This post was updated July 20, 2021 to reflect NVIDIA TensorRT 8.0 updates. In this post, you learn how to deploy TensorFlow trained deep learning models using…
Overview
This article discusses how to speed up deep learning inference using a workflow that integrates TensorFlow, ONNX, and NVIDIA TensorRT. It provides a detailed guide on converting TensorFlow models to ONNX format and optimizing them with TensorRT for enhanced performance.
What You'll Learn
1
How to convert TensorFlow models to ONNX format for optimization
2
How to create a TensorRT engine from an ONNX model
3
How to run inference using the TensorRT engine
4
Why using TensorRT can significantly speed up inference times
Prerequisites & Requirements
- Basic understanding of deep learning frameworks like TensorFlow
- Installation of TensorFlow, ONNX, and TensorRT
- Familiarity with Python programming
Key Questions Answered
How do you convert a TensorFlow model to ONNX format?
To convert a TensorFlow model to ONNX format, you can use the tf2onnx tool. After installing it, run the command 'python -m tf2onnx.convert --input /Path/to/model.pb --output model.onnx' to perform the conversion.
What are the steps to create a TensorRT engine from an ONNX model?
To create a TensorRT engine from an ONNX model, you need to parse the ONNX model using TensorRT's ONNX parser, set the input shape, and then build the engine using the builder. Finally, save the engine to a .plan file for inference.
What are the benefits of using TensorRT for deep learning inference?
Using TensorRT for deep learning inference can lead to significant performance improvements, enabling faster inference times and reduced latency. It optimizes the model for the specific hardware, making it more efficient for deployment in production environments.
What is the ONNX format and why is it important?
ONNX (Open Neural Network Exchange) is an open format for representing machine learning models. It allows models trained in different frameworks to be used interchangeably, facilitating easier deployment and optimization across various platforms.
Technologies & Tools
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Backend
Tensorflow
Used for training deep learning models before conversion to ONNX.
Format
Onnx
Serves as the intermediary format for model conversion and optimization.
Backend
Nvidia Tensorrt
Used for optimizing and accelerating inference of deep learning models.
Key Actionable Insights
1Utilize the TensorFlow-ONNX-TensorRT workflow to optimize your deep learning models for production.This workflow allows you to leverage the strengths of each framework, ensuring that your models run efficiently on NVIDIA hardware.
2Experiment with different precision settings (FP32, FP16, INT8) when optimizing your models with TensorRT.Choosing the right precision can significantly impact performance and resource utilization, especially in resource-constrained environments.
3Regularly update your TensorRT and ONNX libraries to benefit from the latest optimizations and features.Staying updated ensures that you have access to the latest performance improvements and bug fixes, which can enhance your model's efficiency.
Common Pitfalls
1
Failing to properly set the input shape when creating the TensorRT engine can lead to runtime errors.
Always ensure that the input shape matches the expected dimensions of the model to avoid issues during inference.
2
Not optimizing the model for the specific hardware can result in suboptimal performance.
Utilizing TensorRT's optimization features is crucial for achieving the best inference speed and efficiency.
Related Concepts
Deep Learning Model Optimization
Model Conversion Techniques
Performance Benchmarking