castai-core-workflow-a

# CAST AI Core Workflow: Autoscaler & Policies ## Overview Primary workflow for CAST AI: configure autoscaler policies to optimize cluster costs. Covers enabling spot instances, configuring the node downscaler and evictor, setting cluster CPU/memory limits, and creating node templates for workload-specific requirements. ## Prerequisites - Completed `castai-install-auth` with Phase 2 (cluster controller + evictor) - `CASTAI_API_KEY` and `CASTAI_CLUSTER_ID` set - Cluster in "ready" status ## Instructions ### Step 1: Read Current Policies ```bash curl -s -H "X-API-Key: ${CASTAI_API_KEY}" \ "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \ | jq . ``` ### Step 2: Enable Cost-Optimized Autoscaling ```bash curl -X PUT -H "X-API-Key: ${CASTAI_API_KEY}" \ -H "Content-Type: application/json" \ "https://api.cast.ai/v1/kubernetes/clusters/${CASTAI_CLUSTER_ID}/policies" \ -d '{ "enabled": true, "unschedulablePods": { "enabled": true, "headroom": { "cpuPercentage": 10, "memoryPercentage": 10, "enabled": true } }, "nodeDownscaler": { "enabled": true, "emptyNodes": { "enabled": true, "delaySeconds": 180 } }, "spotInstances": { "enabled": true, "clouds": ["aws"], "spotDiversityEnabled": true, "spotDiversityPriceIncreaseLimitPercent": 20 }, "clusterLimits": { "enabled": true, "cpu": { "minCores": 4,...