Automate AI Workflows with Qualcomm AI Runtime

🚀 Technical Briefing:This tutorial is part of our deep-dive series on Agentic Workflows at[Gate of AI]. For the full technical breakdown, interactive code sandbox, and the native Arabic translation, visit the[original article here].

<span>Tutorial</span>
<span>Advanced</span>
<span>⏱ 45 min read</span>
<span>© Gate of AI 2026-07-07</span>

In this tutorial, you'll learn to automate AI workflows using Qualcomm AI Runtime, enhancing deployment efficiency and model performance.

In this tutorial, we will build a sophisticated AI workflow automation system using the Qualcomm AI Runtime (QAIRT). This system will allow you to efficiently deploy AI models, manage inference tasks, and handle model updates with minimal downtime. The finished project will streamline the deployment process, reduce errors, and optimize resource usage.

The system leverages advanced features of QAIRT, such as model optimization, efficient resource allocation, and seamless integration with various AI frameworks. By the end of this tutorial, you will have a deep understanding of how to automate complex AI workflows, enabling scalable and efficient AI deployments in production environments.

To begin, we need to set up our development environment and install the necessary tools. This includes the Qualcomm AI Runtime SDK, which provides the tools and libraries required to build and optimize AI workflows.

pip install qairt-sdk

Next, configure your environment variables to include the necessary paths for the QAIRT SDK. This ensures that the SDK tools can be accessed from any directory in your terminal.

  
  
  .env file example

QAIRT_HOME=/path/to/qairt-sdk
PATH=$QAIRT_HOME/bin:$PATH

Make sure to replace /path/to/qairt-sdk

with the actual path where the SDK is installed on your system.

The first step in automating your AI workflow is to prepare your AI model for deployment. This involves converting your model to a format that is compatible with the Qualcomm AI Runtime.

import torch
from torchvision import models

  
  
  Load a pre-trained model

model = models.resnet50(pretrained=True)

  
  
  Export the model to ONNX format

torch.onnx.export(model, 
                  torch.randn(1, 3, 224, 224), 
                  "resnet50.onnx", 
                  export_params=True, 
                  opset_version=11, 
                  do_constant_folding=True, 
                  input_names = ['input'], 
                  output_names = ['output'])

This code snippet demonstrates how to export a PyTorch model to the ONNX format, which is widely supported by AI deployment frameworks. The torch.onnx.export

function converts the model and saves it as an ONNX file, making it ready for optimization and deployment.

With your model in ONNX format, the next step is to optimize it using the Qualcomm AI Runtime. Optimization enhances performance by reducing model size and increasing inference speed.

from qairt_sdk import ModelOptimizer

  
  
  Initialize the model optimizer

optimizer = ModelOptimizer()

  
  
  Optimize the ONNX model

optimized_model_path = optimizer.optimize("resnet50.onnx", target_device="snapdragon")

print(f"Optimized model saved to {optimized_model_path}")

Here, we use the ModelOptimizer

class from the QAIRT SDK to optimize the ONNX model for a specific target device, in this case, a Snapdragon processor. The optimized model is saved to a specified path, ready for deployment.

Once your model is optimized, you can automate its deployment using the Qualcomm AI Runtime's deployment tools. This step involves setting up a deployment pipeline that handles model updates and scales with demand.

from qairt_sdk import DeploymentManager

  
  
  Initialize the deployment manager

deployment_manager = DeploymentManager()

  
  
  Deploy the optimized model

deployment_manager.deploy(optimized_model_path, service_name="image-classification-service")

print("Model deployed successfully as image-classification-service")

The DeploymentManager

class facilitates the deployment of AI models as services. By specifying the service name, you can easily manage and update your model deployments, ensuring that your AI system remains responsive and up-to-date.

⚠️ Common Mistake: Ensure that your environment variables are correctly set up before running the optimization and deployment scripts. Incorrect paths can lead to errors during model conversion and deployment.

To verify that your AI workflow automation is working correctly, you can test the deployed service using sample input data. This helps ensure that your model is performing as expected in a production environment.

import requests

  
  
  Sample input data

input_data = {"input": [0.0] * 224 * 224 * 3}

  
  
  Send a request to the deployed service

response = requests.post("http://localhost:8000/predict", json=input_data)

  
  
  Check the response

print(response.json())

This test script sends a sample input to the deployed service and prints the response. The output should match your expectations based on the model's training, confirming that the deployment is successful.

With your AI workflow automation in place, you can extend this tutorial by: