Beginners

For those who are new to AI, deep neural network training can be daunting. To help with this, TAO provides an easy-to-use command line interface–the TAO CLI Launcher–to interact with and run TAO workflows.

The TAO Launcher is a lightweight, Python-based, command-line interface, which acts as a front-end for TAO containers built on top of PyTorch, TensorFlow, and TensorRT. The CLI abstracts information about what network actions are implemented in which container. When you use the CLI, the respective container gets launched automatically based on the model you plan to use.

After you have downloaded the getting started resource using the instructions for the package content, you can get started with the launcher as follows:

1. Installing the prerequisite software required to run the CLI

2. Setting up your Python environment

3. Installing the TAO launcher

4. Running a sample launcher TAO tutorial notebook

The TAO Launcher is strictly a Python3 package, capable of running on Python versions >= 3.10.

1. Install docker-ce by following the official instructions.

   Note

   After you have installed docker-ce, follow the post-installation steps to ensure that Docker can be run without sudo. The launcher requires that the docker CLI be available to run without superuser (sudo) privileges.

2. Install nvidia-container-toolkit by following the install-guide.

3. Get an NGC account and API key:

   1. Go to NGC and click the TAO container in the Catalog tab. This message will be displayed: “Sign in to access the PULL feature of this repository”.

   2. Enter your Email address and click Next, or click Create an Account.

   3. Choose your organization when prompted for Organization/Team.

   4. Click Sign In.

4. Log in to the NGC Docker registry (nvcr.io) using the command:

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   docker login nvcr.io
           

   Then, enter the following credentials:

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   a. Username: "$oauthtoken"
   b. Password: "YOUR_NGC_API_KEY"
           

   
   

   where YOUR_NGC_API_KEY corresponds to the key you generated from step 3.

We recommend setting up a Python environment using miniconda. The following instructions show how to setup a Python conda environment.

1. Follow the instructions in this link to set up a Conda environment using Miniconda.

2. After you have installed miniconda, create a new environment and set the Python version to 3.10.

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   conda create -n launcher python=3.10
           

   
   

3. Activate the conda environment that you have just created.

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   conda activate launcher
           

   
   

4. Verify that the command prompt shows the name of your Conda environment.

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   (launcher) py-3.10 desktop:
           

   
   

5. Set the notebooks to have the same Python kernel as the virtual environment:

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   python -m pip install ipykernel
   python -m ipykernel install --user --name launcher --display-name "launcher"
           

   
   

When you are done with your session, you can deactivate your conda environment using the deactivate command:

            

            
conda deactivate
        

You may re-instantiate this conda environment using the following command:

            

            
conda activate launcher
        

As part of the getting started package, a quick start script is included to install the TAO launcher CLI package and its Python dependencies, along with the NGC CLI required to pull models and interact with the model, Docker, and resource registry on the NVIDIA GPU Cloud.

1. Install the CLI launcher and the NGC CLI using the quick start script downloaded with the getting_started NGC package from the package content.

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   bash setup/quickstart_launcher.sh --install
           

2. Update the launcher to the latest version of TAO by running the following command:

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   bash setup/quickstart_launcher.sh --upgrade
           

3. Invoke the entry points using the tao command:

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   tao --help
           

   
   

   The following is sample output of the above command:

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   usage: tao [-h] {list,stop,info,dataset,deploy,model} ...
   
   Launcher for TAO
   
   optional arguments:
   -h, --help            show this help message and exit
   
   task_groups:
         {list,stop,info,dataset,deploy,model}
           

   Under task_groups you can see all the launcher-invokable tasks. The following are the tasks that handle launched commands using the TAO Launcher:

   • list

   • stop

   • info

            

            
export PATH=$PATH:~/.local/bin
        

Now that you have installed the TAO launcher and the tutorial notebooks from GitHub, you can get started by running a sample tutorial notebook.

The following command allows you to invoke a sample notebook:

            

            
jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root
        

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

            

            
http://0.0.0.0:8888
        

Execute the cells in the notebook to train a model using TAO.

You may choose any one of the sample notebooks below to get started with TAO. As a starting point, NVIDIA recommends running the GroundingDINO open vocabulary detection notebook.

The tables below provide a list of the TAO tutorial notebooks.

Purpose-Built Pre-Trained Models

The following is a list of purpose-built pre-trained models mapped with their corresponding samples.

Open Model Architectures

The TAO Docker gives you access to a repository of pretrained models that can serve as a starting point when training deep neural networks. These models are hosted on the NGC. Follow these steps to download the models:

1. Download the NGC CLI and install it. More information about the NGC Catalog CLI is available here.

2. Follow the instructions below to configure the NGC CLI and download the models.

ListingAall Available Models

Use this command to get a list of models that are hosted in the NGC model registry:

            

            
ngc registry model list <model_glob_string>
        

Here is an example of using this command for the computer vision models:

            

            
ngc registry model list nvidia/tao/pretrained_*
        

This command returns a list of the pretrained backbones available for different tasks:

            

            
+----------------------------------------+----------------------------------------+----------------------------------------+-----------------------+---------------------------+-----------+---------------+------------+-------------+---------------------+
| Name                                   | Repository                             | Latest Version                         | Application           | Framework                 | Precision | Last Modified | Permission | Access Type | Associated Products |
+========================================+========================================+========================================+=======================+===========================+===========+===============+============+=============+=====================+
| TAO Pretrained EfficientDet            | nvidia/tao/pretrained_efficientdet     | efficientnet_b2                        | Object Detection      | Transfer Learning Toolkit | FP32      | Apr 04, 2023  | unlocked   |             |                     |
| Pre-trained DINO ImageNet weights      | nvidia/tao/pretrained_dino_imagenet    | gcvit_large_imagenet22k_384            | Object Detection      | TAO Toolkit               | FP32      | Oct 16, 2023  | unlocked   |             |                     |
| TAO Pretrained DetectNet V2            | nvidia/tao/pretrained_detectnet_v2     | resnet34                               | Object Detection      | Transfer Learning Toolkit | FP32      | Apr 04, 2023  | unlocked   |             |                     |
| Pre-trained DINO NvImageNet weights    | nvidia/tao/pretrained_dino_nvimagenet  | resnet50                               | Object Detection      | TAO Toolkit               | FP32      | Oct 16, 2023  | unlocked   |             |                     |
| DINO                                   | nvidia/tao/pretrained_dino_coco        | dino_fan_large_trainable_v1.0          | Object Detection      | TAO Toolkit               | FP32      | Feb 03, 2024  | unlocked   |             |                     |
| TAO Pretrained Classification          | nvidia/tao/pretrained_classification   | cspdarknet_tiny                        | Classification        | Transfer Learning Toolkit | FP32      | Apr 04, 2023  | unlocked   |             |                     |
| TAO Pretrained Object Detection        | nvidia/tao/pretrained_object_detection | cspdarknet_tiny                        | Other                 | Other                     | FP32      | May 03, 2024  | unlocked   |             |                     |
| TAO Pretrained EfficientDet-TF2        | nvidia/tao/pretrained_efficientdet_tf2 | efficientnet_b0                        | OBJECT_DETECTION      | TransferLearningToolkit   | FP32      | Dec 13, 2022  | unlocked   |             |                     |
| Pretrained Mask Auto Label             | nvidia/tao/pretrained_mask_auto_label  | vit-base                               | Semantic Segmentation | Transfer Learning Toolkit | FP32      | Jul 27, 2023  | unlocked   |             |                     |
+----------------------------------------+----------------------------------------+----------------------------------------+-----------------------+---------------------------+-----------+---------------+------------+-------------+---------------------+
        

To view the full list of models, use the following command:

            

            
ngc registry model list nvidia/tao/*
        

Downloading a Model

Use this command to download the model you have chosen from the NGC model registry:

            

            
ngc registry model download-version <org/team/model_name:version> -dest <path_to_download_dir>
        

For example, use this command to download the resnet 18 classification model to the $USER_EXPERIMENT_DIR directory:

            

            
ngc registry model download-version
nvidia/tao/pretrained_classification:resnet18 --dest $USER_EXPERIMENT_DIR/pretrained_resnet18
        

            

            
Downloaded 82.41 MB in 9s, Download speed: 9.14 MB/s
----------------------------------------------------
Transfer id: pretrained_classification_vresnet18 Download status: Completed.
Downloaded local path: /workspace/tao-experiments/pretrained_resnet18/
Total files downloaded: 2
Total downloaded size: 82.41 MB
Started at: 2019-07-16 01:29:53.028400
Completed at: 2019-07-16 01:30:02.053016
Duration taken: 9s seconds
        

After training is complete, follow these instructions to deploy a computer vision model to DeepStream.