GENERATORS_MAP["gen_dfs"],
)
for grid_n, n_mazes in itertools.product(
[10],
np.logspace(1, 2, 2, dtype=int).tolist(), # 100, for CI tests
# np.logspace(1, 3, 5, dtype=int).tolist(), # 1k
# np.logspace(0, 4, 9, dtype=int).tolist(), # 10k, notebook results from this set
)
]
datasets: list[MazeDataset] = [
MazeDataset.from_config(cfg, load_local=False) for cfg in cfgs
]
Profile¶
In [3]:
columns: list[str] = [
"grid_n",
"n_mazes",
"serialize",
"serialize_minimal",
"load",
"load_minimal",
"save",
"save_minimal",
"read",
"read_minimal",
]
speeds_data: list[dict] = list()
In [4]:
def wrapped_timeit_fancy(
name: str,
function: Callable,
do_profiling: bool,
repeats: int,
timing_stat: Callable[[StatCounter], float],
) -> tuple[dict, Any]:
output: dict = dict()
result: FancyTimeitResult = timeit_fancy(
function,
get_return=True,
do_profiling=do_profiling,
repeats=repeats,
)
output[name] = timing_stat(result.timings)
output[f"{name}:stats"] = result.timings
if do_profiling:
output[f"{name}:profiling"] = result.profile
return output, result.return_value
def measure_dataset_speed(
d: MazeDataset,
do_profiling: bool = True,
repeats: int = 1,
timing_stat: Callable[[StatCounter], float] = StatCounter.min,
) -> dict:
if repeats > 1:
warnings.warn(
"Repeats > 1, results might not be accurate due to generation metadata being collected.",
)
kwargs_fancy_timeit: dict = dict(
do_profiling=do_profiling,
timing_stat=timing_stat,
repeats=repeats,
)
set_serialize_minimal_threshold(None)
_d_cpy: MazeDataset = copy.deepcopy(d)
# set up row data
row_data: dict = dict(
grid_n=d.cfg.grid_n,
n_mazes=d.cfg.n_mazes,
)
# serialization & loading
info_serialize, result_serialize = wrapped_timeit_fancy(
"serialize_full",
_d_cpy._serialize_full,
**kwargs_fancy_timeit,
)
row_data.update(info_serialize)
_d_cpy = copy.deepcopy(d)
info_serialize_min, result_serialize_min = wrapped_timeit_fancy(
"serialize_minimal",
_d_cpy._serialize_minimal,
**kwargs_fancy_timeit,
)
row_data.update(info_serialize_min)
_d_cpy = copy.deepcopy(d)
# info_serialize_min_alt, result_serialize_min_alt = wrapped_timeit_fancy(
# 'serialize_minimal_alt', _d_cpy._serialize_minimal_alt, **kwargs_fancy_timeit
# )
# row_data.update(info_serialize_min_alt)
_d_cpy = copy.deepcopy(d)
info_serialize_cat, result_serialize_cat = wrapped_timeit_fancy(
"serialize_minimal_soln_cat",
_d_cpy._serialize_minimal_soln_cat,
**kwargs_fancy_timeit,
)
row_data.update(info_serialize_cat)
_d_cpy = copy.deepcopy(d)
row_data.update(
wrapped_timeit_fancy(
"load_legacy",
lambda: MazeDataset._load_legacy(result_serialize),
**kwargs_fancy_timeit,
)[0],
)
row_data.update(
wrapped_timeit_fancy(
"load_full",
lambda: MazeDataset._load_full(result_serialize),
**kwargs_fancy_timeit,
)[0],
)
row_data.update(
wrapped_timeit_fancy(
"load_minimal",
lambda: MazeDataset._load_minimal(result_serialize_min),
**kwargs_fancy_timeit,
)[0],
)
row_data.update(
wrapped_timeit_fancy(
"load_minimal_soln_cat",
lambda: MazeDataset._load_minimal_soln_cat(result_serialize_cat),
**kwargs_fancy_timeit,
)[0],
)
row_data.update(
wrapped_timeit_fancy(
"load_full",
lambda: MazeDataset._load_full(result_serialize),
**kwargs_fancy_timeit,
)[0],
)
row_data.update(
wrapped_timeit_fancy(
"load_minimal",
lambda: MazeDataset._load_minimal(result_serialize_min),
**kwargs_fancy_timeit,
)[0],
)
row_data.update(
wrapped_timeit_fancy(
"load_minimal_soln_cat",
lambda: MazeDataset._load_minimal_soln_cat(result_serialize_cat),
**kwargs_fancy_timeit,
)[0],
)
# saving and loading
path_default: str = f"../data/{d.cfg.to_fname()}.zanj"
path_min: str = f"../data/{d.cfg.to_fname()}_min.zanj"
# default
set_serialize_minimal_threshold(None)
_d_cpy = copy.deepcopy(d)
row_data.update(
wrapped_timeit_fancy(
"save",
lambda: _d_cpy.save(file_path=path_default),
**kwargs_fancy_timeit,
)[0],
)
_d_cpy = copy.deepcopy(d)
# read_legacy
set_serialize_minimal_threshold(-1)
row_data.update(
wrapped_timeit_fancy(
"read_legacy",
lambda: MazeDataset.read(file_path=path_default),
**kwargs_fancy_timeit,
)[0],
)
# default read
set_serialize_minimal_threshold(None)
row_data.update(
wrapped_timeit_fancy(
"read",
lambda: MazeDataset.read(file_path=path_default),
**kwargs_fancy_timeit,
)[0],
)
# minimal
set_serialize_minimal_threshold(0)
_d_cpy = copy.deepcopy(d)
row_data.update(
wrapped_timeit_fancy(
"save_minimal",
lambda: _d_cpy.save(file_path=path_min),
**kwargs_fancy_timeit,
)[0],
)
_d_cpy = copy.deepcopy(d)
row_data.update(
wrapped_timeit_fancy(
"read_minimal",
lambda: MazeDataset.read(file_path=path_min),
**kwargs_fancy_timeit,
)[0],
)
# asserts
# assert d == read_default
# assert d == read_minimal
# reset cfg?
set_serialize_minimal_threshold(None)
return row_data
Run Profiling¶
In [5]:
for i, d in enumerate(datasets):
print(f"Profiling {i + 1}/{len(datasets)}:\t{d.cfg}")
result = measure_dataset_speed(d)
speeds_data.append(result)
cols_short: str = str({k: v for k, v in result.items() if ":" not in k})
print(f"\t{cols_short}")
print(f"\t{d.cfg!s}")
Profiling 1/2: MazeDatasetConfig(name='test', seq_len_min=1, seq_len_max=512, seed=42, applied_filters=[], grid_n=10, n_mazes=10, maze_ctor=<function LatticeMazeGenerators.gen_dfs at 0x7aac4c8ca2a0>, maze_ctor_kwargs={}, endpoint_kwargs={})
{'grid_n': 10, 'n_mazes': 10, 'serialize_full': 0.0018484899992472492, 'serialize_minimal': 0.003223842999432236, 'serialize_minimal_soln_cat': 0.0014313210049294867, 'load_legacy': 0.005206369998631999, 'load_full': 0.001840072996856179, 'load_minimal': 0.000822582995169796, 'load_minimal_soln_cat': 0.003138806998322252, 'save': 0.046455591000267304, 'read_legacy': 0.018541731005825568, 'read': 0.01719445599883329, 'save_minimal': 0.0133227159967646, 'read_minimal': 0.0011167789998580702}
MazeDatasetConfig(name='test', seq_len_min=1, seq_len_max=512, seed=42, applied_filters=[], grid_n=10, n_mazes=10, maze_ctor=<function LatticeMazeGenerators.gen_dfs at 0x7aac4c8ca2a0>, maze_ctor_kwargs={}, endpoint_kwargs={})
Profiling 2/2: MazeDatasetConfig(name='test', seq_len_min=1, seq_len_max=512, seed=42, applied_filters=[{'name': 'collect_generation_meta', 'args': (), 'kwargs': {}}], grid_n=10, n_mazes=100, maze_ctor=<function LatticeMazeGenerators.gen_dfs at 0x7aac4c8ca2a0>, maze_ctor_kwargs={}, endpoint_kwargs={})
{'grid_n': 10, 'n_mazes': 100, 'serialize_full': 0.0010271579958498478, 'serialize_minimal': 0.001022066004225053, 'serialize_minimal_soln_cat': 0.001120972003263887, 'load_legacy': 0.009384162003698293, 'load_full': 0.007476587001292501, 'load_minimal': 0.0011957349997828715, 'load_minimal_soln_cat': 0.004773883003508672, 'save': 0.10615306399995461, 'read_legacy': 0.020691411002189852, 'read': 0.012401505002344493, 'save_minimal': 0.014303780000773259, 'read_minimal': 0.0020644929973059334}
MazeDatasetConfig(name='test', seq_len_min=1, seq_len_max=512, seed=42, applied_filters=[{'name': 'collect_generation_meta', 'args': (), 'kwargs': {}}], grid_n=10, n_mazes=100, maze_ctor=<function LatticeMazeGenerators.gen_dfs at 0x7aac4c8ca2a0>, maze_ctor_kwargs={}, endpoint_kwargs={})
Results¶
In [6]:
SPEEDS: pd.DataFrame = pd.DataFrame(speeds_data)
SPEEDS
Out[6]:
| grid_n | n_mazes | serialize_full | serialize_full:stats | serialize_full:profiling | serialize_minimal | serialize_minimal:stats | serialize_minimal:profiling | serialize_minimal_soln_cat | serialize_minimal_soln_cat:stats | ... | read_legacy:profiling | read | read:stats | read:profiling | save_minimal | save_minimal:stats | save_minimal:profiling | read_minimal | read_minimal:stats | read_minimal:profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10 | 10 | 0.001848 | {0.0018484899992472492: 1} | <pstats.Stats object at 0x7aad5a182690> | 0.003224 | {0.003223842999432236: 1} | <pstats.Stats object at 0x7aac4912ac00> | 0.001431 | {0.0014313210049294867: 1} | ... | <pstats.Stats object at 0x7aac4912a4b0> | 0.017194 | {0.01719445599883329: 1} | <pstats.Stats object at 0x7aac490201d0> | 0.013323 | {0.0133227159967646: 1} | <pstats.Stats object at 0x7aac49162cf0> | 0.001117 | {0.0011167789998580702: 1} | <pstats.Stats object at 0x7aad117235c0> |
| 1 | 10 | 100 | 0.001027 | {0.0010271579958498478: 1} | <pstats.Stats object at 0x7aac49163f80> | 0.001022 | {0.001022066004225053: 1} | <pstats.Stats object at 0x7aac49129c40> | 0.001121 | {0.001120972003263887: 1} | ... | <pstats.Stats object at 0x7aac49023110> | 0.012402 | {0.012401505002344493: 1} | <pstats.Stats object at 0x7aac49161e50> | 0.014304 | {0.014303780000773259: 1} | <pstats.Stats object at 0x7aac4d392660> | 0.002064 | {0.0020644929973059334: 1} | <pstats.Stats object at 0x7aac48f31040> |
2 rows × 38 columns
In [7]:
def compute_speedups(speeds: pd.DataFrame) -> pd.DataFrame:
# for prefix in column_measurement_prefixes:
# speeds[f'{prefix}_speedup'] = speeds[f'{prefix}_full'] / speeds[f'{prefix}_minimal']
speeds["serialize/speedup"] = speeds["serialize_full"] / speeds["serialize_minimal"]
speeds["load/speedup"] = speeds["load_full"] / speeds["load_minimal"]
speeds["save/speedup"] = speeds["save"] / speeds["save_minimal"]
speeds["read/speedup"] = speeds["read"] / speeds["read_minimal"]
return speeds
SPEEDS = compute_speedups(SPEEDS)
In [8]:
SPEEDS: pd.DataFrame = pd.DataFrame(speeds_data)
# SPEEDS.loc[:,"load_legacy":"load_minimal_soln_cat:profiling"]
SPEEDS.loc[:, "read_legacy":"read:profiling"]
Out[8]:
| read_legacy | read_legacy:stats | read_legacy:profiling | read | read:stats | read:profiling | |
|---|---|---|---|---|---|---|
| 0 | 0.018542 | {0.018541731005825568: 1} | <pstats.Stats object at 0x7aac4912a4b0> | 0.017194 | {0.01719445599883329: 1} | <pstats.Stats object at 0x7aac490201d0> |
| 1 | 0.020691 | {0.020691411002189852: 1} | <pstats.Stats object at 0x7aac49023110> | 0.012402 | {0.012401505002344493: 1} | <pstats.Stats object at 0x7aac49161e50> |
In [9]:
SPEEDS.columns
Out[9]:
Index(['grid_n', 'n_mazes', 'serialize_full', 'serialize_full:stats',
'serialize_full:profiling', 'serialize_minimal',
'serialize_minimal:stats', 'serialize_minimal:profiling',
'serialize_minimal_soln_cat', 'serialize_minimal_soln_cat:stats',
'serialize_minimal_soln_cat:profiling', 'load_legacy',
'load_legacy:stats', 'load_legacy:profiling', 'load_full',
'load_full:stats', 'load_full:profiling', 'load_minimal',
'load_minimal:stats', 'load_minimal:profiling', 'load_minimal_soln_cat',
'load_minimal_soln_cat:stats', 'load_minimal_soln_cat:profiling',
'save', 'save:stats', 'save:profiling', 'read_legacy',
'read_legacy:stats', 'read_legacy:profiling', 'read', 'read:stats',
'read:profiling', 'save_minimal', 'save_minimal:stats',
'save_minimal:profiling', 'read_minimal', 'read_minimal:stats',
'read_minimal:profiling'],
dtype='object')
In [10]:
def compute_speedups(speeds: pd.DataFrame) -> pd.DataFrame:
# for prefix in column_measurement_prefixes:
# speeds[f'{prefix}_speedup'] = speeds[f'{prefix}_full'] / speeds[f'{prefix}_minimal']
speeds["serialize/speedup"] = speeds["serialize_full"] / speeds["serialize_minimal"]
speeds["load_minimal/speedup"] = speeds["load_legacy"] / speeds["load_minimal"]
speeds["load/speedup"] = speeds["load_legacy"] / speeds["load_full"]
speeds["save/speedup"] = speeds["save"] / speeds["save_minimal"]
speeds["read_minimal/speedup"] = speeds["read_legacy"] / speeds["read_minimal"]
speeds["read/speedup"] = speeds["read_legacy"] / speeds["read"]
return speeds
SPEEDS = compute_speedups(SPEEDS)
In [11]:
SPEEDS[[c for c in SPEEDS.columns if ":" not in c]]
Out[11]:
| grid_n | n_mazes | serialize_full | serialize_minimal | serialize_minimal_soln_cat | load_legacy | load_full | load_minimal | load_minimal_soln_cat | save | read_legacy | read | save_minimal | read_minimal | serialize/speedup | load_minimal/speedup | load/speedup | save/speedup | read_minimal/speedup | read/speedup | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10 | 10 | 0.001848 | 0.003224 | 0.001431 | 0.005206 | 0.001840 | 0.000823 | 0.003139 | 0.046456 | 0.018542 | 0.017194 | 0.013323 | 0.001117 | 0.573381 | 6.329294 | 2.829437 | 3.486946 | 16.602865 | 1.078355 |
| 1 | 10 | 100 | 0.001027 | 0.001022 | 0.001121 | 0.009384 | 0.007477 | 0.001196 | 0.004774 | 0.106153 | 0.020691 | 0.012402 | 0.014304 | 0.002064 | 1.004982 | 7.848028 | 1.255140 | 7.421329 | 10.022515 | 1.668460 |
In [12]:
def plot_speeds(
speeds: pd.DataFrame,
column_measurement_prefixes: Sequence[str] = ("serialize", "load", "save", "read"),
) -> None:
n_measurements: int = len(column_measurement_prefixes)
fig, axs = plt.subplots(2, n_measurements, figsize=(n_measurements * 5, 10))
unique_grid_ns: list[int] = speeds["grid_n"].unique().tolist()
for i, prefix in enumerate(column_measurement_prefixes):
print(f"Plotting {prefix} timings and speedups")
for grid_n in unique_grid_ns:
print(f"Plotting grid_n={grid_n}")
# raw timings
ax_timings = axs[0, i]
speeds_masked = speeds[speeds["grid_n"] == grid_n].sort_values("n_mazes")
x_n_mazes = speeds_masked["n_mazes"]
# Plotting
for col in speeds_masked.columns:
if (prefix in col) and ("speedup" not in col) and (":" not in col):
ax_timings.plot(
x_n_mazes,
speeds_masked[col],
"x-",
label=f"grid_n={grid_n}, {col}",
)
# Setting multiple properties with `set`
ax_timings.set(
xscale="log",
yscale="log",
xlabel="Number of mazes",
ylabel="Runtime [sec]",
title=f"{prefix} timings",
)
ax_timings.legend()
# speedups
ax_speedups = axs[1, i]
col_name: str = (
f"{prefix}" if prefix in ("serialize", "save") else f"{prefix}_minimal"
)
ax_speedups.plot(
x_n_mazes,
speeds_masked[f"{col_name}/speedup"],
"x-",
label=f"grid_n={grid_n}",
)
# Setting multiple properties with `set` for ax_speedups
ax_speedups.set(
xscale="log",
yscale="log",
xlabel="Number of mazes",
ylabel="Speedup",
title=f"{col_name} speedups",
)
ax_speedups.plot(
x_n_mazes,
speeds_masked[f"{prefix}/speedup"],
"x-",
label=f"grid_n={grid_n}",
)
# Setting multiple properties with `set` for ax_speedups
ax_speedups.set(
xscale="log",
yscale="log",
xlabel="Number of mazes",
ylabel="Speedup",
title=f"{prefix} speedups",
)
ax_speedups.legend()
plot_speeds(SPEEDS)
Plotting serialize timings and speedups Plotting grid_n=10 Plotting load timings and speedups Plotting grid_n=10 Plotting save timings and speedups Plotting grid_n=10 Plotting read timings and speedups Plotting grid_n=10
Speedups plotted on the bottom set of axes all show the _minimal compared to the legacy performance. serialize_full and save are unchanged from the legacy version, so speedups are plotted relative to those vectors.
In [13]:
SPEEDS[["grid_n", "n_mazes", "serialize_minimal:profiling"]]
Out[13]:
| grid_n | n_mazes | serialize_minimal:profiling | |
|---|---|---|---|
| 0 | 10 | 10 | <pstats.Stats object at 0x7aac4912ac00> |
| 1 | 10 | 100 | <pstats.Stats object at 0x7aac49129c40> |
In [14]:
SPEEDS["load_minimal:profiling"][len(SPEEDS) - 1].sort_stats("tottime").print_stats()
2994 function calls (2943 primitive calls) in 0.002 seconds
Ordered by: internal time
ncalls tottime percall cumtime percall filename:lineno(function)
100 0.000 0.000 0.000 0.000 lattice_maze.py:1188(__post_init__)
500 0.000 0.000 0.000 0.000 {built-in method numpy.array}
100 0.000 0.000 0.001 0.000 lattice_maze.py:1271(__init__)
100 0.000 0.000 0.001 0.000 <string>:2(__init__)
1 0.000 0.000 0.002 0.002 maze_dataset.py:677(_load_minimal)
400 0.000 0.000 0.000 0.000 lattice_maze.py:165(<lambda>)
35 0.000 0.000 0.000 0.000 traceback.py:460(format_frame_summary)
1 0.000 0.000 0.000 0.000 traceback.py:399(_extract_from_extended_frame_gen)
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
166 0.000 0.000 0.000 0.000 traceback.py:318(line)
19 0.000 0.000 0.000 0.000 {built-in method posix.stat}
202/152 0.000 0.000 0.000 0.000 {built-in method builtins.isinstance}
198 0.000 0.000 0.000 0.000 {method 'strip' of 'str' objects}
67 0.000 0.000 0.000 0.000 {method 'format' of 'str' objects}
36 0.000 0.000 0.000 0.000 traceback.py:327(walk_stack)
1 0.000 0.000 0.001 0.001 serializable_dataclass.py:776(load)
172 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
1 0.000 0.000 0.000 0.000 traceback.py:525(format)
3 0.000 0.000 0.000 0.000 loading.py:242(get_item_loader)
10 0.000 0.000 0.000 0.000 serializable_dataclass.py:153(SerializableDataclass__validate_field_type)
201 0.000 0.000 0.000 0.000 {built-in method builtins.len}
35 0.000 0.000 0.000 0.000 linecache.py:26(getline)
23 0.000 0.000 0.000 0.000 linecache.py:52(checkcache)
1 0.000 0.000 0.001 0.001 mlutils.py:77(set_reproducibility)
1 0.000 0.000 0.001 0.001 random.py:32(manual_seed)
35 0.000 0.000 0.000 0.000 {method 'add' of 'set' objects}
35 0.000 0.000 0.000 0.000 linecache.py:149(lazycache)
35 0.000 0.000 0.000 0.000 linecache.py:36(getlines)
8/7 0.000 0.000 0.000 0.000 validate_type.py:58(validate_type)
25 0.000 0.000 0.000 0.000 typing.py:1492(__subclasscheck__)
36 0.000 0.000 0.000 0.000 traceback.py:391(extended_frame_gen)
1 0.000 0.000 0.000 0.000 traceback.py:441(from_list)
1 0.000 0.000 0.000 0.000 serializable_dataclass.py:245(SerializableDataclass__validate_fields_types__dict)
25 0.000 0.000 0.000 0.000 typing.py:1221(__instancecheck__)
25 0.000 0.000 0.000 0.000 {built-in method builtins.issubclass}
2 0.000 0.000 0.000 0.000 dataclasses.py:1278(fields)
35 0.000 0.000 0.000 0.000 {method 'join' of 'str' objects}
8 0.000 0.000 0.000 0.000 serializable_dataclass.py:501(get_cls_type_hints)
32 0.000 0.000 0.000 0.000 traceback.py:312(_original_line)
2 0.000 0.000 0.000 0.000 {method 'manual_seed' of 'torch._C.Generator' objects}
25 0.000 0.000 0.000 0.000 <frozen abc>:121(__subclasscheck__)
10 0.000 0.000 0.000 0.000 serializable_dataclass.py:366(validate_field_type)
1 0.000 0.000 0.000 0.000 {function Random.seed at 0x7aad65ff22a0}
35 0.000 0.000 0.000 0.000 traceback.py:265(__init__)
1 0.000 0.000 0.000 0.000 random.py:135(seed)
32 0.000 0.000 0.000 0.000 {method 'lstrip' of 'str' objects}
1 0.000 0.000 0.001 0.001 traceback.py:214(format_stack)
8 0.000 0.000 0.000 0.000 {built-in method builtins.hasattr}
3 0.000 0.000 0.000 0.000 loading.py:270(load_item_recursive)
25 0.000 0.000 0.000 0.000 {built-in method _abc._abc_subclasscheck}
1 0.000 0.000 0.000 0.000 random.py:122(cb)
1 0.000 0.000 0.000 0.000 <frozen os>:717(__setitem__)
18 0.000 0.000 0.000 0.000 serializable_dataclass.py:129(<lambda>)
1 0.000 0.000 0.000 0.000 traceback.py:221(extract_stack)
1 0.000 0.000 0.001 0.001 __init__.py:79(_lazy_call)
1 0.000 0.000 0.000 0.000 traceback.py:377(extract)
1 0.000 0.000 0.000 0.000 traceback.py:29(format_list)
11 0.000 0.000 0.000 0.000 {built-in method builtins.getattr}
11 0.000 0.000 0.000 0.000 errormode.py:162(from_any)
2 0.000 0.000 0.000 0.000 <frozen os>:791(encode)
22 0.000 0.000 0.000 0.000 dataclasses.py:1293(<genexpr>)
1 0.000 0.000 0.000 0.000 random.py:88(_seed_custom_device)
14 0.000 0.000 0.000 0.000 {method 'get' of 'dict' objects}
1 0.000 0.000 0.000 0.000 {built-in method posix.putenv}
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Out[14]:
<pstats.Stats at 0x7aac4ca2cb00>
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