The ability to export a model to run on a specific target is a critical skill that all AI engineers should possess. To make this article comprehensive, it is divided into two main parts: the basics of model exporting on Microsoft Azure and step-by-step guide on how to export an AI model.
Understanding Exporting a Model in Azure
Before we dive into the practical guide, let’s first understand what exporting a model means in the context of Azure AI solutions.
Exporting a model implies the process of training an AI model using Azure Machine Learning and then downloading it to a local environment or deploying it to a cloud or edge device, in a format that’s supported by your target environment. The exported model can be in ONNX (Open Neural Network Exchange) format, which is a platform-agnostic model format that allows interchange between different deep learning models, or a framework-specific format like TensorFlow, PyTorch etc.
Prerequisites
Azure Machine Learning is a Python-based service, therefore, make sure that you have:
- An Azure subscription
- A Machine Learning workspace
- Access to a development environment with Python and Azure Machine Learning SDK installed.
Step-by-step Guide on How to Export an AI Model
Let’s walk through the steps to train and export a machine learning model in the Azure Machine Learning workspace.
- Create a Training Script
Firstly, a script must be created to train your model. This script uses your chosen machine learning framework (like TensorFlow, PyTorch, etc.) and the Azure Machine Learning Python SDK.
from azureml.core import Run
import argparse
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import joblib
# Get parameters
parser = argparse.ArgumentParser()
parser.add_argument('--regularization', type=float, dest='reg_rate', default=0.01, help='regularization rate')
args = parser.parse_args()
# Get the experiment run context
run = Run.get_context()
# load the diabetes dataset
data = pd.read_csv('data.csv')
# Split data into training set and test set
X_train, X_test, y_train, y_test = train_test_split(data[['Feature1','Feature2','Feature3']], data['Label'], test_size=0.30)
# Logistic regression model
reg = 0.01
model = LogisticRegression(C=1/reg, solver="liblinear").fit(X_train, y_train)
# Save the trained model
model_file = 'model.pkl'
joblib.dump(value=model, filename=model_file)
# upload the model file to the run outputs
run.upload_file(name = 'outputs/' + model_file, path_or_stream = './' + model_file)
run.complete()
- Train the Model in Azure Machine Learning
The training script should be run as an experiment in the Azure ML Workspace.
- Register the Model
After the experiment has completed, you can register the model ensuring it is versioned and can be retrieved later.
from azureml.core import Model
model = run.register_model(model_name='ai-model', model_path='outputs/model.pkl')
- Export the Model
Export the model in ONNX or other framework-specific format.
from azureml.core.model import Model
import os
model_path = Model.get_model_path('ai-model', _workspace=ws)
os.rename(model_path, "model.onnx")
Now, the model is trained, registered, and exported as ‘model.onnx’. The exported model can be used on the specific target which supports ONNX or your specific exported model format.
Understanding how to effectively export models in Azure AI solutions increases efficiency and productivity, providing structural models for use in a variety of environment targets. Azure platform not only offers the tools to train and build AI models, but also to export these models for further use or deployment.
Conclusion
Being able to export a model to run on a specific target is a valuable skill when it comes to AI-102 Designing and Implementing a Microsoft Azure AI Solution, predominantly due to the impressive flexibility and efficiency this provides. If you are preparing for AI-102 exam, mastering this concept of model exporting will certainly leverage your chance to encounter the complexities contemporary AI engineering presents.
Practice Test
True or False: A model cannot be exported to run on Microsoft Azure.
- True
- False
Answer: False
Explanation: Models can be exported to run on Microsoft Azure. The AI-102 exam includes skills related to deploying Azure AI solutions such as bots and AI solutions within the Azure Machine Learning service workspace.
Can you run a model on a specific target in Microsoft Azure?
- A. Yes
- B. No
Answer: A. Yes
Explanation: Yes, you can run a model on a specific target in Microsoft Azure. This can be a compute target that you specify, or the local compute resource where the experiment is being run.
It is necessary to export a model when:
- A. You want to use it on a different platform
- B. There is a change in the model’s input
- C. Both A and B
- D. None of the above
Answer: C. Both A and B
Explanation: When you want to use a model on a different platform or there’s a change in the model’s input, you need to export the model.
True or False: The model can be exported in ONNX format.
- True
- False
Answer: True
Explanation: ONNX is an open format built to represent machine learning models. It allows models to be transferred between different frameworks, such as from PyTorch to Tensorflow.
Model running on a specific target can be processed parallelly.
- A. True
- B. False
Answer: A. True
Explanation: The model can be parallelly processed as Microsoft Azure supports parallel processing which allows for faster performance.
Microsoft Azure supports which format for exporting a model?
- A. ONNX
- B. XML
- C. JSON
- D. All of the above
Answer: A. ONNX
Explanation: Microsoft Azure supports ONNX, an open format built to represent machine learning models.
Running a model on a specific target can help reduce:
- A. Latency
- B. Cost
- C. Both A and B
- D. None of the above
Answer: C. Both A and B
Explanation: Running a model on a specific target can help reduce latency and can save cost by utilizing cloud resources efficiently.
Models can be exported to run on Microsoft Azure using which programming language:
- A. Python
- B. Java
- C. Both A and B
- D. None of the above
Answer: C. Both A and B
Explanation: Both Python and Java are supported by Microsoft Azure for developing and deploying AI solutions.
True or False: Once a model is exported, it can’t be modified.
- True
- False
Answer: False
Explanation: Exported models can be updated or fine-tuned as per requirements. This is a common practice in machine learning to continually improve model performance.
Which of the following is not a method for exporting a model in Microsoft Azure?
- A. PyTorch Export
- B. TensorFlow Export
- C. ONNX Export
- D. SVG Export
Answer: D. SVG Export
Explanation: SVG is a format for two-dimensional graphics, and it’s not used for exporting machine learning models in Microsoft Azure or any other platform. PyTorch, TensorFlow, and ONNX are common methods for exporting models.
Interview Questions
What is the process of exporting a model to run on a specific target on Azure AI?
The process involves several steps: firstly, train your machine learning model using Azure Machine Learning. Then, export the model into a format that the target device or service can use, this is usually ONNX (Open Neural Network Exchange) format. Next, optimize the model for the target service or device if needed. Lastly, deploy the model to the device or service.
What is ONNX and why is it used while exporting a model to run on a specific target?
ONNX (Open Neural Network Exchange) is an open standard format for representing machine learning models. It enables developers to freely move models between state-of-the-art tools and choose the combination best for them.
What specific types of models are supported by Azure Machine Learning for export?
Azure Machine Learning supports the export of models trained using scikit-learn and PyTorch.
What format should the model be in, to be used with Windows Machine Learning?
The model should be in ONNX format to be used with Windows Machine Learning.
Are there limitations to the types of machine learning models that can be converted to the ONNX format?
Yes, there are some limitations. While ONNX supports most common modeling operations and architectures, not all layers and operations from every ML framework (like TensorFlow or Keras) are available in ONNX.
How can you use exported ONNX models in an application?
Exported ONNX models can be directly loaded and used by a variety of platforms and tools such as Windows Machine Learning, ONNX Runtime, and others.
Can you optimize an ONNX model before deployment?
Yes. The process of optimizing the model for better performance on a specific target device or service often involves steps such as increasing its inference speed or reducing its memory footprint.
What tools can be used to optimize ONNX models for deployment?
Tools such as ONNX Runtime and WinML Tools can be used to optimize ONNX models before deployment.
How do you handle operationalization after model export?
After a model is exported, it can be operationalized by deploying it to target devices or services. Azure Machine Learning provides various deployment options, including container instances, Kubernetes, and edge devices.
What is the role of the ONNX Runtime in using exported models?
ONNX Runtime is a high-performance scoring engine for ONNX models. It helps in optimizing and accelerating inferencing across cloud and edge devices.
How can one handle versioning of exported models in Azure?
Azure Machine Learning provides model registry for managing versions of the models. Each time a model is registered, the version is incremented.
Can you use Azure Machine Learning to export models trained outside Azure?
Yes, models trained outside Azure can be exported using Azure Machine Learning. One can register the model into Azure Machine Learning workspace and then deploy.
Are there any restrictions on the size of the model that can be exported with Azure Machine Learning?
Yes, the maximum size for a model file or a collection of model files that can be registered in Azure Machine Learning is 4 GiB.
Is it possible to export models in formats other than ONNX with Azure Machine Learning?
Yes, it is possible, for instance, TensorFlow models can be saved and exported in their native SavedModel format.
How can one load the ONNX model into a prediction script in Azure?
ONNX models can be loaded into a prediction script using ONNX Runtime, an open-source inference engine for ONNX models.
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