Use Cases

Examples By Category

Distributed Python examples for queues, pools, pipelines, and shared state

Distributed data sharing with a DDict-focused walkthrough

Collect and inspect Dragon runtime and application metrics

Workflow patterns for AI loops, MPI orchestration, Parsl, and Batch DAGs

AI examples for data loading, agents, and inference-oriented workflows

Process a large dataset in parallel

Orchestrate executing a serial executable with many different arguments

Orchestrate a parameter sweep for an MPI application

Set up PyTorch distributed launch, rank info, and data loading on GPUs

Track application state to enable fault tolerance and recovery

Run on a collection of servers or a supercomputer

Use a Jupyter notebook with Dragon

Running functions and processes that use specific GPUs

Visualize and analyze system and custom metrics

Build a simple end-to-end workflow with Batch, DDict, and external jobs

Connect and run workflows across multiple systems

Multi-agent LLM pipelines with tools, HITL, and tracing

Serve shared vLLM backends with Dragon queues and GPUs

Schedule parallel tasks and DAG-shaped dependencies with the Batch API

Use Dragon logging to inspect runtime behavior and application issues

First tutorial for using Dragon