tao-train-single-step

You can fine-tune, evaluate, and export a TAO model in a single workflow by automating the process for pretrained models, streamlining iterative training and evaluation without needing AutoML or DEFT loops.

Yes, TAO model training requires Docker and NVIDIA-container-toolkit for execution. These dependencies are necessary to run the containerized environment for the single-step training workflow.

Single-step training and export supports various TAO models, including clip, nvdinov2, and grounding_dino. It is designed to be versatile for fine-tuning different pretrained model architectures on new datasets.

The best way to iteratively train and evaluate TAO models without AutoML is using a single-step workflow that automates fine-tuning and performance evaluation on a new dataset, culminating in model export for deployment.

No, this single-step TAO training workflow is specifically designed for iterative training and evaluation without AutoML or DEFT loops. It simplifies the process by performing training, evaluation, and export continuously.