TensorRT 4 Accelerates Neural Machine Translation, Recommenders, and Speech

NVIDIA has released TensorRT 4 at CVPR 2018. This new version of TensorRT, NVIDIA’s powerful inference optimizer and runtime engine provides: Additional…

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NVIDIA
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Siddharth Sharma
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Overview

NVIDIA's TensorRT 4, released at CVPR 2018, enhances deep learning inference for applications like neural machine translation, recommenders, and speech recognition. Key features include new RNN layers, MLP optimizations, and support for ONNX, resulting in significant speed improvements across various applications.

What You'll Learn

1

How to implement neural machine translation using TensorRT 4

2

Why TensorRT 4 is beneficial for recommender systems

3

How to optimize speech recognition models with TensorRT

4

When to use ONNX format with TensorRT

5

How to integrate TensorFlow with TensorRT for improved inference

Key Questions Answered

What are the new features of TensorRT 4?
TensorRT 4 introduces new RNN layers for neural machine translation, MLP optimizations for recommenders, a native ONNX parser, and integration with TensorFlow. These features enhance performance and allow for custom neural network layers to be executed efficiently on GPUs.
How does TensorRT 4 improve neural machine translation performance?
TensorRT 4 accelerates neural machine translation by providing RNN layers that enhance sequence-to-sequence models, achieving up to 60x higher inference throughput on Tesla V100 GPUs compared to CPU-only implementations. This results in faster and more accurate translations.
What is the role of the RaggedSoftMax layer in TensorRT 4?
The RaggedSoftMax layer in TensorRT 4 implements cross-channel SoftMax for input tensors with variable lengths, allowing for more accurate results and faster computations by using a second tensor to specify sequence lengths.
How can TensorRT 4 be used for speech recognition?
TensorRT 4 enhances speech recognition by optimizing models like Baidu's Deep Speech 2, achieving 60x faster processing of audio input compared to CPU-only implementations. This is accomplished by accelerating all layers in the model, except for the probabilistic language model.

Key Statistics & Figures

Speedup for neural machine translation
up to 60x
Compared to CPU-only implementations on Tesla V100 GPUs.
Speedup across application areas
45x to 190x
Measured speedups for deep learning inference applications including translation, recommenders, and speech.

Technologies & Tools

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Inference Optimizer
Tensorrt
Used to accelerate deep learning inference applications.
Model Format
Onnx
Facilitates model interchange between different deep learning frameworks.
Deep Learning Framework
Tensorflow
Integrated with TensorRT to optimize inference performance.

Key Actionable Insights

1
Utilizing TensorRT 4 for neural machine translation can significantly enhance throughput and accuracy.
By implementing RNN layers and optimizations, developers can achieve faster inference times, making real-time translation applications more feasible.
2
Integrating TensorFlow with TensorRT can streamline the inference process and improve performance.
This integration allows developers to leverage TensorRT's optimizations while maintaining the flexibility of TensorFlow, resulting in a more efficient workflow.
3
Adopting the ONNX format can facilitate model interchange between different frameworks.
With TensorRT 4's native ONNX parser, developers can import models from various deep learning frameworks, optimizing them for GPU performance.

Common Pitfalls

1
Failing to optimize TensorFlow models before integrating with TensorRT can lead to suboptimal performance.
It's crucial to freeze the TensorFlow graph and ensure compatibility with TensorRT to fully leverage its optimization capabilities.

Related Concepts

Neural Machine Translation
Neural Collaborative Filtering
Automatic Speech Recognition
Deep Learning Frameworks