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Python | onnxruntime

Below is a quick guide to get the packages installed to use ONNX for model serialization and inference with ORT.

• Install ONNX Runtime
• Install ONNX for model export
• Quickstart Examples for PyTorch, TensorFlow, and SciKit Learn
• Python API Reference Docs
• Builds
• Learn More

There are two Python packages for ONNX Runtime. Only one of these packages should be installed at a time in any one environment. The GPU package encompasses most of the CPU functionality.

Use the CPU package if you are running on Arm®-based CPUs and/or macOS.

The default CUDA version for ORT is 12.x.

pip install onnxruntime-gpu

For Cuda 11.8, please use the following instructions to install from ORT Azure Devops Feed

pip install onnxruntime-gpu --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-cuda-11/pypi/simple/

## ONNX is built into PyTorch
pip install torch

## tensorflow
pip install tf2onnx

## sklearn
pip install skl2onnx

Train a model using your favorite framework, export to ONNX format and inference in any supported ONNX Runtime language!

In this example we will go over how to export a PyTorch CV model into ONNX format and then inference with ORT. The code to create the model is from the PyTorch Fundamentals learning path on Microsoft Learn.

• Export the model using torch.onnx.export

torch.onnx.export(model,                                # model being run
                  torch.randn(1, 28, 28).to(device),    # model input (or a tuple for multiple inputs)
                  "fashion_mnist_model.onnx",           # where to save the model (can be a file or file-like object)
                  input_names = ['input'],              # the model's input names
                  output_names = ['output'])            # the model's output names

• Load the onnx model with onnx.load

  import onnx
  onnx_model = onnx.load("fashion_mnist_model.onnx")
  onnx.checker.check_model(onnx_model)
  

• Create inference session using ort.InferenceSession

import onnxruntime as ort
import numpy as np
x, y = test_data[0][0], test_data[0][1]
ort_sess = ort.InferenceSession('fashion_mnist_model.onnx')
outputs = ort_sess.run(None, {'input': x.numpy()})

# Print Result
predicted, actual = classes[outputs[0][0].argmax(0)], classes[y]
print(f'Predicted: "{predicted}", Actual: "{actual}"')

In this example we will go over how to export a PyTorch NLP model into ONNX format and then inference with ORT. The code to create the AG News model is from this PyTorch tutorial.

• Process text and create the sample data input and offsets for export.

  import torch
  text = "Text from the news article"
  text = torch.tensor(text_pipeline(text))
  offsets = torch.tensor([0])
  

• Export Model

  # Export the model
  torch.onnx.export(model,                     # model being run
                  (text, offsets),           # model input (or a tuple for multiple inputs)
                  "ag_news_model.onnx",      # where to save the model (can be a file or file-like object)
                  export_params=True,        # store the trained parameter weights inside the model file
                  opset_version=10,          # the ONNX version to export the model to
                  do_constant_folding=True,  # whether to execute constant folding for optimization
                  input_names = ['input', 'offsets'],   # the model's input names
                  output_names = ['output'], # the model's output names
                  dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes
                                'output' : {0 : 'batch_size'}})
  

• Load the model using onnx.load

  import onnx
  onnx_model = onnx.load("ag_news_model.onnx")
  onnx.checker.check_model(onnx_model)
  

• Create inference session with ort.InferenceSession

  import onnxruntime as ort
  import numpy as np
  ort_sess = ort.InferenceSession('ag_news_model.onnx')
  outputs = ort_sess.run(None, {'input': text.numpy(),
                              'offsets':  torch.tensor([0]).numpy()})
  # Print Result
  result = outputs[0].argmax(axis=1)+1
  print("This is a %s news" %ag_news_label[result[0]])
  

In this example we will go over how to export a TensorFlow CV model into ONNX format and then inference with ORT. The model used is from this GitHub Notebook for Keras resnet50.

• Get the pretrained model

import os
import tensorflow as tf
from tensorflow.keras.applications.resnet50 import ResNet50
import onnxruntime

model = ResNet50(weights='imagenet')

preds = model.predict(x)
print('Keras Predicted:', decode_predictions(preds, top=3)[0])
model.save(os.path.join("/tmp", model.name))

• Convert the model to onnx and export

import tf2onnx
import onnxruntime as rt

spec = (tf.TensorSpec((None, 224, 224, 3), tf.float32, name="input"),)
output_path = model.name + ".onnx"

model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=13, output_path=output_path)
output_names = [n.name for n in model_proto.graph.output]

• Create inference session with rt.InferenceSession

providers = ['CPUExecutionProvider']
m = rt.InferenceSession(output_path, providers=providers)
onnx_pred = m.run(output_names, {"input": x})

print('ONNX Predicted:', decode_predictions(onnx_pred[0], top=3)[0])

In this example we will go over how to export a SciKit Learn CV model into ONNX format and then inference with ORT. We’ll use the famous iris datasets.

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y)

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

LogisticRegression()

• Convert or export 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())

• Load and run the model using ONNX Runtime We will use ONNX Runtime to compute the predictions for this machine learning model.

import numpy
import onnxruntime as rt

sess = rt.InferenceSession("logreg_iris.onnx")
input_name = sess.get_inputs()[0].name
pred_onx = sess.run(None, {input_name: X_test.astype(numpy.float32)})[0]
print(pred_onx)

OUTPUT:
 [0 1 0 0 1 2 2 0 0 2 1 0 2 2 1 1 2 2 2 0 2 2 1 2 1 1 1 0 2 1 1 1 1 0 1 0 0
  1]

• Get predicted class

The code can be changed to get one specific output by specifying its name into a list.

import numpy
import onnxruntime as rt

sess = rt.InferenceSession("logreg_iris.onnx")
input_name = sess.get_inputs()[0].name
label_name = sess.get_outputs()[0].name
pred_onx = sess.run(
    [label_name], {input_name: X_test.astype(numpy.float32)})[0]
print(pred_onx)

Go to the ORT Python API Docs

If using pip, run pip install --upgrade pip prior to downloading.

Example to install onnxruntime-gpu for CUDA 11.*:

python -m pip install onnxruntime-gpu --extra-index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-11-nightly/pypi/simple/

Example to install onnxruntime-gpu for CUDA 12.*:

python -m pip install onnxruntime-gpu --pre --extra-index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/

For Python compiler version notes, see this page

• Python Tutorials
• TensorFlow with ONNX Runtime
• PyTorch with ONNX Runtime
• scikit-learn with ONNX Runtime

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