TensorFlow 2.0 with Tighter TensorRT Integration Now Available

To help developers build scalable ML-powered applications, Google released TensorFlow 2.0, one of the core open source libraries for training deep learning…

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NVIDIA
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Nefi Alarcon
•2 min read•intermediate•
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Overview

The article discusses the release of TensorFlow 2.0, highlighting its enhanced performance and integration with TensorRT for machine learning applications. It emphasizes the improvements in training speed and ease of use for developers, particularly on NVIDIA GPUs.

What You'll Learn

1

How to achieve up to 3x faster training performance using mixed precision on NVIDIA GPUs

2

Why TensorRT integration can lead to up to 7x speedup on inference tasks

3

How to utilize the Distribution Strategy API for multi-GPU training with minimal code changes

Key Questions Answered

What performance improvements can developers expect from TensorFlow 2.0?
Developers can expect up to 3x faster training performance using mixed precision on NVIDIA Volta and Turing GPUs, and up to 7x speedup on inference tasks with TensorRT integration. This makes TensorFlow 2.0 a powerful tool for building scalable ML applications.
How does TensorFlow 2.0 simplify the development of ML applications?
TensorFlow 2.0 simplifies ML application development through tighter integration with Keras, default eager execution, and a more Pythonic API. These enhancements make the framework more accessible and familiar for Python developers.
What is the role of the Distribution Strategy API in TensorFlow 2.0?
The Distribution Strategy API allows developers to easily distribute training across multiple GPUs with minimal code changes. This feature is particularly useful for leveraging Keras’ Model.fit and custom training loops, enhancing scalability.

Key Statistics & Figures

Training performance improvement
up to 3x
Achieved using mixed precision on NVIDIA Volta and Turing GPUs.
Inference speedup
up to 7x
Realized through the integration of TensorRT with TensorFlow 2.0.

Technologies & Tools

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Framework
Tensorflow
Core library for training deep learning models.
Inference Optimizer
Tensorrt
Used for optimizing deep learning inference performance.
Hardware
Nvidia Volta And Turing Gpus
Hardware platforms that support mixed precision training.
Hardware
Nvidia T4 Gpus
Used for improved inference performance on Google Cloud.

Key Actionable Insights

1
Leverage mixed precision training to significantly reduce training times for deep learning models.
Using mixed precision on NVIDIA GPUs can lead to substantial performance gains, making it a valuable technique for developers aiming to optimize their ML workflows.
2
Utilize TensorRT for optimizing inference performance in production environments.
By integrating TensorRT with TensorFlow 2.0, developers can achieve remarkable speed improvements, which is crucial for applications requiring real-time inference.
3
Adopt the Distribution Strategy API for efficient multi-GPU training.
This API simplifies the process of scaling training across multiple GPUs, which is essential for handling larger datasets and more complex models.