tao-run-on-kubernetes

To submit TAO container jobs to Kubernetes with GPU scheduling, you can automate the process as Kubernetes Jobs. This supports single-node and multi-node distributed training on clusters with NVIDIA GPUs.

Yes, running TAO training jobs on Kubernetes requires the NVIDIA GPU Operator or device plugin. You also need the CUDA toolkit and specific SDK configurations to enable GPU-based computing.

Yes, you can run multi-node distributed training using TAO containers on a Kubernetes cluster. The automation supports both single-node and multi-node setups for scalable ML workloads.

Automating TAO job submission on Kubernetes is best handled by configuring the jobs with NVIDIA GPU scheduling. This approach streamlines container deployment for training and inference tasks.

TAO container jobs may fail to schedule GPUs if the Kubernetes cluster lacks the NVIDIA GPU Operator or device plugin. Ensure CUDA and the GPU operator are correctly configured before submission.