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Building CV Service Using NVIDIA NGC and Triton in Google Cloud

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Description

Building computer vision service using NVIDIA NGC to train, optimize and deploy with Triton Inference Server application in GCP Marketplace.

Publisher

NVIDIA

Use Case

Other

Framework

Other

Latest Version

3.0

Modified

August 25, 2021

Compressed Size

2.01 MB

Building a Computer Vision Service Using NVIDIA NGC and Triton in Google Cloud

The following instruction is used in the NVIDIA GCP Lab Link.

Prerequisite

NVIDIA NGC Base VM Images is available in Google Cloud Marketplace, in the following example, we use GCP's latest A100 GPU in A2 family to launch a NGC VM. NGC VM preinstalls NVIDIA Docker.

TAO(TLT) Stream Analysytics container provides the runtime dependencies for steps below. It can be launched with

docker run --gpus all -it \
 --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 -p8888:8888 \
 -v "$(pwd)":/tlt nvcr.io/nvidia/tlt-streamanalytics:v3.0-py3

Download RGB Pretrained PeopleNet from NGC and Deploy to Triton

mkdir /tlt/models
ngc registry model download-version "nvidia/tlt_peoplenet:unpruned_v2.1"

First, we convert the pretrained models weights to a etlt format, then optimized to a TensorRT Engine.

detectnet_v2 export -k "tlt_encode" -m /tlt/models/tlt_peoplenet_vunpruned_v2.1/resnet34_peoplenet.tlt -o /tlt/models/tlt_peoplenet_vunpruned_v2.1/resnet34_peoplenet.etlt
tlt-converter -k "tlt_encode" -d 3,544,960 -e /tlt/models/tlt_peoplenet_vunpruned_v2.1/model.engine -o output_cov/Sigmoid,output_bbox/BiasAdd /tlt/models/tlt_peoplenet_vunpruned_v2.1/resnet34_peoplenet.etlt

Second, we move the pretrained engine to a GCS directory.

gsutil cp ~/tlt/models/tlt_peoplenet_vunpruned_v2.1/model.engine gs://dongm-tlt/tlt/triton-model/peoplenet_tlt/1/model.plan

Here is a sample Triton configuration file for the RGB PeopleNet model, we also drop it to the same bucket.

wget https://raw.githubusercontent.com/NVIDIA-AI-IOT/tlt-triton-apps/main/model_repository/peoplenet_tlt/config.pbtxt
gsutil cp config.pbtxt gs://dongm-tlt/tlt/triton-model/peoplenet_tlt/

The GKE A100 cluster could be created with command below:

export PROJECT_ID=k80-exploration
export ZONE=us-central1-a
export REGION=us-central1
export DEPLOYMENT_NAME=dongm-cv-gke

gcloud beta container clusters create ${DEPLOYMENT_NAME} \
--addons=HorizontalPodAutoscaling,HttpLoadBalancing,Istio \
--machine-type=n1-standard-8 \
--node-locations=${ZONE} \
--zone=${ZONE} \
--subnetwork=default \
--scopes cloud-platform \
--num-nodes 1 \
--project ${PROJECT_ID}

# add GPU node pools, user can modify number of node based on workloads
gcloud container node-pools create accel \
 --project ${PROJECT_ID} \
 --zone ${ZONE} \
 --cluster ${DEPLOYMENT_NAME} \
 --num-nodes 1 \
 --accelerator type=nvidia-tesla-a100,count=1 \
 --enable-autoscaling --min-nodes 1 --max-nodes 2 \
 --machine-type a2-highgpu-1g \
 --disk-size=100 \
 --scopes cloud-platform \
 --verbosity error

# so that you can run kubectl locally to the cluster
gcloud container clusters get-credentials ${DEPLOYMENT_NAME} --project ${PROJECT_ID} --zone ${ZONE} 

# deploy NVIDIA device plugin for GKE to prepare GPU nodes for driver install
kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/container-engine-accelerators/master/nvidia-driver-installer/cos/daemonset-preloaded.yaml

# make sure you can run kubectl locally to access the cluster
kubectl create clusterrolebinding cluster-admin-binding --clusterrole cluster-admin --user "$(gcloud config get-value account)"

# enable stackdriver custom metrics adaptor
kubectl apply -f https://raw.githubusercontent.com/GoogleCloudPlatform/k8s-stackdriver/master/custom-metrics-stackdriver-adapter/deploy/production/adapter.yaml

Last, we use GKE Triton Marketplace Application to launch a Triton deployment in GKE, pointing model repository to gs://dongm-tlt/tlt/triton-model. Given the compatibilty required by TensorRT, please note we will launch a A100 GKE cluster to host the application, and in the meanwhile, use Triton v2.5.0 version of GKE application to be compatible with TAO 3.0.

Inference Experiment with RGB Images

Once the application has been deployed, we will leverage TAO Triton Application to send inference request. Follow getting started guide to set up python environment, but skip start server part as server has been deployed to GKE. Find out Istio ingress IP with: export INGRESS_HOST=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}') , for example: 34.141.169.102.

In triton_dev environment, install jupyter and ipython.

Launch a jupyter notebook from the GCP VM `` And set up a port forwarding to your client gcloud compute ssh --project k80-exploration --zone us-central1-a dongm-nvidia-ngc-base-vm -- -L 8888:localhost:8888

And run the client python script in the notebook tao_triton_client.ipynb or with command below:

python3 ${TLT_TRITON_REPO_ROOT}/tlt_triton/python/entrypoints/tlt_client.py ../inference_images/rgb \
 -m peoplenet_tlt \
 -x 1 -b 16 --mode DetectNet_v2 \
 -i https -u 34.134.191.228:80 --async \
 --output_path ../inference_images_out \
 --postprocessing_config ../inference_config/clustering_config_peoplenet_rgb.prototxt 

Prepare FLIR Thermal Dataset(IR Images)

Follow notebook ir2kitti_preprocess.ipynb to convert data label to kitti format, preprocess the images, generate tf records for Training.

Retrain PeopleNet with FLIR Thermal Dataset(IR Images)

We modify the TAO Training configuration (training_config/training_spec.txt in the zip file) to point to the TF records location and kick off a retraining.

detectnet_v2 train -e training_config/training_spec.txt -r experiments -n "final_model" -k "tlt_encode" --gpus 8

In the example configuration, we train for 60 epochs and the accuracy could reach 72% by the end of training.

Deploy IR PeopleNet to Triton

First, we convert the retrained models weights to a etlt format, then optimized to a TensorRT Engine.

detectnet_v2 export -m /tlt/experiments/weights/final_model.tlt -o /tlt/models/ir.etlt -k "tlt_encode"
tlt-converter -k "tlt_encode" -d 3,544,960 -e /tlt/models/ir.engine -o output_cov/Sigmoid,output_bbox/BiasAdd /tlt/models/ir.etlt 

Second, we move the retrained engine to a GCS directory.

gsutil cp /tlt/models/ir.engine gs://dongm-tlt/tlt/triton-model/peoplenet_ir_tlt/1/model.plan

We also modify the config.pbtxt file to fit IR model as below,

name: "peoplenet_ir_tlt"
platform: "tensorrt_plan"
max_batch_size: 16
input [
 {
 name: "input_1"
 data_type: TYPE_FP32
 format: FORMAT_NCHW
 dims: [ 3, 544, 960 ]
 }
]
output [
 {
 name: "output_bbox/BiasAdd"
 data_type: TYPE_FP32
 dims: [ 4, 34, 60 ]
 },
 {
 name: "output_cov/Sigmoid"
 data_type: TYPE_FP32
 dims: [ 1, 34, 60 ]
 }
]
dynamic_batching { }

then copy:

gsutil cp config.pbtxt gs://dongm-tlt/tlt/triton-model/peoplenet_tlt/

Last, we use GKE Triton Marketplace Application to launch a Triton deployment in GKE, pointing model repository to gs://dongm-tlt/tlt/triton-model. Given the compatibilty required by TensorRT, please note we will launch a A100 GKE cluster to host the application, and in the meanwhile, use Triton v2.5.0 version of GKE application to be compatible with TAO 3.0.

Inference Experiment with IR Images

And run the client python script in the notebook tao_triton_client.ipynb or with command below

python3 ${TLT_TRITON_REPO_ROOT}/tlt_triton/python/entrypoints/tlt_client.py directory_to_test_images \
 -m peoplenet_tlt \
 -x 1 -b 16 --mode DetectNet_v2 \
 --class_list person \
 -i https -u 34.141.169.102:80 --async \
 --output_path directory_to_test_images_output \
 --postprocessing_config inference_config/clustering_config_peoplenet_ir.prototxt