Gateways, regions, and Kubernetes
Regional caching gateways, persistent volumes for ML workloads, and the infrastructure decisions that decide whether your GPUs stay fed.
4 posts
Training Pipes now ships an agent plugin for Cursor, Claude Code, and Codex — five skills that diagnose data-loading problems and an MCP server with 15 tools to create buckets and mount them over NFS, without leaving your editor.
EBS, EFS, FSx, object storage, CSI drivers — Kubernetes gives you many options for ML storage and all the wrong defaults. Here's the pattern that actually works for training workloads.
POSIX semantics on top of object storage is an old and messy problem. Here's what's possible, what's impossible, and what ML teams should actually demand from a storage layer.
A caching gateway colocated with your GPUs is the biggest single lever for training throughput. Here's how the architecture works and why it produces such dramatic speedups.