How to save checkpoints with PyTorch
Step-by-step guide: how to save checkpoints using PyTorch. Best practices, code examples, and optimization tips.
This guide shows you how to save checkpoints using PyTorch. We cover setup, configuration, best practices, and optimization. Harch Corp provides GPU cloud infrastructure optimized for PyTorch with H100/H200 GPUs, 400G InfiniBand, and 47 gCO2/kWh carbon intensity.
Prerequisites: Set up your PyTorch environment on Harch Corp GPU cloud. Create an H100 or H200 instance.
Configuration: Configure PyTorch for save checkpoints. Set up GPU memory, batch size, and learning rate.
Execution: Run your save checkpoints 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.