download ngc
wget --content-disposition https://api.ngc.nvidia.com/v2/resources/nvidia/ngc-apps/ngc_cli/versions/3.41.4/files/ngccli_linux.zip -O ngccli_linux.zip && unzip ngccli_linux.zip
give permission
chmod u+x ngc-cli/ngc
run
ngc config set
then enter your credential
tutorial video: https://resources.nvidia.com/en-us-riva-tutorials-briefcase/riva-quick-start-guide geting NVIDIA api key:https://org.ngc.nvidia.com/setup/api-key
ngc registry resource download-version nvidia/riva/riva_quickstart:2.15.0
cd riva_quickstart_v2.15.0
bash riva_init.sh
bash riva_start.sh
after you have done using the container, don't forget to run
bash riva_stop.sh
notes that in the code we used port 8889, you might have to edit that in either riva_quickstart_v2.15.0/config.sh or the code. We already provided the config.sh file in this repository so you can just copy and paste the file.
basically clone the repository.
git clone https://github.com/NVAITC-APS/FoodRAG/tree/dev_POC
getting requirements
fresh_install_requirements.txt
start the application
streamlit run main.py
we also archived the code used for training, finetuning, inferecing and the speech emotion recognition models for this project here