How do you implement Kubernetes machine learning workflows and GPU resource management?

Kubernetes ML workflows require specialized resource management, job scheduling, and integration with ML frameworks to efficiently run training and inference workloads.\n\nML Workflow Components:\n• Kubeflow: End-to-end ML platform\n• Argo Workflows: DAG-based ML pipelines\n• MLflow: ML lifecycle management\n• Seldon Core: Model serving platform\n• KServe: Serverless ML inference\n\nGPU Resource Management:\n• NVIDIA GPU Operator: GPU lifecycle management\n• Device plugins: GPU resource advertising\n• Resource quotas: GPU allocation limits\n• Node selectors: GPU-enabled node targeting\n• Time-slicing: GPU sharing between workloads\n\nML Job Types:\n• Training jobs: Model development\n• Hyperparameter tuning: Parameter optimization\n• Distributed training: Multi-node/multi-GPU\n• Batch inference: Large-scale prediction\n• Online serving: Real-time inference\n\nExample GPU Job:\napiVersion: batch/v1\nkind: Job\nmetadata:\n name: ml-training\nspec:\n template:\n spec:\n containers:\n - name: trainer\n image: tensorflow/tensorflow:latest-gpu\n resources:\n limits:\n nvidia.com/gpu: 2\n nodeSelector:\n accelerator: nvidia-tesla-v100\n\nBest Practices:\n• Implement resource quotas for GPU usage\n• Use job queues for workload management\n• Monitor GPU utilization and costs\n• Implement model versioning\n• Use distributed training for large models\n• Optimize container images for ML workloads