skypilot-multi-cloud-orchestration
Category: ml-inference-and-ops · License: MIT · Version: 1.0.0
Tags: Infrastructure, Multi-Cloud, Orchestration, GPU, Cost Optimization, SkyPilot
When to use it
Multi-cloud orchestration for ML workloads with automatic cost optimization. Use when you need to run training or batch jobs across multiple clouds, leverage spot instances with auto-recovery, or optimize GPU costs across providers.Example requests
Ask your agent in plain language, for example:- “How do I get my training job to run on whichever cloud has the cheapest A100s right now?”
- “My spot instance on GCP keeps getting preempted halfway through fine-tuning llama-3-8b with train.py, and I lose everything each time. Checkpoints are written to /outputs. How do I make this resume automatically without babysitting it?”
- “I have a 4-node, 8xH100 pretraining run and a grant that lets me use credits on AWS, Azure and Lambda. I’d rather not rewrite my launch scripts for each provider. What’s a sane way to set this up so the same job spec works everywhere and fails over if one is out of capacity?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole ml-inference-and-ops category with /plugin install ml-inference-and-ops@research-agent-skills.