Introduction
TensorRT is a software that optimizes deep learning inference on NVIDIA GPUs. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.
TensorRT is designed to work in conjunction with training frameworks such as TensorFlow, PyTorch, and MXNet. It takes a trained network, which consists of a network definition and a set of trained parameters, and produces a highly optimized runtime engine that performs inference for that network.
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TensorRT is ideal for applications that require fast and efficient inference, such as computer vision, natural language processing, speech recognition, recommender systems, and more. TensorRT can help you achieve higher performance, lower latency, lower power consumption, and reduced memory footprint for your inference workloads.
Features and Benefits
TensorRT provides several features and benefits that make it a powerful tool for deep learning inference. Some of them are:
Optimization techniques: TensorRT applies various optimization techniques such as quantization, layer and tensor fusion, kernel tuning, and others to improve the execution speed and efficiency of your network. TensorRT also supports reduced-precision inference using INT8 or FP16, which can significantly reduce latency and memory usage without compromising accuracy.
Framework integration: TensorRT is integrated with popular frameworks such as PyTorch and TensorFlow, so you can easily import your trained models from these frameworks into TensorRT. TensorRT also provides an ONNX parser that allows you to import models from other frameworks that support the ONNX format.
Inference serving: TensorRT-optimized models can be deployed, run, and scaled with NVIDIA Triton, an open-source inference serving software that includes TensorRT as one of its backends. Triton enables high throughput with dynamic batching and concurrent model execution, as well as features such as model ensembles, streaming audio/video inputs, and more.
Benchmarking: TensorRT has demonstrated world-leading inference performance across various domains and tasks in the industry-standard benchmark for MLPerf Inference. TensorRT-based applications perform up to 36X faster than CPU-only platforms during inference.
Installation
There are different ways to install TensorRT depending on your platform and preference. Here are some of the most common options:
Container installation: You can use a customized virtual machine image (VMI) that NVIDIA publishes and maintains on a regular basis. These VMIs contain pre-installed versions of TensorRT along with other NVIDIA software components such as CUDA, cuDNN, NCCL, etc. You can find these VMIs on the .
Debian installation: You can download the Debian package of TensorRT from the . This package contains the core library files for TensorRT along with the parsers (ONNX and Caffe) and plugins. You can install this package using the dpkg command on Ubuntu or Debian systems.
Pip installation: You can install the Python package of TensorRT using the pip command. This package contains the Python bindings for TensorRT along with the ONNX parser. You can use this package to access the TensorRT API from Python scripts.
For more details on how to install TensorRT using these or other methods, please refer to the .
Examples
To help you get started with using TensorRT for your inference applications, NVIDIA provides a set of that demonstrate how to use TensorRT for various tasks such as importing models, optimizing engines, and performing inference. These samples are written in C++ or Python and cover different domains such as computer vision, natural language processing, recommender systems, and more. You can find these samples in the /usr/src/tensorrt/samples directory after installing TensorRT.
Here are some examples of how to use TensorRT for common tasks:
Importing models
To import a model into TensorRT, you need to use a parser that can read the model format and convert it into a TensorRT network. TensorRT supports two parsers: ONNX and Caffe. You can also write your own parser for other formats using the TensorRT API.
The ONNX parser can import models from frameworks that support the ONNX format, such as PyTorch, TensorFlow, MXNet, etc. The Caffe parser can import models from the Caffe framework.
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To use the ONNX parser, you need to include the NvOnnxParser.h header file and create an instance of the nvonnxparser::IParser class. Then, you can call the parseFromFile() method to parse the ONNX model file and populate the TensorRT network.
To use the Caffe parser, you need to include the NvCaffeParser.h header file and create an instance of the nvcaffeparser1::ICaffeParser class. Then, you can call the parse() method to parse the Caffe prototxt and model files and populate the TensorRT network.
Optimizing engines
To optimize a network for inference, you need to use a builder that can create an engine from the network. TensorRT supports two builders: regular and network definition API (NDA). You can also use an optimizer to fine-tune the engine performance.
The regular builder can create an engine from a network that is imported using a parser or defined using the TensorRT API. The NDA builder can create an engine from a network that is defined using a lower-level API that gives more control over the network structure and optimization parameters.
To use the regular builder, you need to include the NvInfer.h header file and create an instance of the nvinfer1::IBuilder class. Then, you can set various builder options such as max batch size, max workspace size, precision mode, etc. Finally, you can call the buildCudaEngine() method to build an engine from the network.
To use the NDA builder, you need to include the NvInfer.h header file and create an instance of the nvinfer1::INetworkDefinitionCreationFlags class. Then, you can set various flags such as explicit batch dimension, explicit precision, etc. Next, you need to create an instance of the nvinfer1::IBuilderConfig class and set various config options such as max workspace size, precision mode, etc. Finally, you can call the buildEngineWithConfig() method to build an engine from the network.
To use an optimizer, you need to include the NvInfer.h header file and create an instance of the nvinfer1::IOptimizationProfile class. Then, you can set various optimization parameters such as input shape ranges, dynamic shapes, etc. Next, you need to add the optimization profile to the builder config using the addOptimizationProfile() method. Finally, you can build an engine with the config that includes the optimization profile.
Performing inference
To perform inference using an engine, you need to use a runtime that can execute the engine on a GPU. You can also use an executor that can manage the input and output buffers for the engine.
The runtime can load an engine from a file or a memory buffer and execute it on a GPU. The executor can allocate and deallocate the input and output buffers for the engine and copy the data between the host and the device.
To use the runtime, you need to include the NvInfer.h header file and create an instance of the nvinfer1::IRuntime class. Then, you can call the deserializeCudaEngine() method to load an engine from a file or a memory buffer. Next, you need to create an instance of the nvinfer1::IExecutionContext class using the createExecutionContext() method of the engine. Finally, you can call the execute() or executeV2() method of the context to execute the engine on a GPU.
To use an executor, you need to include the NvInfer.h header file and create an instance of the nvinfer1::IHostMemory class. Then, you can call the allocate() method to allocate memory for the input and output buffers. Next, you need to copy the input data from the host to the device using the cudaMemcpy() function. Then, you can execute the engine using the runtime as described above. After that, you need to copy the output data from the device to the host using the cudaMemcpy() function. Finally, you can call the free() method to free the memory for the input and output buffers.
FAQ
In this section, I will answer some frequently asked questions about TensorRT. If you have more questions, please visit the .
What are the hardware and software requirements for TensorRT?
To use TensorRT, you need a NVIDIA GPU that supports CUDA compute capability 3.5 or higher. You also need a CUDA driver that is compatible with your GPU and TensorRT version. For software, you need a Linux or Windows operating system that supports your GPU and driver. You also need a C++ or Python compiler that is compatible with your operating system and TensorRT version.
How do I update TensorRT to a newer version?
To update TensorRT to a newer version, you need to uninstall the previous version and install the new version using one of the methods described in the installation section. You also need to update any dependencies such as CUDA, cuDNN, NCCL, etc. that are required by the new version of TensorRT.
How do I troubleshoot TensorRT errors or issues?
To troubleshoot TensorRT errors or issues, you can use several tools and techniques such as:
Logger: You can use a logger object that implements the nvinfer1::ILogger interface to capture and display error messages from TensorRT. You can pass this object to the builder, parser, runtime, or engine when creating them.
Profiler: You can use a profiler object that implements the nvinfer1::IProfiler interface to measure and report the execution time of each layer in your network. You can pass this object to the context when creating it and enable the profiler using the setProfiler() method of the context.
Debugger: You can use a debugger tool such as NVIDIA Nsight Systems or NVIDIA Nsight Compute to inspect and analyze the GPU activity and performance of your TensorRT application. You can launch your application with these tools and capture traces, metrics, kernels, etc.
How do I compare TensorRT performance with other frameworks or platforms?
To compare TensorRT performance with other frameworks or platforms, you can use a benchmarking tool such as MLPerf Inference or NVIDIA DeepStream. These tools provide standardized and reproducible measurements of inference latency, throughput, accuracy, and power consumption across various domains and tasks. You can run these tools on your own hardware or use pre-trained models and results provided by NVIDIA or other vendors.
How do I get more information or support for TensorRT?
To get more information or support for TensorRT, you can use several resources such as:
Documentation: You can access the official documentation of TensorRT from the page. This page contains guides, references, tutorials, samples, release notes, etc. for different versions of TensorRT.
Forum: You can join the to ask questions, share feedback, report issues, or interact with other developers and NVIDIA experts. You can also search for existing topics or solutions related to your problem.
Blog: You can read the to learn about the latest news, updates, tips, tricks, best practices, and success stories related to TensorRT and other NVIDIA products.
Conclusion
In this article, I have given you an overview of TensorRT, a software that optimizes deep learning inference on NVIDIA GPUs. I have explained what TensorRT is, why you should use it, how to install it, and how to use it for various tasks. I have also answered some frequently asked questions about TensorRT. I hope you have found this article useful and informative.
If you want to learn more about TensorRT or try it out for yourself, you can visit the . There you can find more resources such as documentation, samples, downloads, forums, blogs, etc. You can also contact NVIDIA for technical support or feedback.
Thank you for reading this article. I hope you have enjoyed it and learned something new. ? 44f88ac181
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