ONNX Runtime in Computer Vision - Deep Dive

Overview - ONNX Runtime

What is it?

ONNX Runtime is a tool that helps run machine learning models quickly and efficiently on many devices. It supports models saved in the ONNX format, which is a common way to share models between different software. This runtime makes it easier to use models without worrying about the details of the original training framework. It works on computers, phones, and even cloud servers.

Why it matters

Without ONNX Runtime, developers would need to use the original software that created the model, which can be slow or hard to run on some devices. ONNX Runtime solves this by providing a fast and flexible way to run models anywhere. This means apps can work better and faster, helping things like image recognition, voice assistants, and medical diagnosis happen smoothly in real life.

Where it fits

Before learning ONNX Runtime, you should understand basic machine learning concepts and how models are trained and saved. After mastering ONNX Runtime, you can explore advanced model optimization, deployment on edge devices, and integrating AI into real-world applications.

Mental Model

Core Idea

ONNX Runtime is a universal engine that runs machine learning models saved in a standard format, making them fast and easy to use anywhere.

Think of it like...

Imagine ONNX Runtime as a universal power adapter that lets you plug any device into any socket, so your gadgets work no matter where you are.

┌───────────────┐       ┌───────────────┐       ┌───────────────┐
│  Model Saved  │──────▶│ ONNX Runtime  │──────▶│  Device/Cloud │
│  in ONNX      │       │  Engine       │       │  Execution    │
└───────────────┘       └───────────────┘       └───────────────┘

Build-Up - 6 Steps

1

Foundation

Understanding ONNX Model Format

Concept: Learn what ONNX format is and why models are saved this way.

ONNX stands for Open Neural Network Exchange. It is a file format that stores machine learning models in a way that different software can understand. Instead of being tied to one tool, models saved in ONNX can be used anywhere. This makes sharing and running models easier.

Result

You know that ONNX is a common language for models, like a universal file format.

Understanding ONNX format is key because ONNX Runtime depends on this standard to run models from many sources.

2

Foundation

What ONNX Runtime Does

Concept: Discover the role of ONNX Runtime as the engine that runs ONNX models.

ONNX Runtime takes a model saved in ONNX format and runs it to make predictions. It handles all the math and operations inside the model. It works on many devices and speeds up the process by using special tricks and hardware support.

Result

You see ONNX Runtime as the tool that makes models work fast and smoothly on your device.

Knowing ONNX Runtime’s role helps you understand why it is important for deploying AI in real applications.

3

Intermediate

Running Models on Different Devices

🤔Before reading on: do you think ONNX Runtime can run the same model on a phone and a cloud server without changes? Commit to your answer.

Concept: Learn how ONNX Runtime supports many devices and platforms.

ONNX Runtime is designed to work on computers, mobile phones, and cloud servers. It uses device-specific optimizations to run models efficiently. This means the same ONNX model file can be used everywhere, and ONNX Runtime adjusts how it runs based on the device.

Result

You understand that ONNX Runtime makes AI models portable and fast across devices.

Recognizing device flexibility is crucial for building AI apps that work everywhere without rewriting models.

4

Intermediate

Optimizing Model Performance

🤔Before reading on: do you think ONNX Runtime changes the model itself to make it faster, or just how it runs? Commit to your answer.

Concept: Explore how ONNX Runtime improves speed using optimizations without altering the original model.

ONNX Runtime applies optimizations like fusing operations, using faster math libraries, and leveraging hardware like GPUs or specialized chips. These optimizations happen during runtime, so the model file stays the same but runs faster.

Result

You see that ONNX Runtime boosts speed while keeping model accuracy intact.

Understanding runtime optimizations helps you appreciate how ONNX Runtime balances speed and correctness.

5

Advanced

Customizing Execution Providers

🤔Before reading on: do you think ONNX Runtime uses one fixed way to run models, or can it switch methods? Commit to your answer.

Concept: Learn about execution providers that let ONNX Runtime run models using different hardware or software backends.

ONNX Runtime supports execution providers like CPU, GPU (CUDA, DirectML), and specialized accelerators. You can choose or combine these providers to get the best performance for your hardware. This flexibility allows ONNX Runtime to adapt to new devices and technologies.

Result

You understand how to tailor ONNX Runtime to your hardware for maximum speed.

Knowing execution providers unlocks advanced deployment strategies and hardware use.

6

Expert

Extending ONNX Runtime with Custom Operators

🤔Before reading on: do you think ONNX Runtime can only run built-in model operations, or can it be extended? Commit to your answer.

Concept: Discover how to add new operations to ONNX Runtime when your model uses custom or new functions.

Sometimes models use operations not yet supported by ONNX Runtime. You can write custom operators in C++ or Python and register them with ONNX Runtime. This lets you run any model, even cutting-edge ones, without waiting for official support.

Result

You see that ONNX Runtime is flexible and can grow with new AI research.

Understanding custom operators shows how ONNX Runtime stays relevant and adaptable in fast-changing AI.

Under the Hood

ONNX Runtime loads the ONNX model graph, which is a network of operations and data flows. It compiles this graph into an optimized execution plan using graph transformations and operator kernels. During runtime, it schedules operations efficiently, using hardware acceleration when available. Memory management and threading are handled to maximize throughput and minimize latency.

Why designed this way?

ONNX Runtime was created to solve the problem of fragmented AI frameworks and slow deployment. By using a standard model format and a modular runtime, it allows developers to deploy models quickly on any device. The design balances flexibility, speed, and ease of integration, avoiding the need to rewrite models for each platform.

┌───────────────┐       ┌───────────────────────┐       ┌───────────────┐
│ ONNX Model    │──────▶│ Graph Optimizer       │──────▶│ Execution Plan │
│ (Operators &  │       │ (Fusing, Simplifying) │       │ (Optimized    │
│  Data Flow)   │       └───────────────────────┘       │  Instructions)│
└───────────────┘                                       └───────────────┘
         │                                                      │
         ▼                                                      ▼
┌───────────────────┐                                ┌───────────────────┐
│ Execution Engine   │◀─────────────┐               │ Hardware (CPU,    │
│ (Scheduling,      │              │               │ GPU, Accelerators)│
│ Memory, Threads)  │──────────────┘               └───────────────────┘

Myth Busters - 4 Common Misconceptions

Quick: Does ONNX Runtime train models or only run them? Commit to yes or no.

Common Belief: ONNX Runtime can train machine learning models just like training frameworks.

Reality: ONNX Runtime is designed only for running (inference) models, not training them.

Why it matters: Confusing runtime with training leads to wasted effort trying to train models with ONNX Runtime, which it cannot do.

Quick: Do you think ONNX Runtime changes the model's predictions when optimizing? Commit to yes or no.

Common Belief: ONNX Runtime optimizations can change the model's output results.

Reality: Optimizations preserve the model's predictions exactly; they only improve speed and efficiency.

Why it matters: Believing optimizations alter results can cause mistrust and prevent using ONNX Runtime's speed benefits.

Quick: Can ONNX Runtime run any machine learning model format? Commit to yes or no.

Common Belief: ONNX Runtime can run models saved in any format, like TensorFlow or PyTorch files directly.

Reality: ONNX Runtime only runs models saved in the ONNX format; other formats must be converted first.

Why it matters: Expecting direct support for all formats causes confusion and deployment delays.

Quick: Does ONNX Runtime always run faster than the original framework? Commit to yes or no.

Common Belief: ONNX Runtime is always faster than the original training framework for running models.

Reality: ONNX Runtime is often faster but not guaranteed; performance depends on model, hardware, and optimizations.

Why it matters: Assuming guaranteed speedup can lead to disappointment and poor hardware choices.

Expert Zone

1

ONNX Runtime's graph optimizations can sometimes reorder operations in ways that expose hardware parallelism but require careful validation to avoid subtle bugs.

2

Execution providers can be combined in hybrid modes, allowing parts of a model to run on different hardware simultaneously for maximum efficiency.

3

Custom operators must match ONNX Runtime's memory and threading models exactly to avoid crashes or incorrect results, which is a common source of hard-to-debug errors.

When NOT to use

ONNX Runtime is not suitable when you need to train models or perform heavy model editing. In those cases, use training frameworks like PyTorch or TensorFlow. Also, for very simple models or scripts, direct framework inference might be easier.

Production Patterns

In production, ONNX Runtime is often integrated into microservices or mobile apps to provide fast AI predictions. It is combined with model versioning systems and hardware-specific tuning. Teams use execution providers to leverage GPUs or accelerators and monitor runtime performance closely.

Connections

Containerization (Docker)

Builds-on

Knowing how ONNX Runtime runs models helps you package AI apps in containers for consistent deployment across environments.

Compiler Optimization

Same pattern

ONNX Runtime’s graph optimizations are similar to compiler optimizations in programming languages, improving speed without changing output.

Electrical Power Adapters

Analogy

Understanding ONNX Runtime as a universal adapter helps grasp how it enables models to run on diverse hardware seamlessly.

Common Pitfalls

#1 Trying to run a model in ONNX Runtime without converting it to ONNX format first.

Wrong approach:

session = onnxruntime.InferenceSession('model.pth')

Correct approach:

session = onnxruntime.InferenceSession('model.onnx')

Root cause:

Confusing original training model files with ONNX format files.

#2 Assuming ONNX Runtime will automatically use GPU without specifying the execution provider.

Wrong approach:

session = onnxruntime.InferenceSession('model.onnx') # No provider specified

Correct approach:

session = onnxruntime.InferenceSession('model.onnx', providers=['CUDAExecutionProvider'])

Root cause:

Not configuring ONNX Runtime to use available hardware accelerators.

#3 Modifying the ONNX model file directly to optimize it instead of using ONNX Runtime's optimization features.

Wrong approach:

Manually editing the ONNX file to fuse nodes.

Correct approach:

Use ONNX Runtime's built-in graph optimization options during session creation.

Root cause:

Lack of understanding that ONNX Runtime handles optimizations internally.

Key Takeaways

ONNX Runtime is a powerful engine that runs machine learning models saved in the ONNX format efficiently across many devices.

It separates model training from deployment, focusing solely on fast and flexible inference.

Optimizations happen during runtime to speed up models without changing their predictions.

Execution providers allow ONNX Runtime to leverage different hardware like CPUs, GPUs, and accelerators.

Custom operators extend ONNX Runtime’s capabilities, making it adaptable to new AI developments.

Practice

1. What is the main purpose of ONNX Runtime in machine learning?

easy

A. To collect and label training data

B. To train new machine learning models from scratch

C. To visualize data and create charts

D. To run pre-trained machine learning models efficiently on different devices

Solution

1. Step 1: Understand ONNX Runtime's role

   ONNX Runtime is designed to run models that are already trained, not to train new ones.

2. Step 2: Identify the correct purpose

   It helps run these models efficiently on many devices, making deployment easier.

3. Final Answer:

   To run pre-trained machine learning models efficiently on different devices -> Option D

4. Quick Check:

   ONNX Runtime runs models = A [OK]

Hint: ONNX Runtime runs models, not trains them [OK]

Common Mistakes:

• Confusing ONNX Runtime with training frameworks
• Thinking it is for data visualization
• Assuming it collects or labels data

2. Which Python code snippet correctly loads an ONNX model using ONNX Runtime?

easy

A. import onnxruntime as ort
session = ort.Model('model.onnx')

B. import onnxruntime as ort
session = ort.load_model('model.onnx')

C. import onnxruntime as ort
session = ort.InferenceSession('model.onnx')

D. import onnxruntime as ort
session = ort.run('model.onnx')

Solution

1. Step 1: Recall ONNX Runtime loading method

   The correct method to load a model is using InferenceSession with the model file path.

2. Step 2: Check each option

   Only import onnxruntime as ort session = ort.InferenceSession('model.onnx') uses ort.InferenceSession correctly; others use invalid methods.

3. Final Answer:

   import onnxruntime as ort\nsession = ort.InferenceSession('model.onnx') -> Option C

4. Quick Check:

   Use InferenceSession to load model = A [OK]

Hint: Use ort.InferenceSession('model.onnx') to load model [OK]

Common Mistakes:

• Using non-existent methods like load_model or run
• Not importing onnxruntime correctly
• Confusing model loading with running

3. Given the code below, what will be the output type of outputs?

import onnxruntime as ort
import numpy as np

session = ort.InferenceSession('model.onnx')
input_name = session.get_inputs()[0].name
input_data = np.random.rand(1, 3, 224, 224).astype(np.float32)
outputs = session.run(None, {input_name: input_data})
print(type(outputs))

medium

A.

B.

C.

D.

Solution

1. Step 1: Understand session.run output

   Calling session.run returns a list of outputs from the model.

2. Step 2: Check the print statement

   Printing type(outputs) will show <class 'list'> because outputs is a list.

3. Final Answer:

   <class 'list'> -> Option A

4. Quick Check:

   session.run returns list = C [OK]

Hint: session.run returns a list of outputs [OK]

Common Mistakes:

• Assuming outputs is a numpy array directly
• Thinking outputs is a dictionary
• Confusing tuple with list

4. Identify the error in the following ONNX Runtime code snippet:

import onnxruntime as ort
session = ort.InferenceSession('model.onnx')
input_name = session.get_inputs()[0]
input_data = [1.0, 2.0, 3.0]
outputs = session.run(None, {input_name: input_data})

medium

A. input_name should be the name string, not the input object

B. input_data must be a dictionary, not a list

C. session.run requires the model path as first argument

D. onnxruntime does not support list inputs

Solution

1. Step 1: Check input_name assignment

   session.get_inputs()[0] returns an input object, but session.run expects the input name string as key.

2. Step 2: Correct usage

   Use session.get_inputs()[0].name to get the input name string for the dictionary key.

3. Final Answer:

   input_name should be the name string, not the input object -> Option A

4. Quick Check:

   Use input_name = session.get_inputs()[0].name [OK]

Hint: Use input_name = session.get_inputs()[0].name [OK]

Common Mistakes:

• Using input object instead of input name string
• Passing wrong input data types
• Misunderstanding session.run arguments

5. You want to run an ONNX model on a GPU using ONNX Runtime. Which code snippet correctly enables GPU execution?

hard

A. import onnxruntime as ort
session = ort.InferenceSession('model.onnx', execution_mode='GPU')

B. import onnxruntime as ort
session = ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider'])

C. import onnxruntime as ort
session = ort.InferenceSession('model.onnx', use_gpu=True)

D. import onnxruntime as ort
session = ort.InferenceSession('model.onnx', device='GPU')

Solution

1. Step 1: Recall how to enable GPU in ONNX Runtime

   ONNX Runtime uses the 'providers' argument with 'CUDAExecutionProvider' to run on GPU.

2. Step 2: Check each option

   Only import onnxruntime as ort session = ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider']) correctly uses providers=['CUDAExecutionProvider']; others use invalid parameters.

3. Final Answer:

   import onnxruntime as ort\nsession = ort.InferenceSession('model.onnx', providers=['CUDAExecutionProvider']) -> Option B

4. Quick Check:

   Use providers=['CUDAExecutionProvider'] for GPU [OK]

Hint: Set providers=['CUDAExecutionProvider'] to use GPU [OK]

Common Mistakes:

• Using non-existent parameters like device or use_gpu
• Confusing execution_mode with providers
• Not specifying providers disables GPU