TAO Toolkit Triton Apps 測試紀錄

參考 TAO Toolkit Triton Apps

$ pip3 install virtualenv virtualenvwrapper

$ mkdir $HOME/.virtualenvs

$ vi ~/.bashrc

export WORKON_HOME=$HOME/.virtualenvs

export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python3

source /home/user/.local/bin/virtualenvwrapper.sh

$ source ~/.bashrc

$ mkvirtualenv tao -p /usr/bin/python3

$ deactivate

$ workon tao

$ git clone https://github.com/NVIDIA-AI-IOT/tao-toolkit-triton-apps.git

$ cd tao-toolkit-triton-apps/tao_triton

$ pip3 install -r requirements-pip.txt 

$ pip3 install nvidia-pyindex

$ pip3 install tritonclient[all]

$ add2virtualenv=`pwd`

$ cd ..

$ bash scripts/start_server.sh 若產生下列錯誤

...

Get:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64  InRelease [1581 B]

Err:1 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64  InRelease

  The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC

Get:2 http://archive.ubuntu.com/ubuntu focal InRelease [265 kB]

Get:3 http://security.ubuntu.com/ubuntu focal-security InRelease [114 kB]

...

$ vi docker/Dockerfile

在

# Download and install TAO Toolkit converter

前，加上

RUN apt-key del 7fa2af80

RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub

RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/7fa2af80.pub

$ bash scripts/start_server.sh 

new session, 準備圖檔於 images 目錄

$ mkdir images images_out

$ cd images

$ wget https://github.com/openalpr/benchmarks/raw/master/seg_and_ocr/usimages/ca286.png

$ mv ca286.png 5VCF203.png

$ wget https://developer.nvidia.com/sites/default/files/akamai/NGC_Images/models/peoplenet/input_11ft45deg_000070.jpg

$ mv input_11ft45deg_000070.jpg peopleseg.jpg

$ cd ../tao-toolkit-triton-apps

$ export PYTHONPATH=`pwd`:$PYTHONPATH

$ pip install scikit-image

$ python tao_triton/python/entrypoints/tao_client.py ../images -m peoplenet_tao -x 1 -b 8 --mode DetectNet_v2 -i https -u localhost:8000 --async --output_path ../images_out --postprocessing_config tao_triton/python/clustering_specs/clustering_config_peoplenet.prototxt 

$ python tao_triton/python/entrypoints/tao_client.py ../images -m dashcamnet_tao -x 1 -b 8 --mode DetectNet_v2 -i https -u localhost:8000 --async --output_path ../images_out --postprocessing_config tao_triton/python/clustering_specs/clustering_config_dashcamnet.prototxt --class_list car,bicycle,person,road_sign

$ python tao_triton/python/entrypoints/tao_client.py ../images -m vehicletypenet_tao -x 1 -b 1 --mode Classification -i https -u localhost:8000 --async --output_path ../images_out

$ python tao_triton/python/entrypoints/tao_client.py ../images -m lprnet_tao -x 1 -b 1 --mode LPRNet -i https -u localhost:8000 --async --output_path ../images_out

$ python tao_triton/python/entrypoints/tao_client.py ../images -m yolov3_tao -x 1 -b 1 --mode YOLOv3 -i https -u localhost:8000 --async --output_path ../images_out

$ python tao_triton/python/entrypoints/tao_client.py ../images -m peoplesegnet_tao -x 1 -b 1 --mode Peoplesegnet -i https -u localhost:8000 --async --output_path ../images_out