Optimizing Models for GPU Servers in Keras
Overview
Keras is a high-level deep learning library that simplifies building and training deep learning models. Optimizing models for GPU servers can significantly accelerate their training, which can be especially useful for large and complex models. This article will discuss some strategies for optimizing models for GPU Servers in Keras.
Introduction to TensorRT
TensorFlow TensorRT is a library for optimizing deep learning models trained with TensorFlow, an open-source machine learning framework. The library is designed to run on NVIDIA GPUs, specialized hardware devices well-suited for running deep learning algorithms. TensorRT allows users to take trained TensorFlow models and apply graph transformations and optimizations to improve performance. This can include reducing the precision of the weights and activations of the model to reduce memory usage and improve inference speed. Additionally, TensorRT provides tools for visualizing and profiling the performance of optimized models, making it easier to identify potential areas for further optimization. Overall, TensorFlow TensorRT can be a valuable tool for deploying deep learning models on edge devices with limited computational resources.
Optimizing a Keras Model with TensorRT
TensorRT is a tool developed by NVIDIA that can optimize pre-trained deep learning models. It can reduce a model's inference time and memory usage, which can be useful for deploying the model on edge devices with limited resources. To use TensorRT with a Keras model, you first need to convert the model to a TensorFlow model and then use the TensorFlow TensorRT API to optimize the model. Here is an outline of the steps you can follow to do this:
Step 1: Train your Keras model and serialize your model. Step 2: Import TensorFlow TensorRT from tensorflow.python.compiler.tensorrt import trt_convert as trt Step 3: Set up the configuration for the optimization with a precision mode. Step 4: Perform the model conversion via TrtGraphConverterV2 converted. Step 5: Serialize the optimized model for gpu servers in Keras.
Now it's time to look into the example.
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Import Packages
Load a Pre-trained Model and Serialize It
Download the Image and Visualize It
Output:
Test the Keras Model
Output
Turn Learning into Career Growth
Convert the Keras Model to TensorRT Model
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Test the TensorRT Model
Output
Comparison Between Model Sizes, Latency, and Throughput
Benchmark the Keras Model
Output
Benchmark the TensorRT Model
Output
We calculate the latency and the throughput of the Keras model and the optimized TensorRT model. As a result, we can see that the TensorRT model's latency is lower than the Keras model, and the FPS of the TensorRT model is much higher than the Keras model.
Conclusion
This article covered optimizing the model in a GPU server in Keras based environment.
- We understood what TensorRT is and its major concepts.
- We also understood how we could optimize the model using TensorRT.
- We compared both the model in terms of latency and throughput.
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