Note
Click here to download the full example code
Benchmark Linear Regression#
The script compares different implementations for the operator LinearRegression.
baseline: LinearRegression from scikit-learn
ort: onnxruntime,
mlprodict: an implementation based on an array of structures, every structure describes a node,
Import#
import warnings
from time import perf_counter as time
from multiprocessing import cpu_count
import numpy
from numpy.random import rand
from numpy.testing import assert_almost_equal
import matplotlib.pyplot as plt
import pandas
from onnxruntime import InferenceSession
from sklearn import config_context
from sklearn.linear_model import LinearRegression
from sklearn.utils._testing import ignore_warnings
from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
from mlprodict.onnxrt import OnnxInference
Available optimisation on this machine.
from mlprodict.testing.experimental_c_impl.experimental_c import code_optimisation
print(code_optimisation())
AVX-omp=8
Versions#
def version():
from datetime import datetime
import sklearn
import numpy
import onnx
import onnxruntime
import skl2onnx
import mlprodict
df = pandas.DataFrame([
{"name": "date", "version": str(datetime.now())},
{"name": "numpy", "version": numpy.__version__},
{"name": "scikit-learn", "version": sklearn.__version__},
{"name": "onnx", "version": onnx.__version__},
{"name": "onnxruntime", "version": onnxruntime.__version__},
{"name": "skl2onnx", "version": skl2onnx.__version__},
{"name": "mlprodict", "version": mlprodict.__version__},
])
return df
version()
Implementations to benchmark#
def fcts_model(X, y, n_jobs):
"LinearRegression."
model = LinearRegression(n_jobs=n_jobs)
model.fit(X, y)
initial_types = [('X', FloatTensorType([None, X.shape[1]]))]
onx = convert_sklearn(model, initial_types=initial_types)
sess = InferenceSession(onx.SerializeToString())
outputs = [o.name for o in sess.get_outputs()]
oinf = OnnxInference(onx, runtime="python")
def predict_skl_predict(X, model=model):
return model.predict(X)
def predict_onnxrt_predict(X, sess=sess):
return sess.run(outputs[:1], {'X': X})[0]
def predict_onnx_inference(X, oinf=oinf):
return oinf.run({'X': X})["variable"]
return {'predict': (
predict_skl_predict, predict_onnxrt_predict,
predict_onnx_inference)}
Benchmarks#
def allow_configuration(**kwargs):
return True
def bench(n_obs, n_features, n_jobss,
methods, repeat=10, verbose=False):
res = []
for nfeat in n_features:
ntrain = 50000
X_train = numpy.empty((ntrain, nfeat)).astype(numpy.float32)
X_train[:, :] = rand(ntrain, nfeat)[:, :]
eps = rand(ntrain) - 0.5
y_train = X_train.sum(axis=1) + eps
for n_jobs in n_jobss:
fcts = fcts_model(X_train, y_train, n_jobs)
for n in n_obs:
for method in methods:
fct1, fct2, fct3 = fcts[method]
if not allow_configuration(n=n, nfeat=nfeat,
n_jobs=n_jobs, method=method):
continue
obs = dict(n_obs=n, nfeat=nfeat, method=method,
n_jobs=n_jobs)
# creates different inputs to avoid caching in any ways
Xs = []
for r in range(repeat):
x = numpy.empty((n, nfeat))
x[:, :] = rand(n, nfeat)[:, :]
Xs.append(x.astype(numpy.float32))
# measures the baseline
with config_context(assume_finite=True):
st = time()
repeated = 0
for X in Xs:
p1 = fct1(X)
repeated += 1
if time() - st >= 1:
break # stops if longer than a second
end = time()
obs["time_skl"] = (end - st) / repeated
# measures the new implementation
st = time()
r2 = 0
for X in Xs:
p2 = fct2(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_ort"] = (end - st) / r2
# measures the other new implementation
st = time()
r2 = 0
for X in Xs:
p2 = fct3(X)
r2 += 1
if r2 >= repeated:
break
end = time()
obs["time_mlprodict"] = (end - st) / r2
# final
res.append(obs)
if verbose and (len(res) % 1 == 0 or n >= 10000):
print("bench", len(res), ":", obs)
# checks that both produce the same outputs
if n <= 10000:
if len(p1.shape) == 1 and len(p2.shape) == 2:
p2 = p2.ravel()
try:
assert_almost_equal(
p1.ravel(), p2.ravel(), decimal=5)
except AssertionError as e:
warnings.warn(str(e))
return res
Graphs#
def plot_rf_models(dfr):
def autolabel(ax, rects):
for rect in rects:
height = rect.get_height()
ax.annotate(f'{height:1.1f}x',
xy=(rect.get_x() + rect.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom',
fontsize=8)
engines = [_.split('_')[-1] for _ in dfr.columns if _.startswith("time_")]
engines = [_ for _ in engines if _ != 'skl']
for engine in engines:
dfr[f"speedup_{engine}"] = dfr["time_skl"] / dfr[f"time_{engine}"]
print(dfr.tail().T)
ncols = 2
fig, axs = plt.subplots(len(engines), ncols, figsize=(
14, 4 * len(engines)), sharey=True)
row = 0
for row, engine in enumerate(engines):
pos = 0
name = f"LinearRegression - {engine}"
for nf in sorted(set(dfr.nfeat)):
for n_jobs in sorted(set(dfr.n_jobs)):
sub = dfr[(dfr.nfeat == nf) & (dfr.n_jobs == n_jobs)]
ax = axs[row, pos]
labels = sub.n_obs
means = sub[f"speedup_{engine}"]
x = numpy.arange(len(labels))
width = 0.90
rects1 = ax.bar(x, means, width, label='Speedup')
if pos == 0:
ax.set_yscale('log')
ax.set_ylim([0.1, max(dfr[f"speedup_{engine}"])])
if pos == 0:
ax.set_ylabel('Speedup')
ax.set_title('%s\n%d features\n%d jobs' % (name, nf, n_jobs))
if row == len(engines) - 1:
ax.set_xlabel('batch size')
ax.set_xticks(x)
ax.set_xticklabels(labels)
autolabel(ax, rects1)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(8)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(8)
pos += 1
fig.tight_layout()
return fig, ax
Run benchs#
@ignore_warnings(category=FutureWarning)
def run_bench(repeat=250, verbose=False):
n_obs = [1, 10, 100, 1000, 10000]
methods = ['predict']
n_features = [10, 50]
n_jobss = [cpu_count()]
start = time()
results = bench(n_obs, n_features, n_jobss,
methods, repeat=repeat, verbose=verbose)
end = time()
results_df = pandas.DataFrame(results)
print("Total time = %0.3f sec cpu=%d\n" % (end - start, cpu_count()))
# plot the results
return results_df
name = "plot_linear_regression"
df = run_bench(verbose=True)
df.to_csv(f"{name}.csv", index=False)
df.to_excel(f"{name}.xlsx", index=False)
fig, ax = plot_rf_models(df)
fig.savefig(f"{name}.png")
plt.show()
bench 1 : {'n_obs': 1, 'nfeat': 10, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.00014773387555032968, 'time_ort': 0.00010520627163350582, 'time_mlprodict': 6.118528731167317e-05}
bench 2 : {'n_obs': 10, 'nfeat': 10, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0001483665443956852, 'time_ort': 4.1522331535816195e-05, 'time_mlprodict': 6.130124349147082e-05}
bench 3 : {'n_obs': 100, 'nfeat': 10, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0001498299716040492, 'time_ort': 4.6781595796346664e-05, 'time_mlprodict': 6.29116278141737e-05}
bench 4 : {'n_obs': 1000, 'nfeat': 10, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0003365598805248737, 'time_ort': 9.92848165333271e-05, 'time_mlprodict': 7.686508726328612e-05}
bench 5 : {'n_obs': 10000, 'nfeat': 10, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0002445342680439353, 'time_ort': 0.000542697592638433, 'time_mlprodict': 0.00018369774054735898}
bench 6 : {'n_obs': 1, 'nfeat': 50, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0003002711357548833, 'time_ort': 4.500821139663458e-05, 'time_mlprodict': 6.16198442876339e-05}
bench 7 : {'n_obs': 10, 'nfeat': 50, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0001500316085293889, 'time_ort': 4.406862426549196e-05, 'time_mlprodict': 6.194743979722261e-05}
bench 8 : {'n_obs': 100, 'nfeat': 50, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.00015513039566576481, 'time_ort': 6.595087610185147e-05, 'time_mlprodict': 6.675603147596121e-05}
bench 9 : {'n_obs': 1000, 'nfeat': 50, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.00040518213249742983, 'time_ort': 0.0002856091680005193, 'time_mlprodict': 8.659934438765049e-05}
bench 10 : {'n_obs': 10000, 'nfeat': 50, 'method': 'predict', 'n_jobs': 8, 'time_skl': 0.0004963645478710532, 'time_ort': 0.0009404946751892567, 'time_mlprodict': 0.00042046605609357356}
Total time = 6.585 sec cpu=8
5 6 7 8 9
n_obs 1 10 100 1000 10000
nfeat 50 50 50 50 50
method predict predict predict predict predict
n_jobs 8 8 8 8 8
time_skl 0.0003 0.00015 0.000155 0.000405 0.000496
time_ort 0.000045 0.000044 0.000066 0.000286 0.00094
time_mlprodict 0.000062 0.000062 0.000067 0.000087 0.00042
speedup_ort 6.671475 3.404499 2.352211 1.418659 0.52777
speedup_mlprodict 4.872962 2.421918 2.323841 4.678813 1.18051
somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_opml_linear_regression.py:247: MatplotlibDeprecationWarning: The label function was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use Tick.label1 instead.
tick.label.set_fontsize(8)
somewhere/workspace/mlprodict/mlprodict_UT_39_std/_doc/examples/plot_opml_linear_regression.py:249: MatplotlibDeprecationWarning: The label function was deprecated in Matplotlib 3.1 and will be removed in 3.8. Use Tick.label1 instead.
tick.label.set_fontsize(8)
Total running time of the script: ( 0 minutes 9.719 seconds)