skypilot-multi-cloud-orchestration

Orchestrate machine learning workloads across AWS, GCP, Azure, and Kubernetes.

11.5k|842|Updated Nov 3, 2025

Search Tags:#orchestration #infrastructure #cost optimization #gpu #multi-cloud #skypilot

Ready to run

npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill skypilot-multi-cloud-orchestration

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skypilot-multi-cloud-orchestration
Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/main/09-infrastructure/skypilot
Command: npx skills add https://github.com/Orchestra-Research/AI-Research-SKILLs --skill skypilot-multi-cloud-orchestration

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill simplifies and optimizes the execution of machine learning workloads across multiple cloud providers, significantly reducing costs and complexity.

Core Features & Use Cases

Multi-Cloud Orchestration: Seamlessly run jobs on AWS, GCP, Azure, and more.
Cost Optimization: Automatically selects the cheapest cloud and instance types, leveraging spot instances for up to 6x savings.
Distributed Training: Manages multi-node training setups with ease.
Model Serving: Deploy and scale ML models with Sky Serve.
Use Case: You need to train a large language model that requires 32 A100 GPUs. Use this Skill to find the most cost-effective way to provision these resources across AWS and GCP, ensuring your training job runs reliably even if spot instances are preempted.

Quick Start

Install SkyPilot and check your cloud credentials by running pip install skypilot[aws,gcp,azure,kubernetes] followed by sky check.

Frequently Asked Questions about skypilot-multi-cloud-orchestration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run machine learning workloads across multiple cloud providers to reduce costs?▼

Multi-cloud orchestration runs machine learning workloads across AWS, GCP, Azure, and Kubernetes, automatically selecting the cheapest cloud and instance types. It leverages spot instances to reduce infrastructure costs by up to 6x while ensuring reliable execution.

What is the best way to provision 32 A100 GPUs for distributed training when capacity is limited?▼

Provisioning 32 A100 GPUs for distributed training is handled by finding the most cost-effective resources across AWS and GCP. It manages multi-node setups and ensures training jobs run reliably even if provisioned spot instances are preempted.

Does multi-cloud orchestration support deploying and scaling ML models?▼

Multi-cloud orchestration supports deploying and scaling ML models through Sky Serve. It provides flexible resource provisioning across AWS, GCP, Azure, and Kubernetes to manage both distributed training and model serving workloads efficiently.

Can I use spot instances for distributed training without losing my progress?▼

Spot instances are supported for distributed training with automatic cloud selection to handle preemptions. This approach optimizes for cost and performance, ensuring your machine learning training jobs run reliably across multiple cloud providers even if instances are interrupted.

What do I need to set up before orchestrating ML jobs across AWS, GCP, and Azure?▼

Before orchestrating ML jobs, you need to install SkyPilot and verify your cloud credentials. Run `pip install skypilot[aws,gcp,azure,kubernetes]` followed by the `sky check` command to ensure your environment is ready for multi-cloud workload execution.