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NVIDIA's Launcher for TAO Toolkit.

Project description

TAO Toolkit Quick Start

The NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, simplifies and accelerates the model training process by abstracting away the complexity of AI models and the deep learning framework. You can use the power of transfer learning to fine-tune NVIDIA pretrained models with your own data and optimize the model for inference throughput — all without the need for AI expertise or large training datasets.

TAO quick start video.


Minimum Hardware requirements

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

  • 32 GB system RAM
  • 32 GB of GPU RAM
  • 8 core CPU
  • 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 >520 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.

wget --content-disposition -O
unzip -u  -d ./getting_started_v4.0.0 && rm -rf && cd ./getting_started_v4.0.0

File Hierarchy

    |--> quickstart_api_bare_metal
    |--> quickstart_api_aws_eks
    |--> tao_api_starter_kit
        |--> api
            |--> automl
            |--> end2end
            |--> dataset_prepare
        |--> client
            |--> automl
            |--> end2end
            |--> dataset_prepare
    |--> tao_launcher_starter_kit
        |--> yolov4_tiny
        |--> yolov4
        |--> yolov3
        |-->  ...

How to run TAO?

TAO is available as a docker container or as a collection of Python wheels.

There are 4 ways to run TAO depending on user preference and their setup. See the full list below.

1. Launcher CLI

The TAO Launcher is a lightweight Python based CLI application to run TAO. The launcher basically acts as a front-end for the multiple TAO Toolkit containers built on both PyTorch and Tensorflow. The multiple containers essentially get launched automatically based on the type of model you plan to use for your computer vision or conversational AI use-cases.

TAO Launcher

To get started, use the setup/ to validate your setup and install TAO launcher. Jupyter notebooks to train using the Launcher is provided under notebooks/launcher_starter_kit.

Detail instructions on installing pre-requisite and setup is provided in TAO documentation - Launcher

2. Directly from Container

Users have option to also run TAO directly using the docker container. To use container directly, user needs to know which container to pull. There are multiple containers under TAO, and depending on the model that you want to train you will need to pull the appropriate container. This is not required when using the Launcher CLI.

export DOCKER_NAME="nvidia/tao/tao-toolkit"
export DOCKER_TAG="***" ## for TensorFlow docker
export DOCKER_TAG="***" ## for PyTorch docker

docker run -it --rm --gpus all -v /path/in/host:/path/in/docker $DOCKER_CONTAINER \
detectnet_v2 train -e /path/to/experiment/spec.txt -r /path/to/results/dir -k $KEY --gpus 4

More information about running directly from docker is provided in TAO documentation - Container


TAO Toolkit API is a Kubernetes service that enables building end-to-end AI models using REST APIs. The API service can be installed on a Kubernetes cluster (local / AWS EKS) using a Helm chart along with minimal dependencies. TAO toolkit jobs can be run using GPUs available on the cluster and can scale to a multi-node setting. Users can use a TAO client CLI to interact with TAO services remotely or can integrate it in their own apps and services directly using REST APIs.


To get started, use the provided one-click deploy script to deploy either on bare-metal setup or on managed Kubernetes service like Amazon EKS. Jupyter notebooks to train using the APIs directly or using the client app is provided under notebooks/api_starter_kit

More information about setting up the API services and the API is provided in TAO documentation - API

4. Python Wheel

Users can also run TAO directly on bare-metal without docker or K8s. Users can deploy TAO notebooks directly on Google Colab without having to configure infrastructure. The full instructions are provided in the Colab notebook below.

CV Task Model Arch One-click Deploy
Classification ResNet18 Train on Colab
Multi-task Classification ResNet18 Train on Colab
Object Detection Deformable-DETR Train on Colab
Object Detection DSSD Train on Colab
Object Detection EfficientDet Train on Colab
Object Detection RetinaNet Train on Colab
Object Detection SSD Train on Colab
Object Detection YOLOv3 Train on Colab
Object Detection YOLOv4 Train on Colab
Object Detection YoloV4 Tiny Train on Colab
Action Recognition ActionRecognition Train on Colab
OCR LPRNet Train on Colab
Pose Action Classification PoseClassificationNet Train on Colab
3D Point Cloud PointPillar Train on Colab
Emotion Recognition EmotionNet Train on Colab
Gesture Recognition GestureNet Train on Colab
Heart Rate Estimation HeartRateNet Train on Colab

After starting TAO service locally or remotely, start Jupyter notebook
jupyter notebook --ip --port 8888 --allow-root

Open an internet browser on localhost and navigate to the following URL:

Open the notebook that you are interested in training and start training.

Note: All the instructions to train, prune, optimize and download pretrained models are provided in the notebook.

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
PeopleNet notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
TrafficCamNet notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
DashCamNet notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
FaceDetectIR notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
VehicleMakeNet notebooks/tao_launcher_starter_kit/classification/classification.ipynb
VehicleTypeNet notebooks/tao_launcher_starter_kit/classification/classification.ipynb
PeopleSegNet notebooks/tao_launcher_starter_kit/mask_rcnn/mask_rcnn.ipynb
PeopleSemSegNet notebooks/tao_launcher_starter_kit/unet/unet_isbi.ipynb
Bodypose Estimation notebooks/tao_launcher_starter_kit/bpnet/bpnet.ipynb
License Plate Detection notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
License Plate Recognition notebooks/tao_launcher_starter_kit/lprnet/lprnet.ipynb
Gaze Estimation notebooks/tao_launcher_starter_kit/gazenet/gazenet.ipynb
Facial Landmark notebooks/tao_launcher_starter_kit/fpenet/fpenet.ipynb
Heart Rate Estimation notebooks/tao_launcher_starter_kit/heartratenet/heartratenet.ipynb
Gesture Recognition notebooks/tao_launcher_starter_kit/gesturenet/gesturenet.ipynb
Emotion Recognition notebooks/tao_launcher_starter_kit/emotionnet/emotionnet.ipynb
FaceDetect notebooks/tao_launcher_starter_kit/facenet/facenet.ipynb
ActionRecognitionNet notebooks/tao_launcher_starter_kit/action_recognition_net/actionrecognitionnet.ipynb
PoseClassificationNet notebooks/tao_launcher_starter_kit/pose_classification_net/pose_classificationnet.ipynb
Pointpillars notebooks/tao_launcher_starter_kit/pointpillars/pointpillars.ipynb
ReIdentificationNet notebooks/tao_launcher_starter_kit/re_identification_net/reidentificationnet.ipynb

Open model architecture Jupyter notebook
Deformable-DETR notebooks/tao_launcher_starter_kit/deformable_detr/deformable_detr.ipynb
SegFormer notebooks/tao_launcher_starter_kit/segformer/segformer.ipynb
DetectNet_v2 notebooks/tao_launcher_starter_kit/detectnet_v2/detectnet_v2.ipynb
FasterRCNN notebooks/tao_launcher_starter_kit/faster_rcnn/faster_rcnn.ipynb
YOLOV3 notebooks/tao_launcher_starter_kit/yolo_v3/yolo_v3.ipynb
YOLOV4 notebooks/tao_launcher_starter_kit/yolo_v4/yolo_v4.ipynb
YOLOv4-Tiny notebooks/tao_launcher_starter_kit/yolo_v4_tiny/yolo_v4_tiny.ipynb
SSD notebooks/tao_launcher_starter_kit/ssd/ssd.ipynb
DSSD notebooks/tao_launcher_starter_kit/dssd/dssd.ipynb
RetinaNet notebooks/tao_launcher_starter_kit/retinanet/retinanet.ipynb
MaskRCNN notebooks/tao_launcher_starter_kit/mask_rcnn/mask_rcnn.ipynb
UNET notebooks/tao_launcher_starter_kit/unet/unet_isbi.ipynb
Image Classification notebooks/tao_launcher_starter_kit/classification/classification.ipynb
EfficientDet notebooks/tao_launcher_starter_kit/efficientdet/efficientdet.ipynb

Conversational AI

For Conversational AI, all notebooks are available on NGC. Please download the notebook from the appropriate NGC resource as mentioned in the table below.

Conversational AI Task Jupyter Notebooks
Speech to Text Citrinet Speech to Text Citrinet Notebook
Speech to Text Conformer Speech to Text Conformer Notebook
Question Answering Question Answering Notebook
Text Classification Text Classification Notebook
Token Classification Token Classification Notebook
Punctuation and Capitalization Punctuation Capitalization Notebook
Intent and Slot Classification Intent Slot Classification Notebook
NGram Language Model NGram Language Model Notebook
Text to Speech Text to Speech Notebook

Important Links


Synthetic Data and TAO
Action Recognition Blog
Real-time License Plate Detection
2 Pose Estimation: Part 1
Part 2
Building ConvAI with TAO Toolkit


TAO Toolkit getting Started License for TAO containers is included within the container at workspace/EULA.pdf. License for the pre-trained models are available with the model files. By pulling and using the Train Adapt Optimize (TAO) Toolkit container to download models, you accept the terms and conditions of these licenses.

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