Ultralytics YOLO

Run the same lifecycle on three complementary surfaces:

Ultralytics Platform — the fastest start: upload or clone data, annotate in the browser, train on cloud GPUs, inspect metrics, test predictions, export, and deploy a dedicated endpoint without local setup.
ultralytics package / yolo CLI — use local or remote compute, scripts, notebooks, custom pipelines, and exported artifacts directly.
ul CLI (Python 3.11+, installed by ultralytics or ultralytics-platform) — script the Platform API itself: ul cloud <resource> <operation> key=value lists, creates, clones, trains, exports, and deploys Platform resources from a terminal (see platform-cli).

ul cloud train|predict|export upload local inputs as needed and run on Platform. Training returns after submission; add watch to follow and download results. ul cloud download uses the printed model URI and waits if needed; Ctrl-C stops waiting without canceling training. All four shortcuts need ultralytics installed; the ul cloud <resource> <operation> commands do not.

ul cloud train model=yolo26n.pt data=ul://username/datasets/helmets epochs=100 project=helmets name=exp1 # → run URI and download command
ul cloud download model=ul://username/helmets/exp1                                                       # → weights/best.pt and results
ul cloud predict model=ul://username/helmets/exp1 source=video.mp4                                       # → annotated output
ul cloud export model=ul://username/helmets/exp1 format=onnx                                             # → downloaded artifact

Mix them freely. Set ULTRALYTICS_API_KEY, use a Platform dataset as data=ul://username/datasets/dataset-slug, and set project=username/project-slug name=experiment during local training to stream its metrics back to Platform.