Your ML Job Was Reclaimed Mid-Training. Here Is What Works
Spot instances are workable for long ML runs when interruption recovery is designed in. See checkpoint and resume patterns for PyTorch and JAX.
SkyPilot blog
SkyPilot engineering notes on multi-cloud GPU orchestration, ML training costs, and spot reliability.
Post archive
Cloud GPU Training Costs Defy Assumptions: 2025 Benchmarks
We measured real costs for identical A100 and H100 fine-tuning jobs across AWS, GCP, and Azure. The gaps were wider than expected.
Spot-Price Routing: Multi-Cloud Orchestration Choices
Cross-cloud training involves more than comparing GPU prices. Credential handling, networking, hardware differences, and migration state all shape how SkyPilot routes jobs.
When to switch GPU pricing tiers in 2025
On-demand, reserved, and spot pricing fit different workloads. Use this framework to match GPU costs with job duration, interruption tolerance, and team size.
72-hour training runs on 32 GPUs face compound failure risk
Multi-node failures follow recognizable patterns. Learn how restart protocols, checkpoint timing, and clear orchestration boundaries reduce lost work during a 72-hour run.
Choosing AWS or GCP for ML Training, with Azure in the Mix
AWS, GCP, and Azure vary in GPU access, spot reliability, networking, and tooling. Compare their strengths by workload before choosing a provider.
Set a Queue Policy for ML Teams: Priorities, Preemption, and Cost
GPU queue rules affect both spending and team trust. Compare FIFO, priority tiers, preemption, budget caps, and fair-share scheduling by team size.
30-Second Spot Interruption Handling for ML Engineers
Spot instances provide 30 seconds to 2 minutes of warning before reclamation. Here is how to use that time to preserve training state.
Not a GPU primer: five ML cost strategies
GPU costs grow with experiments and model size, but waste adds to the bill. These five strategies target separate sources of GPU waste and combine to reduce training costs.
sky launch: Multi-Cloud ML Training Setup
Your first cross-cloud training run mainly requires credential and network setup. This guide covers the full sequence and the common points of failure.
45% GPU Utilization: The Monitoring Metric That Misleads Teams
GPU utilization below 80% often points to slow data loading, multi-node communication, or unused reserved capacity. The right remedy depends on the cause.