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 skl2onnx.tutorial 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)

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

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

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Measure the processing time

We use function skl2onnx.tutorial.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

  0%|          | 0/4 [00:00<?, ?it/s]
 25%|##5       | 1/4 [00:23<01:11, 23.98s/it]
 50%|#####     | 2/4 [00:47<00:47, 23.72s/it]
 75%|#######5  | 3/4 [00:54<00:16, 16.22s/it]
100%|##########| 4/4 [01:07<00:00, 14.66s/it]
100%|##########| 4/4 [01:07<00:00, 16.77s/it]

	average	deviation	min_exec	max_exec	repeat	number	size	mean_obs
0	0.047938	0.000205	0.047669	0.048882	50	10	1	0.047938
1	0.047066	0.000233	0.046815	0.048085	50	10	10	0.004707
2	0.072965	0.004347	0.067796	0.079429	10	10	1000	0.000073
3	0.245015	0.000774	0.243960	0.245910	5	10	10000	0.000025

Graphe.

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

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)
sess = InferenceSession(onx.SerializeToString())
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

  0%|          | 0/4 [00:00<?, ?it/s]
 25%|##5       | 1/4 [00:30<01:31, 30.38s/it]
 50%|#####     | 2/4 [01:04<01:05, 32.67s/it]
 75%|#######5  | 3/4 [01:47<00:37, 37.27s/it]
100%|##########| 4/4 [03:41<00:00, 67.67s/it]
100%|##########| 4/4 [03:41<00:00, 55.42s/it]

	average	deviation	min_exec	max_exec	repeat	number	size	skl	ort	pyrt
0	0.048880	0.000555	0.048377	0.051552	50	10	1	0.048880	0.000184	0.011666
1	0.047796	0.000096	0.047586	0.048032	50	10	10	0.004780	0.000075	0.001997
2	0.070935	0.001939	0.069122	0.073992	10	10	1000	0.000071	0.000011	0.000346
3	0.245143	0.000063	0.245053	0.245219	5	10	10000	0.000025	0.000006	0.000198

Graph.

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

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).