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trt-engine-explorer

This repository contains Python code (trex package) to explore various aspects of a TensorRT engine plan and its associated inference profiling data.

An engine plan file is a serialized TensorRT engine format. It contains information about the final inference graph and can be deserialized for inference runtime execution. An engine plan is specific to the hardware and software versions of the system used to build the engine.

trex is useful for initial model performance debugging, visualization of plan graphs, and for understanding the characteristics of an engine plan. For in-depth performance analysis, Nvidia ® Nsight Systems ™ is the recommended performance analysis tool.

Features

The trex package contains an API and Jupyter notebooks for viewing and inspecting TensorRT engine-plan files and profiling data.

  • An engine plan graph (JSON) is loaded to a Pandas dataframe which allows slicing, querying, filtering, viewing and diagraming.
  • An engine plan graph can be visualized as SVG/PNG files.
  • Layer linters are an API for flagging potential performance hazards (preview feature).
  • Four Jupyter notebooks provide:
    • An introduction to trex tutorial.
    • trex API examples.
    • Detailed engine plan performance, characteristics and structure analysis.
    • Comparison of two or more engine plans.
  • Because trex operates on JSON input files, it does not require a GPU.

Caveats

When trtexec times individual layers, the total engine latency (computed by summing the average latency of each layer) is higher than the latency reported for the entire engine. This is due to per-layer measurement overheads.

To measure per-layer execution times, when trtexec enqueues kernel layers for execution in a stream, it places CUDA event objects between the layers to monitor the start and completion of each layer. These CUDA events add a small overhead which is more noticeable with smaller networks (shallow and narrow networks or networks with small activation data).

Supported TensorRT Versions

Starting with TensorRT 8.2, engine-plan graph and profiling data can be exported to JSON files. trex supports TensortRT 8.x, 9.x and 10.0.

trex has only been tested on 22.04 with Python 3.10.12.
trex does not require a GPU, but generating the input JSON file(s) does require a GPU.

Installation

The instructions below detail how to use a Python3 virtualenv for installing and using trex (Python 3.8+ is required).

1. Clone the trex code repository from TensorRT OSS repository

$ git clone https://github.com/NVIDIA/TensorRT.git

2. Create and activate a Python virtual environment

The commands listed below create and activate a Python virtual environment named env_trex which is stored in a directory by the same name, and configures the current shell to use it as the default python environment.

$ cd TensorRT/tools/experimental/trt-engine-explorer
$ python3 -m virtualenv env_trex
$ source env_trex/bin/activate

3. Install trex in development mode

To install core functionality only:

$ python3 -m pip install -e .

To install all packages (core + packages required for using Jupyter notebooks):

$ python3 -m pip install -e .[notebook]

4. Install Graphviz

Generating dot and SVG graphs requires Graphviz, an open source graph visualization software:

$ sudo apt --yes install graphviz

Workflow

The typical trex workflow is depicted below:

  1. Convert an external model to a TensorRT INetworkDefinition.
  2. Build a TensorRT engine.
  3. Profile the engine while creating the necessary JSON files.
  4. Explore the engine by loading the JSON files in a trex notebook.

The Python script utils/process_engine.py implements this workflow for ONNX models:

  1. Use trtexec to import an ONNX model and create an engine.
  2. Load the engine and create an engine-graph JSON file.
  3. Use trtexec to profile the engine's inference execution and store the results in an engine profiling JSON file.
  4. Create an engine graph diagram in SVG format.

For more information see TensorRT Engine Inspector and the Tutorial notebook.

Jupyter Server

Launch the Jupyter notebook server as detailed below and open your browser at http://localhost:8888 or http://<your-ip-address>:8888

$ jupyter-notebook --ip=0.0.0.0 --no-browser

If you're using JupyterLab, you can launch the server with:

$ jupyter lab --ip=0.0.0.0 --port=8888

License

The TensorRT Engine Explorer license can be found in the LICENSE file.