TLT Quick Start Guide

This page provides a quick start guide for installing and running TLT.

Hardware

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

• 32 GB system RAM

• 32 GB of GPU RAM

• 8 core CPU

• 1 NVIDIA GPU

• 100 GB of SSD space

TLT is supported on A100, V100 and RTX 30x0 GPUs.

Software Requirements

Installing the Pre-requisites

The tlt-launcher is strictly a python3 only package, capable of running on python 3.6.9 or 3.7.

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

   Once you have installed docker-ce, follow the post-installation steps to ensure that the docker can be run without sudo.

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 Transfer Learning Toolkit container in the Catalog tab. This message is 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 docker login nvcr.io and 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.

Installing TLT

The Transfer Learning Toolkit (TLT) is a Python pip package that is hosted on the NVIDIA PyIndex. The package uses the docker restAPI under the hood to interact with the NGC Docker registry to pull and instantiate the underlying docker containers. You must have an NGC account and an API key associated with your account. See the Installation Prerequisites section for details on creating an NGC account and obtaining an API key.

1. Create a new virtualenv using virtualenvwrapper.

   You may follow the instructions in this link to set up a Python virtualenv using a virtualenvwrapper.

   Once you have followed the instructions to install virtualenv and virtualenvwrapper, set the Python version in the virtualenv. This can be done in either of the following ways:

   • Defining the environment variable called VIRTUALENVWRAPPER_PYTHON. This variable should point to the path where the python3 binary is installed in your local machine. You can also add it to your .bashrc or .bash_profile for setting your Python virtualenv by default.

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     export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3
             

     
     

   • Setting the path to the python3 binary when creating your virtualenv using the virtualenvwrapper wrapper

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     mkvirtualenv launcher -p /path/to/your/python3
             

     
     

   Once you have logged into the virtualenv, the command prompt should show the name of your virtual environment

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   (launcher) py-3.6.9 desktop:
           

   When you are done with you session, you may deactivate your virtualenv using the deactivate command:

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   deactivate
           

   
   

   You may re-instantiate this created virtualenv env using the workon command.

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   workon launcher
           

2. Install the tlt launcher Python package called nvidia-tlt.

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   pip3 install nvidia-pyindex
   pip3 install nvidia-tlt
           

   Note

   The nvidia-tlt package is hosted in the nvidia-pyindex, which has to be installed as a pre-requisite to install nvidia-tlt.

   If you had installed an older version of nvidia-tlt launcher, you may upgrade to the latest version by running the following command.

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   pip3 install --upgrade nvidia-tlt
           

3. Invoke the entrypoints using the tlt command.

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   tlt --help
           

   The sample output of the above command is:

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   usage: tlt [-h]
            {list,stop,info,augment,bpnet,classification,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,gesturenet,
            heartratenet,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,
            retinanet,speech_to_text,ssd,text_classification,tlt-converter,token_classification,unet,yolo_v3,yolo_v4}
            ...
   
   Launcher for TLT
   
   optional arguments:
   -h, --help            show this help message and exit
   
   tasks:
         {list,stop,info,augment,bpnet,classification,detectnet_v2,dssd,emotionnet,faster_rcnn,fpenet,gazenet,gesturenet,heartratenet
         ,intent_slot_classification,lprnet,mask_rcnn,punctuation_and_capitalization,question_answering,retinanet,speech_to_text,
         ssd,text_classification,tlt-converter,token_classification,unet,yolo_v3,yolo_v4}
           

   Note that under tasks you can see all the launcher-invokable tasks. The following are the specific tasks that help with handling the launched commands using the TLT launcher:

   • list

   • stop

   • info

Running the Transfer Learning Toolkit

Information about the TLT launcher CLI and details on using it to run TLT supported tasks are captured in the section.

Use the examples

Example Jupyter notebooks for all the tasks that are supported in TLT are available in NGC resources. TLT provides sample workflows for Computer Vision and Conversational AI.

Computer Vision

All the samples for the supported computer vision tasks are hosted on ngc under the TLT Computer Vision Samples. To run the available examples, download this sample resource by using the following commands.

            

            
wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/tlt_cv_samples/versions/v1.1.0/zip -O tlt_cv_samples_v1.1.0.zip
unzip -u tlt_cv_samples_v1.1.0.zip  -d ./tlt_cv_samples_v1.1.0 && rm -rf tlt_cv_samples_v1.1.0.zip && cd ./tlt_cv_samples_v1.1.0
        

Conversational AI

The TLT Conversational AI package, provides several end to end sample workflows to train conversational AI models using TLT and subsequently deploying them to Riva. You can find these samples at:

You can download these resources, by using the NGC CLI command available at the NGC resource page. Once you download the respective tutorial resource, you may instantiate the jupyter notebook server.

            

            
pip3 install jupyter
jupyter notebook --ip 0.0.0.0 --allow-root --port 8888
        

Copy and paste the link produced from this command into your browser to access the notebook. The /workspace/examples folder will contain a demo notebook. Feel free to use any free port available to host the notebook if port 8888 is unavailable.

Downloading the Models

The Transfer Learning Toolkit 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. To download the models, please download the NGC CLI and install it. More information about the NGC Catalog CLI is available here. Once you have installed the CLI, you may follow the instructions below to configure the NGC CLI and download the models.

Configure the NGC API key

Using the NGC API Key obtained in Installation Prerequisites, configure the enclosed ngc cli by executing this command and following the prompts:

            

            
ngc config set
        

Get a list of 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>
        

For the computer vision models, here is an example of using this command:

            

            
ngc registry model list nvidia/tlt_pretrained_*
        

To view all the conversational AI models, you may using the following command:

            

            
ngc registry model list nvidia/tlt-riva/*
        

Download a model

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

            

            
ngc registry model download-version <ORG/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/tlt_pretrained_classification:resnet18 --dest
$USER_EXPERIMENT_DIR/pretrained_resnet18
        

            

            
Downloaded 82.41 MB in 9s, Download speed: 9.14 MB/s
----------------------------------------------------
Transfer id: tlt_iva_classification_resnet18_v1 Download status: Completed.
Downloaded local path: /workspace/tlt-experiments/pretrained_resnet18/
tlt_resnet18_classification_v1
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
        

Install the Python Virtual Environment

Follow these instructions to install the virtualenv with Python 3.6.9. Once virtualenvwrapper is set up, set the version of python to be used in the virtual env by using the VIRTUALENVWRAPPER_PYTHON variable. You may do so by running the following:

            

            
export VIRTUALENVWRAPPER_PYTHON=/path/to/bin/python3.x
        

where x >= 6 and <= 8.

Use the following command to instantiate a virtual environment:

            

            
mkvirtualenv launcher -p /path/to/bin/python3.x
        

where x >= 6 and <= 8

Download Jupyter Notebook

TLT provides samples notebooks to walk through an prescrible TLT workflow. These samples are hosted on NGC as a resource and can be downloaded from NGC by executing the command mentioned below.

            

            
wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/tlt_cv_samples/versions/v1.1.0/zip -O tlt_cv_samples_v1.1.0.zip
unzip -u tlt_cv_samples_v1.1.0.zip  -d ./tlt_cv_samples_v1.1.0 && rm -rf tlt_cv_samples_v1.1.0.zip && cd ./tlt_cv_samples_v1.1.0
        

The list with their corresponding samples mapped are mentioned below.

Open model architecture:

Start Jupyter Notebook

Once the notebook samples are downloaded, you may start the notebook using the below commands:

            

            
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
        

1. Train the Model

Follow the Notebook instructions to train the model.

You may now deploy these models to DeepStream by following the instructions here.