AI / Infrastructure
Selected lab
GPU Orchestration Lab
Dockerized multi-stage GPU pipeline with Prefect and on-demand worker pools.
Stack
Docker, Prefect, RunPod, PostgreSQL, S3-compatible storage, FAISS, vLLM, FastAPI workers, Stable Diffusion, NLLB, CUDA
Proof angle
Shows orchestration separated from GPU-heavy stages so each workload can run in the right worker image and hardware profile.
Overview
A GPU orchestration lab using Prefect, RunPod, Docker worker images, PostgreSQL, S3-compatible storage, FAISS, vLLM, FastAPI workers, Stable Diffusion, NLLB, and autoscaling logic.
Where this is relevant
Relevant for GPU orchestration, model serving, embedding search, tagging, image generation, translation, object storage, and autoscaled AI worker pools.
What it shows
- small controller with specialized GPU-heavy worker images
- separate pools for embedding search, model serving, tagging, image generation, translation, and asset generation
- RunPod worker autoscaling and object-storage handoff
- clear separation between orchestration/state and workload-specific hardware