Train, convert and predict with ONNX Runtime

This example demonstrates an end to end scenario starting with the training of a machine learned model to its use in its converted from.

Train a logistic regression

The first step consists in retrieving the iris datset.

from sklearn.datasets import load_iris
iris = load_iris()
X, y = iris.data, iris.target

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y)

Then we fit a model.

from sklearn.linear_model import LogisticRegression
clr = LogisticRegression()
clr.fit(X_train, y_train)

We compute the prediction on the test set and we show the confusion matrix.

from sklearn.metrics import confusion_matrix

pred = clr.predict(X_test)
print(confusion_matrix(y_test, pred))

Out:

[[ 9  0  0]
 [ 0 15  1]
 [ 0  1 12]]

Conversion to ONNX format

We use module sklearn-onnx to convert the model into ONNX format.

from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType

initial_type = [('float_input', FloatTensorType([None, 4]))]
onx = convert_sklearn(clr, initial_types=initial_type)
with open("logreg_iris.onnx", "wb") as f:
    f.write(onx.SerializeToString())

We load the model with ONNX Runtime and look at its input and output.

import onnxruntime as rt
sess = rt.InferenceSession("logreg_iris.onnx")

print("input name='{}' and shape={}".format(
    sess.get_inputs()[0].name, sess.get_inputs()[0].shape))
print("output name='{}' and shape={}".format(
    sess.get_outputs()[0].name, sess.get_outputs()[0].shape))

Out:

input name='float_input' and shape=[None, 4]
output name='output_label' and shape=[1]

We compute the predictions.

input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name

import numpy
pred_onx = sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]
print(confusion_matrix(pred, pred_onx))

Out:

[[ 9  0  0]
 [ 0 16  0]
 [ 0  0 13]]

The prediction are perfectly identical.

Probabilities

Probabilities are needed to compute other relevant metrics such as the ROC Curve. Let’s see how to get them first with scikit-learn.

prob_sklearn = clr.predict_proba(X_test)
print(prob_sklearn[:3])

Out:

[[0.00198628 0.71308408 0.28492964]
 [0.00481246 0.83770353 0.15748401]
 [0.00483108 0.75930899 0.23585993]]

And then with ONNX Runtime. The probabilies appear to be

prob_name = sess.get_outputs()[1].name
prob_rt = sess.run([prob_name], {input_name: X_test.astype(numpy.float32)})[0]

import pprint
pprint.pprint(prob_rt[0:3])

Out:

[{0: 0.0019862803164869547, 1: 0.7130840420722961, 2: 0.28492966294288635},
 {0: 0.0048124659806489944, 1: 0.8377037048339844, 2: 0.15748386085033417},
 {0: 0.004831086378544569, 1: 0.7593092322349548, 2: 0.23585963249206543}]

Let’s benchmark.

from timeit import Timer

def speed(inst, number=10, repeat=20):
    timer = Timer(inst, globals=globals())
    raw = numpy.array(timer.repeat(repeat, number=number))
    ave = raw.sum() / len(raw) / number
    mi, ma = raw.min() / number, raw.max() / number
    print("Average %1.3g min=%1.3g max=%1.3g" % (ave, mi, ma))
    return ave

print("Execution time for clr.predict")
speed("clr.predict(X_test)")

print("Execution time for ONNX Runtime")
speed("sess.run([label_name], {input_name: X_test.astype(numpy.float32)})[0]")

Out:

Execution time for clr.predict
Average 4.8e-05 min=4.63e-05 max=5.71e-05
Execution time for ONNX Runtime
Average 3.19e-05 min=3.08e-05 max=3.81e-05

Let’s benchmark a scenario similar to what a webservice experiences: the model has to do one prediction at a time as opposed to a batch of prediction.

def loop(X_test, fct, n=None):
    nrow = X_test.shape[0]
    if n is None:
        n = nrow
    for i in range(0, n):
        im = i % nrow
        fct(X_test[im: im+1])

print("Execution time for clr.predict")
speed("loop(X_test, clr.predict, 100)")

def sess_predict(x):
    return sess.run([label_name], {input_name: x.astype(numpy.float32)})[0]

print("Execution time for sess_predict")
speed("loop(X_test, sess_predict, 100)")

Out:

Execution time for clr.predict
Average 0.00445 min=0.00423 max=0.00495
Execution time for sess_predict
Average 0.00151 min=0.00145 max=0.00164

Let’s do the same for the probabilities.

print("Execution time for predict_proba")
speed("loop(X_test, clr.predict_proba, 100)")

def sess_predict_proba(x):
    return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]

print("Execution time for sess_predict_proba")
speed("loop(X_test, sess_predict_proba, 100)")

Out:

Execution time for predict_proba
Average 0.00643 min=0.00632 max=0.00666
Execution time for sess_predict_proba
Average 0.00158 min=0.00152 max=0.00177

This second comparison is better as ONNX Runtime, in this experience, computes the label and the probabilities in every case.

Benchmark with RandomForest

We first train and save a model in ONNX format.

from sklearn.ensemble import RandomForestClassifier
rf = RandomForestClassifier()
rf.fit(X_train, y_train)

initial_type = [('float_input', FloatTensorType([1, 4]))]
onx = convert_sklearn(rf, initial_types=initial_type)
with open("rf_iris.onnx", "wb") as f:
    f.write(onx.SerializeToString())

We compare.

sess = rt.InferenceSession("rf_iris.onnx")

def sess_predict_proba_rf(x):
    return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]

print("Execution time for predict_proba")
speed("loop(X_test, rf.predict_proba, 100)")

print("Execution time for sess_predict_proba")
speed("loop(X_test, sess_predict_proba_rf, 100)")

Out:

Execution time for predict_proba
Average 0.516 min=0.483 max=0.572
Execution time for sess_predict_proba
Average 0.00176 min=0.00171 max=0.00188

Let’s see with different number of trees.

measures = []

for n_trees in range(5, 51, 5):
    print(n_trees)
    rf = RandomForestClassifier(n_estimators=n_trees)
    rf.fit(X_train, y_train)
    initial_type = [('float_input', FloatTensorType([1, 4]))]
    onx = convert_sklearn(rf, initial_types=initial_type)
    with open("rf_iris_%d.onnx" % n_trees, "wb") as f:
        f.write(onx.SerializeToString())
    sess = rt.InferenceSession("rf_iris_%d.onnx" % n_trees)
    def sess_predict_proba_loop(x):
        return sess.run([prob_name], {input_name: x.astype(numpy.float32)})[0]
    tsk = speed("loop(X_test, rf.predict_proba, 100)", number=5, repeat=5)
    trt = speed("loop(X_test, sess_predict_proba_loop, 100)", number=5, repeat=5)
    measures.append({'n_trees': n_trees, 'sklearn': tsk, 'rt': trt})

from pandas import DataFrame
df = DataFrame(measures)
ax = df.plot(x="n_trees", y="sklearn", label="scikit-learn", c="blue", logy=True)
df.plot(x="n_trees", y="rt", label="onnxruntime",
                ax=ax, c="green", logy=True)
ax.set_xlabel("Number of trees")
ax.set_ylabel("Prediction time (s)")
ax.set_title("Speed comparison between scikit-learn and ONNX Runtime\nFor a random forest on Iris dataset")
ax.legend()

Out:

5
Average 0.042 min=0.0412 max=0.0432
Average 0.00137 min=0.00136 max=0.00138
10
Average 0.065 min=0.0641 max=0.0662
Average 0.00151 min=0.00139 max=0.00159
15
Average 0.0886 min=0.0873 max=0.0911
Average 0.00145 min=0.00143 max=0.00148
20
Average 0.113 min=0.112 max=0.115
Average 0.0015 min=0.00149 max=0.00152
25
Average 0.14 min=0.135 max=0.15
Average 0.00161 min=0.0016 max=0.00164
30
Average 0.159 min=0.154 max=0.167
Average 0.00149 min=0.00146 max=0.00158
35
Average 0.183 min=0.177 max=0.195
Average 0.00153 min=0.0015 max=0.00159
40
Average 0.221 min=0.203 max=0.242
Average 0.0019 min=0.00153 max=0.0024
45
Average 0.23 min=0.22 max=0.248
Average 0.00177 min=0.00171 max=0.0018
50
Average 0.245 min=0.244 max=0.248
Average 0.0016 min=0.00157 max=0.00165

Total running time of the script: ( 2 minutes 25.165 seconds)