How to fine-tune model with Kubernetes
Step-by-step guide: how to fine-tune model using Kubernetes. Best practices, code examples, and optimization tips.
This guide shows you how to fine-tune model using Kubernetes. We cover setup, configuration, best practices, and optimization. Harch Corp provides GPU cloud infrastructure optimized for Kubernetes with H100/H200 GPUs, 400G InfiniBand, and 47 gCO2/kWh carbon intensity.
Prerequisites: Set up your Kubernetes environment on Harch Corp GPU cloud. Create an H100 or H200 instance.
Configuration: Configure Kubernetes for fine-tune model. Set up GPU memory, batch size, and learning rate.
Execution: Run your fine-tune model workload. Monitor GPU utilization and training metrics.
Optimization: Optimize for performance and cost. Use mixed precision, gradient checkpointing, and spot instances.
Monitoring: Set up monitoring with Prometheus and Grafana. Track GPU utilization, memory, and throughput.
Scaling: Scale to multiple GPUs with distributed training. Use NCCL and InfiniBand for communication.
Deployment: Deploy your model to production. Use vLLM or TensorRT-LLM for inference.
Cost optimization: Use spot instances, reserved capacity, and auto-scaling to minimize costs.