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» Requirements and Installa on

Requirements and Installation

This page describes the requirements and installa on steps for Transfer Learning Toolkit (TLT).

Hardware Requirements

TLT has the following hardware requirements:

Recommended

32 GB system RAM

32 GB of GPU RAM

8 core CPU

1 NVIDIA GPU

100 GB of SSD space

TLT is supported on A10/A40/A100, V100 and RTX 30x0 GPUs.

Software Requirements

TLT has the following so ware requirements:

Ubuntu 18.04 LTS

NVIDIA GPU Cloud

account and API key

docker-ce

nvidia docker

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Perform the following prerequisite steps before installing TLT:

1. Install Docker.

2. Install NVIDIA GPU driver v455.xx or above.

3. Install nvidia docker

4. Get an

NGC

account and API key:

a. 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”.

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

c. Choose your organiza on when prompted for Organiza on/Team.

d. Click Sign In.

4. Execute

docker login nvcr.io

from the command line and enter these login creden als:

a. Username: “$oauthtoken”

b. Password: “YOUR_NGC_API_KEY”

Getting Started With TLT

1. Prerequisites

Install virtualenv with python 3.6.9:

To setup the python virtual environment, please follow these

instruc ons

. Once

virtualenvwrapper is setup, please set the version of python to be used in the virtual env by

using the

VIRTUALENVWRAPPER_PYTHON

variable. You may do so by running

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

where x >= 6 and <= 8

Instan ate a virtual environment using the below

(3)

where x >= 6 and <= 8

2. 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 execu ng the command

men oned below.

wget --content-disposition

https://api.ngc.nvidia.com/v2/resources/nvidia/tlt_cv_samples/versions/v1.0/zip -O tlt_cv_samples_v1.0.zip

The list with their corresponding samples mapped are men oned below.

Model Name

Jupyter Notebook

VehicleTypeNet

classifica on

VehicleMakeNet

classifica on

TrafficCamNet

detectnet_v2

PeopleSegNet

mask_rcnn

PeopleNet

detectnet

License Plate Recogni on

lprnet

License Plate Detec on

detectnet_v2

Heart Rate Es ma on

heartratenet

Gesture Recogni on

gesturenet

Gaze Es ma on

gazenet

Facial Landmark

fpenet

FaceDetectIR

detectnet_v2

FaceDetect

facenet

(4)

DashCamNet

dashcamnet

Open model architecture:

Open model architecture

Jupyter notebook

DetectNet_v2

detectnet_v2

FasterRCNN

faster_rcnn

YOLOV3

yolo_v3

YOLOV4

yolo_v4

SSD

ssd

DSSD

dssd

Re naNet

re nanet

MaskRCNN

mask_rcnn

UNET

unet

Classifica on

classifica on

3. Start Jupyter Notebook

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

commands:

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

Open the internet browser on localhost and type the url wri en below:

http://0.0.0.0:8888

(5)

If you want to run the notebook from a remote server then please follow these

steps

.

4. Train the Model

Follow the Notebook instruc ons to train the model. Once the model is trained, export the .tlt

model to .etlt using below command:

tlt model_name export \

-m $USER_EXPERIMENT_DIR/experiment_dir_pruned/trained_model.tlt \

-o $USER_EXPERIMENT_DIR/experiment_dir_final/trained_model.etlt \

-k $KEY

Deepstream will use the exported .etlt model for inference.

Deepstream - TLT Integration

This sec on will describe how to integrate TLT models with Deepstream.

Prerequisites

Install

Deepstream

.

Download and install DeepStream SDK or use

DeepStream docker image

. Follow the

instruc ons men oned in the

quick start guide

.

Application files

To run the TLT model with Deepstream, below are the list of files required for each applica on.

Deepstream Pipeline: deepstream_applica on.c

Custom parser func on for post-processing :

nvdsinfer_customparser_$(MODEL)_tlt

TLT Models

(6)

1. Deepstream Pipeline:

Create a deepstream pipeline in deepstream_applica on.c. Sample applica ons can be found at

NVIDIA-AI-IOT tlt applica on

and

NVIDIA-AI-IOT lpr-lpd applica on

.

2. Create Custom Parser:

Create a nvdsinfer_customparser_$(MODEL)_tlt for post processing. Sample parsers can be

found at

NVIDIA-AI-IOT

3. TLT Models:

Use the .etlt model which was generated with TLT export.

 Note

For Unet model and LPR model, you must convert the etlt model to TensorRT engine file

using tlt-convert like following:

tlt-converter -k nvidia_tlt -p image_input,1x3x48x96,4x3x48x96,16x3x48x96 ./us_lprnet_baseline18_deployable.etlt -t fp16 -e lpr_us_onnx_b16.engine tlt-converter -e models/unet/unet_resnet18.etlt_b1_gpu0_fp16.engine -p

input_1,1x3x608x960,1x3x608x960,1x3x608x960 -t fp16 -k tlt_encode -m 1 tlt_encode models/unet/unet_resnet18.etlt

4. Model configuration files:

The DeepStream configura on file includes some run me parameters for DeepStream nvinfer

plugin, such as model path, label file path, TensorRT inference precision, input and output node

names, input dimensions and so on. In this sample, each model has its own DeepStream

configura on file, e.g. pgie_dssd_tlt_config.txt for DSSD model. Please refer to DeepStream

Development Guide for detailed explana ons of those parameters.

(7)

Build and Run the application:

Build Parser

cd /opt/nvidia/deepstream/deepstream-5.0/sources/application/nvdsinfer_customparser_$(MODEL)_tlt make cp nvdsinfer_customparser_$(MODEL)_tlt.so /opt/nvidia/deepstream/deepstream-5.0/lib

Build Applica on

cd /opt/nvidia/deepstream/deepstream-5.0/sources/apps/application make

Run the Applica on

./application_name --args

Resources

Deepstream-TLT PeopleNet model Deployment

(8)

Deepstream-TLT TrafficCamNet model Deployment

TrafficCamNet

Deepstream-TLT DashCamNet model deployment

## Download Model:

mkdir -p $HOME/peoplenet && \

wget https://api.ngc.nvidia.com/v2/models/nvidia/tlt_peoplenet/versions/pruned_v1.0/files/resnet34_peoplen \ -O $HOME/peoplenet/resnet34_peoplenet_pruned.etlt ## Run Application xhost +

docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v

$HOME:/opt/nvidia/deepstream/deepstream-5.0/samples/models/tlt_pretrained_models \

-w /opt/nvidia/deepstream/deepstream-5.0/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.0.1-20.09-samples \

deepstream-app -c deepstream_app_source1_peoplenet.txt

## Download Model:

mkdir -p $HOME/trafficcamnet && \

wget

https://api.ngc.nvidia.com/v2/models/nvidia/tlt_trafficcamnet/versions/pruned_v1.0/files/resnet18_tra

\

-O $HOME/trafficcamnet/resnet18_trafficcamnet_pruned.etlt && \

wget https://api.ngc.nvidia.com/v2/models/nvidia/tlt_trafficcamnet/versions/pruned_v1.0/files/trafficnet_i \ -O $HOME/trafficcamnet/trafficnet_int8.txt ## Run Application xhost +

docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v

$HOME:/opt/nvidia/deepstream/deepstream-5.0/samples/models/tlt_pretrained_models \

-w /opt/nvidia/deepstream/deepstream-5.0/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.0.1-20.09-samples \

(9)

DashCamNet

Deepstream-TLT FaceDetectIR model deployment

FaceDetectIR

## Download Model:

mkdir -p $HOME/dashcamnet && \

wget

https://api.ngc.nvidia.com/v2/models/nvidia/tlt_dashcamnet/versions/pruned_v1.0/files/resnet18_dashca

\

-O $HOME/dashcamnet/resnet18_dashcamnet_pruned.etlt && \

wget

https://api.ngc.nvidia.com/v2/models/nvidia/tlt_dashcamnet/versions/pruned_v1.0/files/dashcamnet_int8

\

-O $HOME/dashcamnet/dashcamnet_int8.txt mkdir -p $HOME/vehiclemakenet && \

wget

https://api.ngc.nvidia.com/v2/models/nvidia/tlt_vehiclemakenet/versions/pruned_v1.0/files/resnet18_ve

\

-O $HOME/vehiclemakenet/resnet18_vehiclemakenet_pruned.etlt && \

wget

https://api.ngc.nvidia.com/v2/models/nvidia/tlt_vehiclemakenet/versions/pruned_v1.0/files/vehiclemake

\

-O $HOME/vehiclemakenet/vehiclemakenet_int8.txt mkdir -p $HOME/vehicletypenet && \

wget

https://api.ngc.nvidia.com/v2/models/nvidia/tlt_vehicletypenet/versions/pruned_v1.0/files/resnet18_ve

\

-O $HOME/vehicletypenet/resnet18_vehicletypenet_pruned.etlt && \

wget https://api.ngc.nvidia.com/v2/models/nvidia/tlt_vehicletypenet/versions/pruned_v1.0/files/vehicletype \ -O $HOME/vehicletypenet/vehicletypenet_int8.txt ## Run Application xhost +

docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v

$HOME:/opt/nvidia/deepstream/deepstream-5.0/samples/models/tlt_pretrained_models \

-w /opt/nvidia/deepstream/deepstream-5.0/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.0.1-20.09-samples \

(10)

Deepstream-TLT apps

.

## Download Model

mkdir -p $HOME/facedetectir && \

wget

https://api.ngc.nvidia.com/v2/models/nvidia/tlt_facedetectir/versions/pruned_v1.0/files/resnet18_face

\

-O $HOME/facedetectir/resnet18_facedetectir_pruned.etlt && \

wget https://api.ngc.nvidia.com/v2/models/nvidia/tlt_facedetectir/versions/pruned_v1.0/files/facedetectir_ \ -O $HOME/facedetectir/facedetectir_int8.txt ## Run Application xhost +

docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v

$HOME:/opt/nvidia/deepstream/deepstream-5.0/samples/models/tlt_pretrained_models \

-w /opt/nvidia/deepstream/deepstream-5.0/samples/configs/tlt_pretrained_models nvcr.io/nvidia/deepstream:5.0.1-20.09-samples \

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