kalfazed / tensorrt_starter

This repository give a guidline to learn CUDA and TensorRT from the beginning.

CUDA and TensorRT Starter Workspace

This repository guides freshmen who does not have background of parallel programming in C++ to learn CUDA and TensorRT from the beginning.

• CUDA and TensorRT Starter Workspace
  • How to install
  • How to run
  • Chapter description
    • chapter1-build-environment
    • chapter2-cuda-programming
    • chapter3-tensorrt-basics-and-onnx
    • chapter4-tensorrt-optimiztion
    • chapter5-tensorrt-api-basics
    • chapter6-deploy-classification-and-inference-design
    • chapter7-deploy-yolo-detection

This repository is still working in progress(~24/02/21). I will add some more samples and more detailed description in the future. Please feel free to contribute to this repository

How to install

Please pull the repository firstly

git clone [e-mail]:kalfazed/tensorrt_starter.git

After clone the repository, please modify the opencv, cuda, cudnn, and TensorRT version and install directory in config/Makefile.config located in the root direcoty of the repository. The recommaned version in this repository is opencv==4.x, cuda==11.6, cudnn==8.9, TensorRT==8.6.1.6

# Please change the cuda version if needed
# In default, cuDNN library is located in /usr/local/cuda/lib64
CXX                         :=  g++
CUDA_VER                    :=  11

# Please modify the opencv and tensorrt install directory
OPENCV_INSTALL_DIR          :=  /usr/local/include/opencv4
TENSORRT_INSTALL_DIR        :=  /home/kalfazed/packages/TensorRT-8.6.1.6

Besides, please also change the ARCH in config/Makefile.config. This parameter will be used by nvcc, which is a compiler for cuda program.

How to run

Inside each subfolder of each chapter, the basic directory structure is as follow: (For some chapters, it will be different)

|-config
    |- Makefile.config
|-src
    |- cpp
        |- xxx.c
    |- python
        |- yyy.py
|-Makefile

Please run make firstly, then it will generate a binary named trt-cuda or trt-infer, depending on different chapters. Pleae run the binary directly or run make run command.

Chapter description

chapter1-build-environment

• 1.0-build-environment

chapter2-cuda-programming

• 2.1-dim_and_index
• 2.2-cpp_cuda_interactive
• 2.3-matmul-basic
• 2.4-error-handler
• 2.5-device-info
• 2.6-nsight-system-and-compute
• 2.7-matmul-shared-memory
• 2.8-bank-conflict
• 2.9-stream-and-event
• 2.10-bilinear-interpolation
• 2.11-bilinear-interpolation-template
• 2.12-affine-transformation
• 2.13-implicit-gemm-conv
• 2.14-pcd-voxelization

chapter3-tensorrt-basics-and-onnx

• 3.1-generate-onnx
• 3.2-export-onnx
• 3.3-read-and-parse-onnx
• 3.4-export-unsupported-node
• 3.5-onnxsurgeon
• 3.6-export-onnx-from-oss
• 3.7-trtexec-analysis

chapter4-tensorrt-optimiztion

• 4.1-polygraphy[WIP]

chapter5-tensorrt-api-basics

• 5.1-mnist-sample
• 5.2-load-model
• 5.3-infer-model
• 5.4-print-structure
• 5.5-build-model
• 5.6-build-sub-graph
• 5.7-custom-basic-trt-plugin
• 5.8-plugin-unit-test

chapter6-deploy-classification-and-inference-design

• 6.0-preprocess-speed-compare
• 6.1-deploy-classification
• 6.2-deploy-classification-advanced
• 6.3-int8-calibration
• 6.4-trt-engine-inspector

chapter7-deploy-yolo-detection

• 7.1-load-save-tensor
• 7.2-affine-transformation
• 7.3-deploy-yolo-basic
• 7.4-quantization-analysis
• 7.5-deploy-yolo-multitask