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 onnx.reference import ReferenceEvaluator
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 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), (100, 10)]

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%|          | 0/3 [00:00<?, ?it/s]
 33%|███▎      | 1/3 [00:07<00:14,  7.06s/it]
 67%|██████▋   | 2/3 [00:12<00:06,  6.25s/it]
100%|██████████| 3/3 [00:14<00:00,  4.01s/it]
100%|██████████| 3/3 [00:14<00:00,  4.70s/it]

	average	deviation	min_exec	max_exec	repeat	number	size	mean_obs
0	0.014108	0.004352	0.008928	0.029686	50	10	1	0.014108
1	0.011358	0.003103	0.008228	0.020336	50	10	10	0.001136
2	0.013486	0.002790	0.009885	0.018525	10	10	100	0.000135

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(), providers=["CPUExecutionProvider"])
oinf = ReferenceEvaluator(onx)

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"]

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

    # end
    obs.append(mt)


df = DataFrame(obs)
df

  0%|          | 0/3 [00:00<?, ?it/s]
 33%|███▎      | 1/3 [00:15<00:31, 15.60s/it]
 67%|██████▋   | 2/3 [00:40<00:21, 21.10s/it]
100%|██████████| 3/3 [01:03<00:00, 21.84s/it]
100%|██████████| 3/3 [01:03<00:00, 21.09s/it]

	average	deviation	min_exec	max_exec	repeat	number	size	skl	ort	pyrt
0	0.012201	0.003428	0.008608	0.021660	50	10	1	0.012201	0.000030	0.018960
1	0.011550	0.003386	0.008357	0.023143	50	10	10	0.001155	0.000021	0.003811
2	0.013936	0.004899	0.009295	0.023858	10	10	100	0.000139	0.000004	0.002129

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).