Note
Click here to download the full example code
Profile the execution of a simple model¶
ONNX Runtime can profile the execution of the model. This example shows how to interpret the results.
import onnx
import onnxruntime as rt
import numpy
from onnxruntime.datasets import get_example
def change_ir_version(filename, ir_version=6):
"onnxruntime==1.2.0 does not support opset <= 7 and ir_version > 6"
with open(filename, "rb") as f:
model = onnx.load(f)
model.ir_version = 6
if model.opset_import[0].version <= 7:
model.opset_import[0].version = 11
return model
Let’s load a very simple model and compute some prediction.
example1 = get_example("mul_1.onnx")
onnx_model = change_ir_version(example1)
onnx_model_str = onnx_model.SerializeToString()
sess = rt.InferenceSession(onnx_model_str, providers=rt.get_available_providers())
input_name = sess.get_inputs()[0].name
x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
res = sess.run(None, {input_name: x})
print(res)
Out:
[array([[ 1., 4.],
[ 9., 16.],
[25., 36.]], dtype=float32)]
We need to enable to profiling before running the predictions.
options = rt.SessionOptions()
options.enable_profiling = True
sess_profile = rt.InferenceSession(onnx_model_str, options, providers=rt.get_available_providers())
input_name = sess.get_inputs()[0].name
x = numpy.array([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], dtype=numpy.float32)
sess.run(None, {input_name: x})
prof_file = sess_profile.end_profiling()
print(prof_file)
Out:
onnxruntime_profile__2022-01-04_17-09-55.json
The results are stored un a file in JSON format. Let’s see what it contains.
import json
with open(prof_file, "r") as f:
sess_time = json.load(f)
import pprint
pprint.pprint(sess_time)
Out:
[{'args': {},
'cat': 'Session',
'dur': 56,
'name': 'model_loading_array',
'ph': 'X',
'pid': 3089,
'tid': 3089,
'ts': 1},
{'args': {},
'cat': 'Session',
'dur': 240,
'name': 'session_initialization',
'ph': 'X',
'pid': 3089,
'tid': 3089,
'ts': 71}]
Total running time of the script: ( 0 minutes 0.007 seconds)
