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name ultralytics-platform
description This skill should be used when user asks to "upload my model to Ultralytics Platform", "push this run to the platform", "upload a dataset to platform", "download a dataset from platform", "search platform datasets", "start cloud training", "train on platform GPUs", "export a model on platform", "deploy a model endpoint", "why is my run not showing on platform", or mentions platform.ultralytics.com, ul:// URIs, ultralytics-platform, or ULTRALYTICS_API_KEY.

Ultralytics Platform

Use ultralytics for YOLO training and inference. Use the generated ultralytics-platform Python SDK for API resource work. It follows the same contract as the live API and handles authentication, typed responses, retries, and errors.

Read the live contract

Before API work, check the generated API reference or GET https://platform.ultralytics.com/openapi.json. Treat the live OpenAPI document as authoritative when examples disagree. The shapes below match API and SDK v0.1.18, checked on 2026-08-27.

uv pip install -U "ultralytics-platform>=0.1.18"
export ULTRALYTICS_API_KEY=ul_... # Settings > API Keys

Platform() reads ULTRALYTICS_API_KEY. The ultralytics package also reads the key saved by yolo login. Never print or commit a key.

Choose the interface

Goal Interface
Track a run that has not started ultralytics training callback
Train with a Platform dataset or model ultralytics with a ul:// URI
Manage datasets, models, training, exports, or deployments ultralytics-platform SDK
Use another language or inspect a new field Live OpenAPI

Live training and ul:// URIs

Pass an owner-qualified project to stream a run:

from ultralytics import YOLO

YOLO("yolo26n.pt").train(data="coco8.yaml", epochs=100, project="owner/project", name="run1")

project= is required. Without it, the callback exits before creating a Platform run. Use the owner prefix for a team workspace.

YOLO("ul://owner/project/model").train(data="ul://owner/datasets/dataset", epochs=100)

SDK

Use a context manager and owner/name paths. Keep returned IDs for operations that require them, including image operations, upload assetId, training modelId, and export IDs.

Responses have resource-specific shapes, not a generic envelope. Create calls return id, owner, and the URL name at the top level. Detail calls wrap the resource under its type, such as dataset. A rename changes the URL name, so use the name returned by the update response.

Read references/recipes.md for live-run diagnosis, finished-run upload, dataset upload, and billable jobs.

Invariants

  • Confirm the target workspace with client.account.summary() and read the exact resource before a mutation. Team work requires an API key created in that workspace.
  • A direct upload is signed URL, PUT with the returned headers, upload completion, then dataset ingest. Model uploads stop after completion.
  • Dataset ingest accepts one source: sessionId, sourceUrl, or a connected-storage reference. Set targetSplit when every incoming image must enter one split.
  • Top-level model metrics accepts only the contract's named summary metrics. Per-epoch trainResults[].metrics accepts numeric metric names from results.csv.
  • On 429, wait for Retry-After before retrying. Do not invent fixed sleeps.

Cost and destructive actions

Cloud training, model exports, and deployments can spend credits. Get approval before calling client.training.start, client.exports.create, or client.deployments.create, then report the cost returned by the create response. Get approval before deletes. Resource deletes move projects, datasets, and models to 30-day trash. client.lifecycle.delete_trash is permanent.