Note
Click here to download the full example code
Benchmark, comparison scikit-learn - onnxruntime-training#
The benchmark compares the processing time between scikit-learn and onnxruntime-training on a linear regression and a neural network. It uses the model trained in Train a scikit-learn neural network with onnxruntime-training on GPU.
First comparison: neural network#
import warnings
from pprint import pprint
import time
import numpy
import matplotlib.pyplot as plt
from pandas import DataFrame
from onnxruntime import get_device
from pyquickhelper.pycode.profiling import profile, profile2graph
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from mlprodict.onnx_conv import to_onnx
from onnxcustom.utils.orttraining_helper import (
add_loss_output, get_train_initializer)
from onnxcustom.training.optimizers import OrtGradientOptimizer
X, y = make_regression(1000, n_features=100, bias=2)
X = X.astype(numpy.float32)
y = y.astype(numpy.float32)
X_train, X_test, y_train, y_test = train_test_split(X, y)
Benchmark function.
def benchmark(skl_model, train_session, name, verbose=True):
"""
:param skl_model: model from scikit-learn
:param train_session: instance of OrtGradientOptimizer
:param name: experiment name
:param verbose: to debug
"""
print(f"[benchmark] {name}")
begin = time.perf_counter()
skl_model.fit(X, y)
duration_skl = time.perf_counter() - begin
length_skl = len(skl_model.loss_curve_)
print(
f"[benchmark] skl={length_skl!r} iterations - {duration_skl!r} seconds")
begin = time.perf_counter()
train_session.fit(X, y)
duration_ort = time.perf_counter() - begin
length_ort = len(train_session.train_losses_)
print(
f"[benchmark] ort={length_ort!r} iterations - {duration_ort!r} seconds")
return dict(skl=duration_skl, ort=duration_ort, name=name,
iter_skl=length_skl, iter_ort=length_ort,
losses_skl=skl_model.loss_curve_,
losses_ort=train_session.train_losses_)
Common parameters and model
batch_size = 15
max_iter = 100
nn = MLPRegressor(hidden_layer_sizes=(50, 10), max_iter=max_iter,
solver='sgd', learning_rate_init=5e-4, alpha=0,
n_iter_no_change=max_iter * 3, batch_size=batch_size,
nesterovs_momentum=False, momentum=0,
learning_rate='invscaling')
with warnings.catch_warnings():
warnings.simplefilter('ignore')
nn.fit(X_train, y_train)
Conversion to ONNX and trainer initialization
onx = to_onnx(nn, X_train[:1].astype(numpy.float32), target_opset=15)
onx_train = add_loss_output(onx)
weights = get_train_initializer(onx)
pprint(list((k, v[0].shape) for k, v in weights.items()))
train_session = OrtGradientOptimizer(
onx_train, list(weights), device='cpu', learning_rate=1e-5,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
benches = [benchmark(nn, train_session, name='NN-CPU')]
[('coefficient', (100, 50)),
('intercepts', (1, 50)),
('coefficient1', (50, 10)),
('intercepts1', (1, 10)),
('coefficient2', (10, 1)),
('intercepts2', (1, 1))]
[benchmark] NN-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 9.01983135798946 seconds
[benchmark] ort=100 iterations - 3.5001193080097437 seconds
Profiling#
def clean_name(text):
pos = text.find('onnxruntime')
if pos >= 0:
return text[pos:]
pos = text.find('onnxcustom')
if pos >= 0:
return text[pos:]
pos = text.find('site-packages')
if pos >= 0:
return text[pos:]
return text
ps = profile(lambda: benchmark(nn, train_session, name='NN-CPU'))[0]
root, nodes = profile2graph(ps, clean_text=clean_name)
text = root.to_text()
print(text)
[benchmark] NN-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 10.36645060707815 seconds
[benchmark] ort=100 iterations - 3.5928091478999704 seconds
isfunction -- 6 6 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/inspect.py:159:isfunction (isfunction)
name -- 101 101 -- 0.00003 0.00003 -- /usr/local/lib/python3.9/inspect.py:2565:name (name)
kind -- 82 82 -- 0.00003 0.00003 -- /usr/local/lib/python3.9/inspect.py:2577:kind (kind)
parameters -- 7 7 -- 0.00000 0.00000 -- /usr/local/lib/python3.9/inspect.py:2882:parameters (parameters)
signature -- 3 3 -- 0.00001 0.00100 -- /usr/local/lib/python3.9/inspect.py:3128:signature (signature)
from_callable -- 3 3 -- 0.00001 0.00099 -- /usr/local/lib/python3.9/inspect.py:2876:from_callable (from_callable)
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isfunction -- 3 3 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/inspect.py:159:isfunction (isfunction) +++
unwrap -- 3 3 -- 0.00002 0.00003 -- /usr/local/lib/python3.9/inspect.py:494:unwrap (unwrap)
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isfunction -- 3 3 -- 0.00000 0.00000 -- /usr/local/lib/python3.9/inspect.py:159:isfunction (isfunction) +++
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name -- 34 34 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/inspect.py:2565:name (name) +++
<method 'append' of 'list' objects> -- 34 34 -- 0.00002 0.00002 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
<method 'get' of 'dict' objects> -- 58 58 -- 0.00002 0.00002 -- ~:0:<method 'get' of 'dict' objects> (<method 'get' of 'dict' objects>) +++
filter -- 18 18 -- 0.00006 0.00015 -- /usr/local/lib/python3.9/logging/__init__.py:787:filter (filter)
filter -- 12 12 -- 0.00002 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:351:filter (filter)
filter -- 6 6 -- 0.00004 0.00005 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:484:filter (filter)
<built-in method builtins.isinstance> -- 18 18 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
<built-in method builtins.hasattr> -- 18 18 -- 0.00002 0.00002 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
acquire -- 30 30 -- 0.00006 0.00009 -- /usr/local/lib/python3.9/logging/__init__.py:892:acquire (acquire)
<method 'acquire' of '_thread.RLock' objects> -- 30 30 -- 0.00003 0.00003 -- ~:0:<method 'acquire' of '_thread.RLock' objects> (<method 'acquire' of '_thread.RLock' objects>)
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<method 'release' of '_thread.RLock' objects> -- 30 30 -- 0.00001 0.00001 -- ~:0:<method 'release' of '_thread.RLock' objects> (<method 'release' of '_thread.RLock' objects>)
emit -- 12 12 -- 0.00007 0.00091 -- /usr/local/lib/python3.9/logging/__init__.py:1067:emit (emit)
format -- 12 12 -- 0.00003 0.00046 -- /usr/local/lib/python3.9/logging/__init__.py:912:format (format)
format -- 12 12 -- 0.00008 0.00043 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:531:format (format)
format -- 12 12 -- 0.00007 0.00032 -- /usr/local/lib/python3.9/logging/__init__.py:646:format (format)
usesTime -- 12 12 -- 0.00002 0.00007 -- /usr/local/lib/python3.9/logging/__init__.py:624:usesTime (usesTime)
usesTime -- 12 12 -- 0.00003 0.00005 -- /usr/local/lib/python3.9/logging/__init__.py:417:usesTime (usesTime)
<method 'find' of 'str' objects> -- 12 12 -- 0.00002 0.00002 -- ~:0:<method 'find' of 'str' objects> (<method 'find' of 'str' objects>)
formatMessage -- 12 12 -- 0.00002 0.00008 -- /usr/local/lib/python3.9/logging/__init__.py:630:formatMessage (formatMessage)
format -- 12 12 -- 0.00002 0.00006 -- /usr/local/lib/python3.9/logging/__init__.py:428:format (format)
_format -- 12 12 -- 0.00004 0.00004 -- /usr/local/lib/python3.9/logging/__init__.py:425:_format (_format)
getMessage -- 12 12 -- 0.00005 0.00011 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:89:getMessage (getMessage)
getMessage -- 12 12 -- 0.00004 0.00004 -- /usr/local/lib/python3.9/logging/__init__.py:354:getMessage (getMessage)
<built-in method builtins.getattr> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>) +++
colorize -- 2 2 -- 0.00001 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/console.py:72:colorize (colorize)
escseq -- 4 4 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/console.py:73:escseq (escseq)
<built-in method builtins.getattr> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>) +++
flush -- 12 12 -- 0.00006 0.00032 -- /usr/local/lib/python3.9/logging/__init__.py:1056:flush (flush)
acquire -- 12 12 -- 0.00002 0.00003 -- /usr/local/lib/python3.9/logging/__init__.py:892:acquire (acquire) +++
release -- 12 12 -- 0.00002 0.00003 -- /usr/local/lib/python3.9/logging/__init__.py:899:release (release) +++
flush -- 6 6 -- 0.00002 0.00019 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:557:flush (flush)
<method 'flush' of '_io.TextIOWrapper' objects> -- 6 6 -- 0.00017 0.00017 -- ~:0:<method 'flush' of '_io.TextIOWrapper' objects> (<method 'flush' of '_io.TextIOWrapper' objects>)
<built-in method builtins.hasattr> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>) +++
write -- 6 6 -- 0.00002 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:549:write (write)
write -- 6 6 -- 0.00002 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:567:write (write)
isEnabledFor -- 12 12 -- 0.00002 0.00002 -- /usr/local/lib/python3.9/logging/__init__.py:1677:isEnabledFor (isEnabledFor)
__init__ -- 2 2 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/warnings.py:403:__init__ (__init__)
<lambda> -- 1 1 -- 0.00000 13.96119 -- onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_orttraining_benchmark.py:124:<lambda> (<lambda>)
benchmark -- 1 1 -- 0.00011 13.96118 -- onnxcustom/onnxcustom_UT_39_std/_doc/examples/plot_orttraining_benchmark.py:46:benchmark (benchmark)
fit -- 1 1 -- 0.00576 3.59277 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers.py:79:fit (fit)
__init__ -- 1 1 -- 0.00004 0.00009 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:31:__init__ (__init__)
get_ort_device -- 1 1 -- 0.00000 0.00000 -- onnxruntime_helper.py:63:get_ort_device (get_ort_device)
numpy_to_ort_value -- 2 2 -- 0.00001 0.00004 -- onnxruntime_helper.py:133:numpy_to_ort_value (numpy_to_ort_value) +++
<listcomp> -- 1 1 -- 0.00003 0.00003 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers.py:96:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers.py:131:<listcomp> (<listcomp>)
<listcomp> -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers.py:132:<listcomp> (<listcomp>)
_iteration -- 100 100 -- 2.95887 3.55918 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers.py:194:_iteration (_iteration)
iter_bind -- 6800 6800 -- 0.05062 0.56841 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:188:iter_bind (iter_bind)
_next_iter -- 6700 6700 -- 0.02880 0.26717 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:98:_next_iter (_next_iter)
<method 'randint'...mState' objects> -- 6700 6700 -- 0.22612 0.22612 -- ~:0:<method 'randint' of 'numpy.random.mtrand.RandomState' objects> (<method 'randint' of 'numpy.random.mtrand.RandomState' objects>)
<built-in method builtins.len> -- 6700 6700 -- 0.00735 0.01225 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
local_bind -- 6700 6700 -- 0.21841 0.21841 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:209:local_bind (local_bind)
<built-in method builtins.len> -- 7100 7100 -- 0.01728 0.03221 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
_bind_input_ortvalue -- 100 100 -- 0.00133 0.00140 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/optimizers.py:167:_bind_input_ortvalue (_bind_input_ortvalue)
<built-in method builtins.isinstance> -- 200 200 -- 0.00007 0.00007 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>) +++
<method 'mean' of 'numpy.ndarray' objects> -- 100 100 -- 0.00060 0.01175 -- ~:0:<method 'mean' of 'numpy.ndarray' objects> (<method 'mean' of 'numpy.ndarray' objects>) +++
<built-in method numpy.array> -- 100 100 -- 0.01226 0.01226 -- ~:0:<built-in method numpy.array> (<built-in method numpy.array>) +++
<method 'append' of 'list' objects> -- 6700 6700 -- 0.00650 0.00650 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
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numpy_to_ort_value -- 100 100 -- 0.00053 0.00230 -- onnxruntime_helper.py:133:numpy_to_ort_value (numpy_to_ort_value) +++
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<method 'append' of 'list' objects> -- 100 100 -- 0.00007 0.00007 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>) +++
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_validate_params -- 1 1 -- 0.00002 0.00285 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/base.py:562:_validate_params (_validate_params)
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<listcomp> -- 1 1 -- 0.00004 0.00006 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/base.py:136:<listcomp> (<listcomp>)
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<listcomp> -- 1 1 -- 0.00001 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/base.py:151:<listcomp> (<listcomp>)
name -- 23 23 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/inspect.py:2565:name (name) +++
<built-in method builtins.getattr> -- 23 23 -- 0.00001 0.00001 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>) +++
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dot -- 20100 20100 -- 0.01246 0.01246 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/multiarray.py:740:dot (dot)
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squared_loss -- 6700 6700 -- 0.21644 0.75569 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_base.py:158:squared_loss (squared_loss)
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inplace_identity -- 6700 6700 -- 0.00467 0.00467 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_base.py:13:inplace_identity (inplace_identity)
inplace_relu -- 13400 13400 -- 0.24747 0.24747 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_base.py:47:inplace_relu (inplace_relu)
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safe_sparse_dot -- 20100 20100 -- 0.65156 0.69440 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/utils/extmath.py:156:safe_sparse_dot (safe_sparse_dot) +++
safe_sparse_dot -- 13400 13400 -- 0.29209 0.32024 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/utils/extmath.py:156:safe_sparse_dot (safe_sparse_dot) +++
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update_params -- 6700 6700 -- 0.34336 1.88125 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_stochastic_optimizers.py:29:update_params (update_params)
<genexpr> -- 46900 46900 -- 0.03107 0.03107 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_stochastic_optimizers.py:43:<genexpr> (<genexpr>)
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basename -- 6 6 -- 0.00004 0.00007 -- /usr/local/lib/python3.9/posixpath.py:140:basename (basename)
_get_sep -- 6 6 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/posixpath.py:41:_get_sep (_get_sep)
name -- 6 6 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/threading.py:1053:name (name)
current_thread -- 6 6 -- 0.00002 0.00002 -- /usr/local/lib/python3.9/threading.py:1318:current_thread (current_thread)
handle -- 6 6 -- 0.00002 0.00134 -- /usr/local/lib/python3.9/logging/__init__.py:1579:handle (handle)
filter -- 6 6 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/logging/__init__.py:787:filter (filter) +++
callHandlers -- 6 6 -- 0.00006 0.00131 -- /usr/local/lib/python3.9/logging/__init__.py:1633:callHandlers (callHandlers)
handle -- 12 12 -- 0.00006 0.00126 -- /usr/local/lib/python3.9/logging/__init__.py:935:handle (handle)
filter -- 12 12 -- 0.00005 0.00014 -- /usr/local/lib/python3.9/logging/__init__.py:787:filter (filter) +++
acquire -- 12 12 -- 0.00003 0.00005 -- /usr/local/lib/python3.9/logging/__init__.py:892:acquire (acquire) +++
release -- 12 12 -- 0.00001 0.00002 -- /usr/local/lib/python3.9/logging/__init__.py:899:release (release) +++
emit -- 6 6 -- 0.00003 0.00031 -- /usr/local/lib/python3.9/logging/__init__.py:1067:emit (emit) +++
emit -- 6 6 -- 0.00005 0.00068 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:151:emit (emit)
acquire -- 6 6 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/logging/__init__.py:892:acquire (acquire) +++
release -- 6 6 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/logging/__init__.py:899:release (release) +++
emit -- 6 6 -- 0.00004 0.00060 -- /usr/local/lib/python3.9/logging/__init__.py:1067:emit (emit) +++
isEnabledFor -- 6 6 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/logging/__init__.py:1677:isEnabledFor (isEnabledFor) +++
isEnabledFor -- 6 6 -- 0.00001 0.00003 -- /usr/local/lib/python3.9/logging/__init__.py:1834:isEnabledFor (isEnabledFor)
isEnabledFor -- 6 6 -- 0.00001 0.00001 -- /usr/local/lib/python3.9/logging/__init__.py:1677:isEnabledFor (isEnabledFor) +++
process -- 6 6 -- 0.00004 0.00005 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sphinx/util/logging.py:131:process (process)
get_state -- 2 2 -- 0.00027 0.00027 -- onnxruntime/capi/training/training_session.py:52:get_state (get_state)
numpy_to_ort_value -- 102 102 -- 0.00054 0.00234 -- onnxruntime_helper.py:133:numpy_to_ort_value (numpy_to_ort_value)
<built-in method onnxruntim...state.ortvalue_from_numpy> -- 102 102 -- 0.00180 0.00180 -- ~:0:<built-in method onnxruntime.capi.onnxruntime_pybind11_state.ortvalue_from_numpy> (<built-in method onnxruntime.capi.onnxruntime_pybind11_state.ortvalue_from_numpy>)
<built-in method builtins.len> -- 14330 14330 -- 0.02493 0.04475 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>)
__len__ -- 13600 13600 -- 0.01983 0.01983 -- onnxcustom/onnxcustom_UT_39_std/_doc/sphinxdoc/source/onnxcustom/training/data_loader.py:94:__len__ (__len__)
<method 'astype' of 'numpy.ndarray' objects> -- 12 12 -- 0.00010 0.00010 -- ~:0:<method 'astype' of 'numpy.ndarray' objects> (<method 'astype' of 'numpy.ndarray' objects>)
<built-in method numpy.array> -- 201 201 -- 0.01305 0.01305 -- ~:0:<built-in method numpy.array> (<built-in method numpy.array>)
<method 'append' of 'list' objects> -- 6950 6950 -- 0.00668 0.00668 -- ~:0:<method 'append' of 'list' objects> (<method 'append' of 'list' objects>)
<method 'mean' of 'numpy.ndarray' objects> -- 6800 6800 -- 0.02775 0.55100 -- ~:0:<method 'mean' of 'numpy.ndarray' objects> (<method 'mean' of 'numpy.ndarray' objects>)
_mean -- 6800 6800 -- 0.24623 0.52326 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/_methods.py:164:_mean (_mean) +++
<built-in method builtins.hasattr> -- 27697 27697 -- 0.02664 0.02664 -- ~:0:<built-in method builtins.hasattr> (<built-in method builtins.hasattr>)
<built-in method builtins.isinstance> -- 135660 135660 -- 0.11396 0.11644 -- ~:0:<built-in method builtins.isinstance> (<built-in method builtins.isinstance>)
__instancecheck__ -- 367 367 -- 0.00038 0.00247 -- /usr/local/lib/python3.9/abc.py:96:__instancecheck__ (__instancecheck__)
<built-in method _abc._abc_instancecheck> -- 367 367 -- 0.00151 0.00210 -- ~:0:<built-in method _abc._abc_instancecheck> (<built-in method _abc._abc_instancecheck>)
__subclasscheck__ -- 121 121 -- 0.00016 0.00058 -- /usr/local/lib/python3.9/abc.py:100:__subclasscheck__ (__subclasscheck__)
<built-in method _abc._abc_subclasscheck> -- 121 121 -- 0.00043 0.00043 -- ~:0:<built-in method _abc._abc_subclasscheck> (<built-in method _abc._abc_subclasscheck>)
<built-in method numpy.empty> -- 107 107 -- 0.00048 0.00048 -- ~:0:<built-in method numpy.empty> (<built-in method numpy.empty>)
<built-in method numpy.arange> -- 101 101 -- 0.00144 0.00144 -- ~:0:<built-in method numpy.arange> (<built-in method numpy.arange>)
<built-in method numpy.core._...th.implement_array_function> -- 40311 40325 -- 0.27119 2.28823 -- ~:0:<built-in method numpy.core._multiarray_umath.implement_array_function> (<built-in method numpy.core._multiarray_umath.implement_array_function>)
clip -- 1 1 -- 0.00001 0.00016 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:2111:clip (clip)
_wrapfunc -- 1 1 -- 0.00001 0.00015 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:51:_wrapfunc (_wrapfunc)
_wrapit -- 1 1 -- 0.00002 0.00014 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:38:_wrapit (_wrapit)
<method 'clip' of 'numpy.ndarray' objects> -- 1 1 -- 0.00001 0.00012 -- ~:0:<method 'clip' of 'numpy.ndarray' objects> (<method 'clip' of 'numpy.ndarray' objects>)
_clip -- 1 1 -- 0.00001 0.00012 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/_methods.py:127:_clip (_clip)
_clip_dep_is_scalar_nan -- 2 2 -- 0.00005 0.00008 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/_methods.py:93:_clip_dep_is_scalar_nan (_clip_dep_is_scalar_nan)
ndim -- 2 2 -- 0.00001 0.00003 -- <__array_function__ internals>:177:ndim (ndim)
_ndim_dispatcher -- 2 2 -- 0.00000 0.00000 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3148:_ndim_dispatcher (_ndim_dispatcher)
_clip_dep_is_byte_swapped -- 2 2 -- 0.00000 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/_methods.py:103:_clip_dep_is_byte_swapped (_clip_dep_is_byte_swapped)
_clip_dep_invoke_with_casting -- 1 1 -- 0.00001 0.00001 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/_methods.py:108:_clip_dep_invoke_with_casting (_clip_dep_invoke_with_casting)
sum -- 2 2 -- 0.00001 0.00018 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:2188:sum (sum)
_wrapreduction -- 2 2 -- 0.00002 0.00016 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:69:_wrapreduction (_wrapreduction) +++
any -- 1 1 -- 0.00001 0.00006 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:2333:any (any)
_wrapreduction -- 1 1 -- 0.00001 0.00005 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:69:_wrapreduction (_wrapreduction) +++
ndim -- 2 2 -- 0.00001 0.00002 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3152:ndim (ndim)
mean -- 20100 20100 -- 0.13697 2.00907 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/fromnumeric.py:3345:mean (mean)
_mean -- 20100 20100 -- 0.70426 1.87210 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/_methods.py:164:_mean (_mean) +++
zeros_like -- 6 6 -- 0.00005 0.00021 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/numeric.py:77:zeros_like (zeros_like)
empty_like -- 6 6 -- 0.00003 0.00007 -- <__array_function__ internals>:177:empty_like (empty_like)
empty_like -- 6 6 -- 0.00000 0.00000 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/multiarray.py:84:empty_like (empty_like)
copyto -- 6 6 -- 0.00003 0.00007 -- <__array_function__ internals>:177:copyto (copyto)
copyto -- 6 6 -- 0.00000 0.00000 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/multiarray.py:1079:copyto (copyto)
unique -- 101 101 -- 0.00081 0.00745 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/lib/arraysetops.py:138:unique (unique)
_unpack_tuple -- 101 101 -- 0.00020 0.00025 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/lib/arraysetops.py:125:_unpack_tuple (_unpack_tuple)
<built-in method builtins.len> -- 101 101 -- 0.00005 0.00005 -- ~:0:<built-in method builtins.len> (<built-in method builtins.len>) +++
_unique1d -- 101 101 -- 0.00433 0.00564 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/lib/arraysetops.py:323:_unique1d (_unique1d)
<method 'flatten' of 'numpy.ndarray' objects> -- 101 101 -- 0.00064 0.00064 -- ~:0:<method 'flatten' of 'numpy.ndarray' objects> (<method 'flatten' of 'numpy.ndarray' objects>)
<method 'sort' of 'numpy.ndarray' objects> -- 101 101 -- 0.00018 0.00018 -- ~:0:<method 'sort' of 'numpy.ndarray' objects> (<method 'sort' of 'numpy.ndarray' objects>)
<built-in method numpy.asanyarray> -- 101 101 -- 0.00003 0.00003 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>) +++
<built-in method numpy.empty> -- 101 101 -- 0.00046 0.00046 -- ~:0:<built-in method numpy.empty> (<built-in method numpy.empty>) +++
<built-in method numpy.asanyarray> -- 101 101 -- 0.00075 0.00075 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>) +++
<built-in method builtins.getattr> -- 20174 20174 -- 0.01770 0.01770 -- ~:0:<built-in method builtins.getattr> (<built-in method builtins.getattr>)
<built-in method numpy.asanyarray> -- 27102 27102 -- 0.01380 0.01380 -- ~:0:<built-in method numpy.asanyarray> (<built-in method numpy.asanyarray>)
<method 'reduce' of 'numpy.ufunc' objects> -- 26909 26909 -- 0.46628 0.46628 -- ~:0:<method 'reduce' of 'numpy.ufunc' objects> (<method 'reduce' of 'numpy.ufunc' objects>)
<method 'get' of 'dict' objects> -- 91 91 -- 0.00004 0.00004 -- ~:0:<method 'get' of 'dict' objects> (<method 'get' of 'dict' objects>)
<built-in method builtins.next> -- 40220 40220 -- 0.05038 0.21636 -- ~:0:<built-in method builtins.next> (<built-in method builtins.next>)
_no_nep50_warning -- 40200 40200 -- 0.08528 0.16598 -- onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/numpy/core/_ufunc_config.py:452:_no_nep50_warning (_no_nep50_warning)
<method 'set' of 'ContextVar' objects> -- 20100 20100 -- 0.04353 0.04353 -- ~:0:<method 'set' of 'ContextVar' objects> (<method 'set' of 'ContextVar' objects>)
<method 'reset' of 'ContextVar' objects> -- 20100 20100 -- 0.03716 0.03716 -- ~:0:<method 'reset' of 'ContextVar' objects> (<method 'reset' of 'ContextVar' objects>)
<built-in method posix.fspath> -- 24 24 -- 0.00001 0.00001 -- ~:0:<built-in method posix.fspath> (<built-in method posix.fspath>)
<built-in method _thread.get_ident> -- 12 12 -- 0.00001 0.00001 -- ~:0:<built-in method _thread.get_ident> (<built-in method _thread.get_ident>)
<method 'rfind' of 'str' objects> -- 18 18 -- 0.00003 0.00003 -- ~:0:<method 'rfind' of 'str' objects> (<method 'rfind' of 'str' objects>)
if GPU is available#
if get_device().upper() == 'GPU':
train_session = OrtGradientOptimizer(
onx_train, list(weights), device='cuda', learning_rate=5e-4,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
benches.append(benchmark(nn, train_session, name='NN-GPU'))
Linear Regression#
lr = MLPRegressor(hidden_layer_sizes=tuple(), max_iter=max_iter,
solver='sgd', learning_rate_init=5e-2, alpha=0,
n_iter_no_change=max_iter * 3, batch_size=batch_size,
nesterovs_momentum=False, momentum=0,
learning_rate='invscaling')
with warnings.catch_warnings():
warnings.simplefilter('ignore')
lr.fit(X, y)
onx = to_onnx(nn, X_train[:1].astype(numpy.float32), target_opset=15)
onx_train = add_loss_output(onx)
inits = get_train_initializer(onx)
weights = {k: v for k, v in inits.items() if k != "shape_tensor"}
pprint(list((k, v[0].shape) for k, v in weights.items()))
train_session = OrtGradientOptimizer(
onx_train, list(weights), device='cpu', learning_rate=1e-4,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
benches.append(benchmark(lr, train_session, name='LR-CPU'))
if get_device().upper() == 'GPU':
train_session = OrtGradientOptimizer(
onx_train, list(weights), device='cuda', learning_rate=1e-4,
warm_start=False, max_iter=max_iter, batch_size=batch_size)
benches.append(benchmark(nn, train_session, name='LR-GPU'))
[('coefficient', (100, 50)),
('intercepts', (1, 50)),
('coefficient1', (50, 10)),
('intercepts1', (1, 10)),
('coefficient2', (10, 1)),
('intercepts2', (1, 1))]
[benchmark] LR-CPU
somewhere/workspace/onnxcustom/onnxcustom_UT_39_std/_venv/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:679: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.
warnings.warn(
[benchmark] skl=100 iterations - 3.212737553054467 seconds
[benchmark] ort=100 iterations - 3.4550795000977814 seconds
GPU profiling#
if get_device().upper() == 'GPU':
ps = profile(lambda: benchmark(nn, train_session, name='LR-GPU'))[0]
root, nodes = profile2graph(ps, clean_text=clean_name)
text = root.to_text()
print(text)
Graphs#
Dataframe first.
df = DataFrame(benches).set_index('name')
df
text output
print(df)
skl ... losses_ort
name ...
NN-CPU 9.019831 ... [30392.508, 23225.424, 19217.426, 15981.25, 13...
LR-CPU 3.212738 ... [31250.82, 11663.726, 5872.9165, 2606.5598, 18...
[2 rows x 6 columns]
Graphs.
fig, ax = plt.subplots(1, 2, figsize=(10, 4))
df[['skl', 'ort']].plot.bar(title="Processing time", ax=ax[0])
ax[0].tick_params(axis='x', rotation=30)
for bench in benches:
ax[1].plot(bench['losses_skl'][1:], label='skl-' + bench['name'])
ax[1].plot(bench['losses_ort'][1:], label='ort-' + bench['name'])
ax[1].set_title("Losses")
ax[1].set_yscale('log')
ax[1].legend()
<matplotlib.legend.Legend object at 0x7faf8f1f8340>
The gradient update are not exactly the same. It should be improved for a fair comprison.
fig.savefig("plot_orttraining_benchmark.png")
# plt.show()
Total running time of the script: ( 0 minutes 44.902 seconds)