ipengxpro/tao_tutorials

Quick start scripts and tutorial notebooks to get started with TAO Toolkit

TAO Toolkit - Tutorials

• Introduction
• Requirements
  • Hardware requirements
    • Minimum system configuration
    • Recommended system configuration
  • Software requirements
• Package Content
  • File Hierarchy
• Jupyter notebooks
• Blogs
• License

Introduction

TAO Toolkit is a Python package hosted on the NVIDIA Python Package Index. It interacts with lower-level TAO dockers available from the NVIDIA GPU Accelerated Container Registry (NGC). The TAO containers come pre-installed with all dependencies required for training. The output of the TAO workflow is a trained model that can be deployed for inference on NVIDIA devices using DeepStream, TensorRT and Triton.

This repository contains the tutorial notebooks for installing and running TAO Toolkit. The notebooks would allow you to leverage the simplicity and convenience of TAO to train, prune, retrian, quantize, export and run inference.

TAO quick start video.

Requirements

Hardware requirements

The following system configuration is recommended to achieve reasonable training performance with TAO Toolkit and supported models provided:

Minimum system configuration

• 8 GB system RAM
• 4 GB of GPU RAM
• 8 core CPU
• 1 NVIDIA GPU
• 100 GB of SSD space

Recommended system configuration

• 32 GB system RAM
• 32 GB of GPU RAM
• 8 core CPU
• 1 NVIDIA GPU
• 100 GB of SSD space

TAO Toolkit is supported on discrete GPUs, such as A100, A40, A30, A2, A16, A100x, A30x, V100, T4, Titan-RTX and Quadro-RTX.

Note: TAO Toolkit is not supported on GPU's before the Pascal generation

Software requirements

Software	Version	Comment
Ubuntu LTS	20.04
python	>=3.6.9<3.7	Not needed if you are using TAO API (See #3 below)
docker-ce	>19.03.5	Not needed if you are using TAO API (See #3 below)
docker-API	1.40	Not needed if you are using TAO API (See #3 below)
nvidia-container-toolkit	>1.3.0-1	Not needed if you are using TAO API (See #3 below)
nvidia-container-runtime	3.4.0-1	Not needed if you are using TAO API (See #3 below)
nvidia-docker2	2.5.0-1	Not needed if you are using TAO API (See #3 below)
nvidia-driver	>525.85	Not needed if you are using TAO API (See #3 below)
python-pip	>21.06	Not needed if you are using TAO API (See #3 below)

Package Content

Download the TAO package which contains startup scripts, Jupyter notebooks and config files.
TAO is supported on Google Colab; if you want to try on Colab, you can skip this step and directly scroll down to #4 in the How to run TAO section.

    ngc registry resource download-version "nvidia/tao/tao-getting-started:5.2.0"
    cd ./tao-getting-started_v5.2.0

File Hierarchy

    setup
        |--> quickstart_launcher.sh
        |--> quickstart_api_bare_metal
        |--> quickstart_api_aws_eks
        |--> quickstart_api_azure_aks
        |--> quickstart_api_gcp_gke
    notebooks
        |--> tao_api_starter_kit
            |--> api
                |--> automl
                |--> end2end
                |--> dataset_prepare
            |--> client
                |--> automl
                |--> end2end
                |--> dataset_prepare
        |--> tao_launcher_starter_kit
            |--> dino
            |--> deformable_detr
            |--> ocdnet
            |-->  ...

Jupyter notebooks

All Notebooks and required spec files are provided in this package. The table below maps which notebook to use for fine-tuning either a purpose-build models like PeopleNet or an open model architecture like YOLO.

Purpose-built Model	Launcher CLI notebook
ActionRecognitionNet	notebooks/tao_launcher_starter_kit/action_recognition_net/action_recognition_net.ipynb
Mask Auto Label	notebooks/tao_launcher_starter_kit/mal/mal.ipynb
OCRNet	notebooks/tao_launcher_starter_kit/ocrnet/ocrnet.ipynb
OCDNet	notebooks/tao_launcher_starter_kit/ocdnet/ocdnet.ipynb
Pointpillars	notebooks/tao_launcher_starter_kit/pointpillars/pointpillars.ipynb
PoseClassificationNet	notebooks/tao_launcher_starter_kit/pose_classification_net/poseclassificationnet.ipynb
ReIdentificationNet	notebooks/tao_launcher_starter_kit/re_identification_net/reidentificationnet.ipynb
CitySemSegFormer	notebooks/tao_launcher_starter_kit/segformer/segformer.ipynb

Open model architecture	Jupyter notebook
Deformable DETR	notebooks/tao_launcher_starter_kit/deformable_detr/deformable_detr.ipynb
DINO	notebooks/tao_launcher_starter_kit/dino/dino.ipynb
Image Classification	notebooks/tao_launcher_starter_kit/classification_pyt/classification_pyt.ipynb
Optical Inspection	notebooks/tao_launcher_starter_kit/optical_inspection/optical_inspection.ipynb
Segformer	notebooks/tao_launcher_starter_kit/segformer/segformer.ipynb

Blogs

Train like a 'pro' with AutoML in TAO
Deploy TAO on Azure ML
Synthetic Data and TAO
Action Recognition Blog
Real-time License Plate Detection
2 Pose Estimation: Part 1
Part 2
Building ConvAI with TAO Toolkit

License

This project is licensed under the Apache-2.0 License.