Ultralytics Platform

---
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", "run Moondream on Platform", "auto-annotate a Platform dataset", "run hosted AI inference", "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 local YOLO training and inference. Use the generated `ultralytics-platform`
Python SDK for Platform resources, hosted inference, and AI annotation. It handles authentication,
typed responses, retries, and errors.

## Read the live contract

Before API work, check the generated [API reference](https://platform.ultralytics.com/api/docs) or
`GET https://platform.ultralytics.com/openapi.json`. Treat the live OpenAPI document as authoritative
when examples disagree. These recipes were checked against API and SDK v0.1.62 on 2026-09-24.
Check the [SDK source](https://github.com/ultralytics/sdk) for generated method signatures.

```bash
uv pip install -U "ultralytics-platform>=0.1.62"
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       |
| Predict with model weights or a dedicated endpoint        | `client.models.predict` / `client.deployments.predict` |
| Preview Moondream or other AI labels on a stored image    | `client.images.predict`          |
| Save AI labels across a dataset                          | `client.datasets.create_batch`   |
| Use another language or inspect a new field                | Live OpenAPI                     |

### Live training and `ul://` URIs

Pass an owner-qualified project to stream a run:

```python
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.

```python
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](references/recipes.md) for live-run diagnosis, finished-run upload,
dataset upload, hosted inference, Moondream and other AI annotation, 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.
- Upload with a signed URL and `PUT` using the returned `headers`. Dataset ingest now verifies and
  completes the upload itself, so `upload.complete` is optional for datasets. Models still require it.
- 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`.
- Hosted AI annotation uses a stored image ID and dataset class names. It is not a free-form chat or
  caption API. Single-image predictions are unsaved, while batch annotation writes labels.
- On `429`, wait for `Retry-After` before retrying. Do not invent fixed sleeps.

## Cost and destructive actions

Cloud training, model exports, deployments, and batch image processing can spend credits. Confirm
the requested scope and cost before an unapproved billable launch. Do not ask again when the user
has already authorized it. Check `client.billing.usage_summary()` for current usage and plan limits.
Training returns cost estimates, but not every create response includes a price.

Get approval for deletes outside the user's authorized scope. Project, dataset, and model deletes
move resources to 30-day trash. Image deletion and `client.lifecycle.delete_trash` are permanent.