Benchmark ONNX conversion

Example Train and deploy a scikit-learn pipeline converts a simple model. This example takes a similar example but on random data and compares the processing time required by each option to compute predictions.

Training a pipeline

import numpy
from pandas import DataFrame
from tqdm import tqdm
from sklearn import config_context
from sklearn.datasets import make_regression
from sklearn.ensemble import (
    GradientBoostingRegressor, RandomForestRegressor,
    VotingRegressor)
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from mlprodict.onnxrt import OnnxInference
from onnxruntime import InferenceSession
from skl2onnx import to_onnx
from onnxcustom.utils import measure_time


N = 11000
X, y = make_regression(N, n_features=10)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, train_size=0.01)
print("Train shape", X_train.shape)
print("Test shape", X_test.shape)

reg1 = GradientBoostingRegressor(random_state=1)
reg2 = RandomForestRegressor(random_state=1)
reg3 = LinearRegression()
ereg = VotingRegressor([('gb', reg1), ('rf', reg2), ('lr', reg3)])
ereg.fit(X_train, y_train)

Out:

Train shape (110, 10)
Test shape (10890, 10)

VotingRegressor(estimators=[('gb', GradientBoostingRegressor(random_state=1)),
                            ('rf', RandomForestRegressor(random_state=1)),
                            ('lr', LinearRegression())])

Measure the processing time

We use function measure_time. The page about assume_finite may be useful if you need to optimize the prediction. We measure the processing time per observation whether or not an observation belongs to a batch or is a single one.

sizes = [(1, 50), (10, 50), (1000, 10), (10000, 5)]

with config_context(assume_finite=True):
    obs = []
    for batch_size, repeat in tqdm(sizes):
        context = {"ereg": ereg, 'X': X_test[:batch_size]}
        mt = measure_time(
            "ereg.predict(X)", context, div_by_number=True,
            number=10, repeat=repeat)
        mt['size'] = context['X'].shape[0]
        mt['mean_obs'] = mt['average'] / mt['size']
        obs.append(mt)

df_skl = DataFrame(obs)
df_skl

Out:

  0%|          | 0/4 [00:00<?, ?it/s]
 25%|##5       | 1/4 [00:20<01:01, 20.40s/it]
 50%|#####     | 2/4 [00:40<00:40, 20.32s/it]
 75%|#######5  | 3/4 [00:46<00:13, 13.71s/it]
100%|##########| 4/4 [00:57<00:00, 12.51s/it]
100%|##########| 4/4 [00:57<00:00, 14.29s/it]

	average	deviation	min_exec	max_exec	repeat	number	size	mean_obs
0	0.040780	0.000159	0.040507	0.041379	50	10	1	0.040780
1	0.040495	0.000136	0.040302	0.041086	50	10	10	0.004049
2	0.058415	0.000166	0.058157	0.058775	10	10	1000	0.000058
3	0.213325	0.000055	0.213257	0.213385	5	10	10000	0.000021

Graphe.

df_skl.set_index('size')[['mean_obs']].plot(
    title="scikit-learn", logx=True, logy=True)

Out:

<AxesSubplot:title={'center':'scikit-learn'}, xlabel='size'>

ONNX runtime

The same is done with the two ONNX runtime available.

onx = to_onnx(ereg, X_train[:1].astype(numpy.float32),
              target_opset={'': 14, 'ai.onnx.ml': 2})
sess = InferenceSession(onx.SerializeToString(),
                        providers=['CPUExecutionProvider'])
oinf = OnnxInference(onx, runtime="python_compiled")

obs = []
for batch_size, repeat in tqdm(sizes):

    # scikit-learn
    context = {"ereg": ereg, 'X': X_test[:batch_size].astype(numpy.float32)}
    mt = measure_time(
        "ereg.predict(X)", context, div_by_number=True,
        number=10, repeat=repeat)
    mt['size'] = context['X'].shape[0]
    mt['skl'] = mt['average'] / mt['size']

    # onnxruntime
    context = {"sess": sess, 'X': X_test[:batch_size].astype(numpy.float32)}
    mt2 = measure_time(
        "sess.run(None, {'X': X})[0]", context, div_by_number=True,
        number=10, repeat=repeat)
    mt['ort'] = mt2['average'] / mt['size']

    # mlprodict
    context = {"oinf": oinf, 'X': X_test[:batch_size].astype(numpy.float32)}
    mt2 = measure_time(
        "oinf.run({'X': X})['variable']", context, div_by_number=True,
        number=10, repeat=repeat)
    mt['pyrt'] = mt2['average'] / mt['size']

    # end
    obs.append(mt)


df = DataFrame(obs)
df

Out:

  0%|          | 0/4 [00:00<?, ?it/s]
 25%|##5       | 1/4 [00:20<01:02, 20.95s/it]
 50%|#####     | 2/4 [00:42<00:42, 21.15s/it]
 75%|#######5  | 3/4 [00:51<00:15, 15.93s/it]
100%|##########| 4/4 [01:12<00:00, 17.86s/it]
100%|##########| 4/4 [01:12<00:00, 18.20s/it]

	average	deviation	min_exec	max_exec	repeat	number	size	skl	ort	pyrt
0	0.041185	0.000134	0.040965	0.041642	50	10	1	0.041185	0.000129	0.000568
1	0.040842	0.000148	0.040634	0.041533	50	10	10	0.004084	0.000068	0.000104
2	0.059027	0.000113	0.058836	0.059276	10	10	1000	0.000059	0.000005	0.000033
3	0.215961	0.000058	0.215881	0.216058	5	10	10000	0.000022	0.000003	0.000017

Graph.

df.set_index('size')[['skl', 'ort', 'pyrt']].plot(
    title="Average prediction time per runtime",
    logx=True, logy=True)

Out:

<AxesSubplot:title={'center':'Average prediction time per runtime'}, xlabel='size'>

ONNX runtimes are much faster than scikit-learn to predict one observation. scikit-learn is optimized for training, for batch prediction. That explains why scikit-learn and ONNX runtimes seem to converge for big batches. They use similar implementation, parallelization and languages (C++, openmp).

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