Home > Servers > PowerEdge Components > White Papers > Developing and Deploying Vision AI with Dell and NVIDIA Metropolis > Step 1: Train and optimize the model with TAO Toolkit
$ sudo su
$ cd /home/
$ pip3 install virtualenv
# To activate the virtualev
$ source .venv/bin/activate
# Install new packages after activating the virtualenv
$ pip3 install virtualenvwrapper
$ export VIRTUALENVWRAPPER_PYTHON=/usr/bin/python/python3.6.9
$ docker login nvcr.io
# Install Tao Toolkit
$ pip3 install nvidia-pyindex
$ pip3 install nvidia-tao
$ tao info
Stdout:
Configuration of the TAO Toolkit Instance
dockers: ['nvidia/tao/tao-toolkit-tf', 'nvidia/tao/tao-toolkit-pyt', 'nvidia/tao/tao-toolkit-lm']
format_version: 1.0
toolkit_version: 3.21.08
published_date: 08/17/2021
$ wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/tao/cv_samples/versions/v1.3.0/zip -O cv_samples_v1.3.0.zip -d /home/TAO_CV_Sample_Jupyter_NoteBooks_v1.3.0
$ Jupyter notebook --ip <YOUR_IP> --port <YOUR_PORT> --allow-root
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://<YOUT_IP>:<YOUR_PORT>/?token=663798ff510efe435606b3f90f95cc44357f702eea05cf2b
Source: https://docs.nvidia.com/tao/tao-toolkit/text/semantic_segmentation/unet.html#int8-mode-overview
Source: https:/docs.nvidia.com/tao/tao-toolkit/text/semantic_segmentation/unet.html#fp16-fp32-model