Getting Started

This section provides a video- and text-based quick start guide for installing and running TAO.

Hardware Requirements

TAO is supported on discrete GPUs such as H100, A100, A40, A30, A2, A16, A100x, A30x, V100, T4, Titan-RTX, and Quadro-RTX.

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 is supported on discrete GPUs, such as H100, A100, A40, A30, A2, A16, A100x, A30x, V100, T4, Titan-RTX, and Quadro-RTX.

Software Requirements

To help you get started with the TAO, NVIDIA distributes a package that consists of setup scripts and tutorial notebooks in GitHub under the tao_tutorials repository.

You can download this resource by cloning the repository to your local machine with the following command:

            

            
git clone https://github.com/NVIDIA/tao_tutorials.git
        

The file hierarchy and contents of the package are as follows:

            

            
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
      |--> classification_pyt
      |--> ocdnet
      |-->  ...

   |--> tao_data_services

      |--> data
      |-->  ...
        

The tao_tutorials repository is broadly classified into two components:

• setup: A set of quick start scripts to help you install and deploy the TAO launcher and the TAO APIs on various Cloud Service Providers.

• notebooks: Beginner friendly end-to-end tutorial notebooks that will help you hit the ground running with TAO. The notebooks install TAO, download the required data, and run TAO commands end-to-end for various use cases.

  These notebooks are split into three categories:

  • tao_api_starter_kit: End-to-end notebooks that help you learn the features supported by the TAO API model of execution.

    The notebooks under the api directory work directly at the REST API level using REST API requests, while the client directory uses the TAO Client CLI to interact with the API server.

  • tao_launcher_starter_kit: Sample notebooks that walk you through the end-to-end workflow for all the

    computer-vision models supported in TAO. You can interact with TAO using the TAO launcher CLI.

  • tao_data_services: Sample notebooks that walk you through the end-to-end workflow of the different

    dataset manipulation and annotation tools that are included as part of TAO.

TAO is built for users with varying levels of AI expertise. The getting started guide is thus split into different sections for different levels of user experience:

• Beginners

• Intermediate Users

• Advanced Users