Learn Tensorflow-Pytorch-Tensorrt-Onnx-From Scratch

Docker, Tensorflow, Pytorch, Onnx, TensorRT, model detection, model classification, model fine-tuning

What you'll learn

1. What is Docker and How to use Docker

2. What is Kubernet and How to use with Docker

3. Nvidia SuperComputer and Cuda Programming Language

4. What are OpenCL and OpenGL and when to use ?

6. Tensorflow and Pytorch Installation, Configuration with Docker

7. DockerFile, Docker Compile and Docker Compose Debug file configuration

8. Different YOLO version, comparisons, and when to use which version of YOLO according to your problem

9. Jupyter Notebook Editor as well as Visual Studio Coding Skills

10. Visual Studio Code Setup and Docker Debugger with VS

11. what is ONNX fframework and how to use apply onnx to your custom problems

11. What is TensorRT Framework and how to use apply to your custom problems

12. Custom Detection, Classification, Segmentation problems and inference on images and videos

13. Python3 Object Oriented Programming

14. Pycuda Language programming

15. Deep Learning Problem Solving Skills on Edge Devices, and Cloud Computings

16. How to generate High Performance Inference Models , in order to get high precision, FPS detection as well as less gpu memory consumption

17. Visual Studio Code with Docker

Requirements

basic python programming knowledge

basic deep learning knowledge

Description

This course is mainly considered for any candidates(students, engineers,experts) that have great motivation to learn deep learning model training and deeployment. Candidates will have deep knowledge of docker, and usage of tensorflow ,pytorch, keras models with docker. In addition, they will be able to optimize and quantize/optimize deeplearning models with ONNX and TensorRT frameworks for deployment in variety of sectors such as on edge devices (nvidia jetson nano, tx2, agx, xavier), automative, robotics  as well as cloud computing via aws and google platform.  Overview of Nvidia Devices and Cuda compiler languageOverview Knowledge of OpenCL and OpenGL Learning and Installation of Docker from scratchPreparation of DockerFiles, Docker Compose as well as Docker Compose Debug fileImplementing and Python codes via both Jupyter notebook as well as Visual studio codeConfiguration and Installation of Plugin packages in Visual Studio CodeLearning, Installation and Confguration of frameworks such as Tensorflow, Pytorch, Kears with docker images from scratchPreprocessing and Preparation of Deep learning datasets for training and testingOpenCV  DNN Training, Testing and Validation of Deep Learning frameworksConversion of prebuilt models to Onnx  and Onnx Inference on imagesConversion of onnx model to TensorRT engine TensorRT engine Inference on images and videosComparison of achieved metrices and result between TensorRT and Onnx Inference

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Overview with Projects

Section 2: Onnx, TensorRT, Docker Overview

Lecture 3 Onnx, TensorRT, Docker Tutorial (part 1)

Lecture 4 Onnx, TensorRT, Docker Tutorial (part 2)

Lecture 5 Onnx, TensorRT, Docker Tutorial (part 3)

Lecture 6 Onnx, TensorRT, Docker Tutorial (part 4)

Section 3: NVIDIA Drivers

Lecture 7 how to install nvidia drivers and set up

Lecture 8 Download Nvidia Driver (Part two)

Lecture 9 Verify Installation of Nvdia Driver and Nouveau Driver

Lecture 10 Verify Installation of Nvidia Driver (Part two)

Section 4: Nvidia Hardware and Software, Cuda programming API Levels

Lecture 11 Docker and Nvidia Stack (Part One)

Lecture 12 Docker and Nvidia Gpu Stack (Part two)

Lecture 13 Docker and Nvidia Gpu Stack (Part three)

Section 5: Docker Installation and Configuration

Lecture 14 Docker Images Installation and Configuration

Lecture 15 Docker SetUp and Configuration with Sudo on Local Machine

Lecture 16 Setup Docker Successfuly on your local Machine

Section 6: Installation of Docker Cuda Toolkit & Setup DockerFile with required packages

Lecture 17 Installing Docker Cuda Toolkit-Nvidia GPU

Lecture 18 Install, Configure,Validate Tensorflow-GPU Docker Image

Lecture 19 What is Docker? and why we need to use Docker Server-Docker Commands Tutorial

Lecture 20 Configuration of Docker Working Directories and DockerFiles

Lecture 21 Organization of Docker files with required packages installations (Part One)

Lecture 22 Organization of Docker files with required packages installations (Part Two)

Section 7: TensorRT & Onnx AI frameworks

Lecture 23 Driver , Kernel and Device Communication

Lecture 24 Deep Learning Frameworks

Lecture 25 Open Neural Network Exchange

Lecture 26 TensorRT - NVIDIA Inference

Lecture 27 TensorRT - NVIDIA Inference Floating Point Precision and AI Sectors

Lecture 28 Nvidia Software and Hardware Logic

Section 8: Resnet 18 with ONNX-TENSORRT

Lecture 29 Docker Configuration for Resnet 18

Lecture 30 Docker Configuration for Resnet 18 (Part 2)

Lecture 31 SetUp Visual Studio Code with Docker Container

Lecture 32 Resnet 18 with ONNX

Lecture 33 Resnet 18 Conversion from Onnx to TensorRT (Part 1)

Lecture 34 Resnet 18 Conversion from Onnx to TensorRT (Part 2)

Lecture 35 Resnet 18 Conversion from Onnx to TensorRT (Part 3)

Lecture 36 Resnet 18 Conversion from Onnx to TensorRT (Part 4)

Section 9: Resnet 18 TensorRT Inference

Lecture 37 TensorrT Inference (Part 1)

Lecture 38 TensorrT Inference 2

Lecture 39 TensorrT Inference 3

Lecture 40 TensorrT Inference 4

Lecture 41 TensorrT Inference 5

Lecture 42 TensorrT Inference 6

Lecture 43 TensorrT Inference 7

Lecture 44 TensorrT Inference-TtrtExec API 8

Section 10: YOLOV4 ONNX DNN

Lecture 45 YOLOV4 ONNX DNN Inference (Part One)

Lecture 46 YOLOV4 ONNX DNN Inference (Part Two)

Lecture 47 YOLOV4 ONNX DNN Inference (Part Three)

Lecture 48 YOLOV4 ONNX DNN Inference (Part Four)

Lecture 49 YOLOV4 ONNX DNN Inference (Part Fifth)

Lecture 50 YOLOV4 ONNX DNN Inference (Part Six)

Lecture 51 YOLOV4 ONNX DNN Inference (Part Seven)

Lecture 52 YOLOV4 ONNX DNN Inference (Part Eight)

Lecture 53 YOLOV4 ONNX DNN Inference (Part Nine)

Section 11: YOLOV4 ONNX DNN Video

Lecture 54 Yolov4 Video Inference part 1

Lecture 55 Yolov4 Video Inference part 2

Lecture 56 Yolov4 Video Inference part 3

Lecture 57 Yolov4 Video Inference part 4

Section 12: YOLOv5 Onnx Inference - OpenCV

Lecture 58 SetUp Workign Directory of yolov5 (2)

Lecture 59 YOLOv5 Onnx Inference - OpenCV (Part 3)

Lecture 60 YOLOv5 Onnx Inference - OpenCV (Part 4)

Lecture 61 YOLOv5 Onnx Inference - OpenCV (Part 5)

Lecture 62 YOLOv5 Onnx Inference - OpenCV (Part 6)

Lecture 63 YOLOv5 Onnx Inference - OpenCV (Part 7)

Lecture 64 YOLOv5 Onnx Inference - OpenCV (Part 8)

Lecture 65 Yolov5 Onnx Inference (Part 9)

Lecture 66 Prepare Yolov5 For Inference ( Part 1)

Section 13: Yolov5 TensorRT Inference

Lecture 67 TensorRT-yoloV5 Inference (Part 1)

Lecture 68 TensorRT-yoloV5 Inference (Part 2)

Lecture 69 TensorRT-yoloV5 Inference (Part 3)

Lecture 70 TensorRT-yoloV5 Inference (Part 4)

Lecture 71 TensorRT-yoloV5 Inference (Part 5)

Lecture 72 TensorRT-yoloV5 Inference (Part 6)

Lecture 73 TensorRT-yoloV5 Inference (Part 7)

Lecture 74 TensorRT-yoloV5 Inference (Part 8)

Lecture 75 TensorRT-yoloV5 Inference (Part 9)

Section 14: TensorRT Tutoruial Without Local GPU, only with Google Colab

Lecture 76 TensorRT Tutorial On Google Colab

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