Note
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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))
[[12 0 0]
[ 0 12 1]
[ 0 0 13]]
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", providers=rt.get_available_providers())
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))
input name='float_input' and shape=[None, 4]
output name='output_label' and shape=[None]
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))
[[12 0 0]
[ 0 12 0]
[ 0 0 14]]
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])
[[9.64397638e-01 3.56022281e-02 1.33661432e-07]
[1.12861883e-02 7.36931324e-01 2.51782488e-01]
[9.71882114e-01 2.81177168e-02 1.68768358e-07]]
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])
[{0: 0.964397668838501, 1: 0.03560224175453186, 2: 1.3366144457904738e-07},
{0: 0.01128618698567152, 1: 0.7369312644004822, 2: 0.25178253650665283},
{0: 0.9718821048736572, 1: 0.028117714449763298, 2: 1.6876846586910688e-07}]
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]")
Execution time for clr.predict
Average 4.45e-05 min=4.23e-05 max=5.86e-05
Execution time for ONNX Runtime
Average 2.2e-05 min=2.14e-05 max=2.68e-05
2.1959864999132605e-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)")
Execution time for clr.predict
Average 0.00412 min=0.00409 max=0.00419
Execution time for sess_predict
Average 0.00102 min=0.00101 max=0.00105
0.0010201415149995795
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)")
Execution time for predict_proba
Average 0.0062 min=0.00618 max=0.00622
Execution time for sess_predict_proba
Average 0.00108 min=0.00107 max=0.0011
0.0010763829249992797
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", providers=rt.get_available_providers())
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)")
Execution time for predict_proba
Average 0.676 min=0.675 max=0.678
Execution time for sess_predict_proba
Average 0.00136 min=0.00132 max=0.00164
0.0013557706149995852
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, providers=rt.get_available_providers())
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()
5
Average 0.0492 min=0.0491 max=0.0493
Average 0.00102 min=0.00101 max=0.00104
10
Average 0.0825 min=0.0824 max=0.0827
Average 0.00105 min=0.00104 max=0.00107
15
Average 0.116 min=0.116 max=0.116
Average 0.00104 min=0.00103 max=0.00107
20
Average 0.149 min=0.149 max=0.149
Average 0.00106 min=0.00105 max=0.00108
25
Average 0.182 min=0.182 max=0.184
Average 0.00108 min=0.00107 max=0.0011
30
Average 0.215 min=0.214 max=0.215
Average 0.00108 min=0.00108 max=0.0011
35
Average 0.248 min=0.247 max=0.248
Average 0.0011 min=0.00108 max=0.00113
40
Average 0.281 min=0.281 max=0.281
Average 0.0011 min=0.00109 max=0.00113
45
Average 0.314 min=0.314 max=0.314
Average 0.0011 min=0.00109 max=0.00113
50
Average 0.347 min=0.347 max=0.348
Average 0.00113 min=0.00112 max=0.00116
<matplotlib.legend.Legend object at 0x7eff9f4d4400>
Total running time of the script: ( 3 minutes 8.827 seconds)
