tao-run-automl-deft-pipeline

Automating the end-to-end NVIDIA TAO training pipeline requires executing a three-phase workflow: AutoML baseline for hyperparameter optimization, a DEFT loop for iterative data improvement, and AutoML refinement on the augmented dataset. This pipeline automates these phases sequentially for DEFT applications like AOI inspection.

The DEFT loop is an iterative data-improvement process applied after an initial AutoML baseline. It enhances the dataset quality before running final AutoML refinement, ensuring the NVIDIA TAO model is trained on optimized data for tasks such as AOI inspection.

Yes, Docker and nvidia-container-toolkit are required dependencies for execution. You must install and configure these container tools in your environment to run the end-to-end training pipeline for NVIDIA TAO models successfully.

Yes, the automated pipeline is ideal for AOI inspection and other DEFT applications. It applies the AutoML baseline, DEFT loop, and AutoML refinement phases directly to end-to-end training workflows for NVIDIA TAO models.

Running hyperparameter optimization (HPO) for NVIDIA TAO models is best handled by the AutoML baseline phase. It initializes the end-to-end pipeline by optimizing model parameters on the initial dataset before proceeding to the DEFT data-improvement loop.