Package: nvidia-tlt
TAO Toolkit Quick Start GuideThis page provides a quick start guide for installing and running TAO Toolkit.RequirementsHardwareThe following system configuration is recommended to achieve reasonable training performance with TAO Toolkit and supported models provided:32 GB system RAM32 GB of GPU RAM8 core CPU1 NVIDIA GPU100 GB of SSD spaceTAO Toolkit is supported on A100, V100 and RTX 30x0 GPUs.Software RequirementsSoftwareVersionUbuntu 18.04 LTS18.04python>=3.6.9docker-ce>19.03.5docker-API1.40nvidia-container-toolkit>1.3.0-1nvidia-container-runtime3.4.0-1nvidia-docker22.5.0-1nvidia-driver>465python-pip>21.06python-devInstalling the Pre-requisitesThe tao-launcher is strictly a python3 only package, capable of running on python 3.6.9 or 3.7.Installdocker-ceby following theofficial instructions.Once you have installed docker-ce, follow thepost-installation stepsto ensure that the
docker can be run withoutsudo.Installnvidia-container-toolkitby following theinstall-guide.Get anNGCaccount and API key:a. Go to NGC and click theTAO Toolkitcontainer in theCatalogtab. This
message is displayed:Sign in to access the PULL feature of this repository.
b. Enter your Email address and clickNext, or clickCreate an Account.
c. Choose your organization when prompted forOrganization/Team.
d. ClickSign In.Log in to the NGC docker registry (nvcr.io) using the commanddocker login nvcr.ioand enter the following credentials:a.Username:$oauthtokenb.Password:YOUR_NGC_API_KEYwhereYOUR_NGC_API_KEYcorresponds to the key you generated from step 3.DeepStream 6.0 -NVIDIA SDK for IVA inferenceis recommended.Installing TAO ToolkitTAO Toolkit 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 theInstallation Prerequisitessection
for details on creating an NGC account and obtaining an API key.Create a newvirtualenvusingvirtualenvwrapper.You may follow the instructions in thislinkto set up a Python virtualenv using a virtualenvwrapper.Once you have followed the instructions to installvirtualenvandvirtualenvwrapper,
set the Python version in thevirtualenv. 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.bashrcor.bash_profilefor setting
your Pythonvirtualenvby default.exportVIRTUALENVWRAPPER_PYTHON=/usr/bin/python3Setting the path to the python3 binary when creating yourvirtualenvusing thevirtualenvwrapperwrappermkvirtualenvlauncher-p/path/to/your/python3Once you have logged into thevirtualenv, the command prompt should show the name of
your virtual environment(launcher)py-3.6.9desktop:When you are done with you session, you may deactivate yourvirtualenvusing thedeactivatecommand:deactivateYou may re-instantiate this createdvirtualenvenv using theworkoncommand.workonlauncherInstall the TAO Launcher Python package callednvidia-tlt.pip3installnvidia-tltIf you had installed an older version ofnvidia-tltlauncher, you may upgrade
to the latest version by running the following command.pip3install--upgradenvidia-tltInvoke the entrypoints using thetltcommand.tlt--helpThe sample output of the above command is: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,converter,token_classification,unet,yolo_v3,yolo_v4,yolo_v4_tiny}...

LauncherforTAO

optionalarguments:
-h,--helpshowthishelpmessageandexittasks:{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,converter,token_classification,unet,yolo_v3,yolo_v4,yolo_v4_tiny}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 TAO Launcher:liststopinfoWhen installing the TAO Toolkit Launcher to your host machine's native python3 as opposed to the recommended route of using virtual
environment, you may get an error saying thattltbinary wasn't found. This is because the path to yourtltbinary
installed by pip wasn't added to thePATHenvironment variable in your local machine. In this case, please run the
following command:exportPATH=$PATH:~/.local/binRunning the TAO ToolkitInformation about the TAO Launcher CLI and details on using it to run TAO supported tasks are
captured in theTAO Toolkit Launchersection of theTAO Toolkit User Guide.Use the examplesExample Jupyter notebooks for all the tasks that are supported in TAO Toolkit are available in NGCresources. TAO Toolkit provides
sample workflows forComputer VisionandConversational AI.Computer VisionAll the samples for the supported computer vision tasks are hosted on ngc under theTAO Computer Vision Samples.
To run the available examples, download this sample resource by using the following commands.wget--content-dispositionhttps://api.ngc.nvidia.com/v2/resources/nvidia/tao/cv_samples/versions/v1.3.0/zip-Ocv_samples_v1.3.0.zip
unzip-ucv_samples_v1.3.0.zip-d./cv_samples_v1.3.0&&rm-rfcv_samples_v1.3.0.zip&&cd./cv_samples_v1.3.0Conversational AIThe TAO Conversational AI package, provides several end to end sample workflows to train conversational AI models
using TAO Toolkit and subsequently deploying them to Riva. You can find these samples at:Conversational AI TaskJupyter NotebooksSpeech to TextSpeech to Text NotebookSpeech to Text CitrinetSpeech to Text Citrinet NotebookQuestion AnsweringQuestion Answering NotebookText ClassificationText Classification NotebookToken ClassificationToken Classification NotebookPunctuation and CapitalizationPunctuation Capitalization NotebookIntent and Slot ClassificationIntent Slot Classification NotebookNGram Language ModelNGram Language Model NotebookText to SpeechText to Speech NotebookYou 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.pip3installjupyter
jupyternotebook--ip0.0.0.0--allow-root--port8888Copy and paste the link produced from this command into your browser to access the notebook. The/workspace/examplesfolder will contain a demo notebook.
Feel free to use any free port available to host the notebook if port 8888 is unavailable.