Platform Cli

---
name: platform-cli
description: >
  Use when operating Ultralytics Platform from a terminal or script with the ul cloud CLI (ul cloud RESOURCE OPERATION key=value, PyPI package ultralytics-platform) — listing, creating, updating, cloning, or moving projects, datasets, and models; inspecting dataset images, statistics, metadata, duplicates, and similar images; starting and monitoring cloud training runs; per-image model validation analysis; downloading weights; exports, deployments, uploads, trash and restore, storage integrations, and account usage, storage, and billing; and building Agents (YOLO, LLM, gate, and alert workflows). For the Platform web UI or local yolo commands, see yolo; for choosing what to train and how to improve it, see yolo-training and yolo-tuning.
---

# Platform CLI (`ul cloud`)

`ul` needs Python 3.11+ and comes with `pip install ultralytics` or `pip install ultralytics-platform`. `ul cloud <resource> <operation>` calls the Platform API through the generated SDK; `ul login`, `ul train`, `ul predict`, the `ul cloud train|predict|export|download` shortcuts (see `yolo`), and the other local commands delegate to the `ultralytics` package. This skill covers Platform operations, not model selection or hyperparameter tuning.

```bash
pip install ultralytics                   # or `pip install ultralytics-platform`
export ULTRALYTICS_API_KEY="YOUR_API_KEY" # or `ul login API_KEY`
ul cloud --help                           # every resource and operation
ul cloud training start --help            # one operation's arguments, types, and choices
```

## Canonical command shapes

```bash
ul cloud account summary # plan, credits, counts; `username` is your workspace
ul cloud datasets list   # omit owner= for your own workspace; owner=TEAM for a team
ul cloud projects create project=helmets name="Helmet Detection" visibility=private
ul cloud models create body='{"owner":"WS","project":"helmets","model":"exp1","name":"Experiment 1"}'
ul cloud training start model_id=MODEL_ID gpu_type=l4 \
  train_args='{"model":"yolo26n.pt","data":"ul://WS/datasets/helmets","epochs":50}'
ul cloud models training project=helmets model=exp1 # live status, epoch progress, metrics
ul cloud models files project=helmets model=exp1    # short-lived weights download URL
```

Argument rules:

- Use the command shapes shown here without a preliminary `--help` call. Check operation help when a needed argument is missing or after an argument error.
- Arguments are SDK Python names as `key=value` (`gpu_type`, `project_id`), never `--key value`. A boolean key given without `=value` means `true`. Quote JSON for the shell.
- An operation takes one `body=` JSON object exactly when its help lists `body (dict[str, Any])`. These are the operations whose API request is a union of shapes: `models create`, `deployments update`, `images update`, `lifecycle delete-trash`, `datasets ingest`, `datasets create-batch`, `upload signed-url`, `storage-integrations create`/`discover`, and `models predict`/`deployments predict`. Every other operation takes flat fields; nested objects such as `train_args`, `metadata`, and `args` are still JSON values.
- Object and array values accept `@file.json` or `@-` for stdin. Multipart binaries such as the predict `file` field accept `@path` only.
- For body keys and value types, read only the operation's request schema, such as `paths["/api/models"].post.requestBody`, from the production contract at `https://platform.ultralytics.com/openapi.json`, instead of guessing or loading the whole file.

Behavior rules:

- Output is the complete API response on stdout, printed as JSON, text, or bytes according to its content type. Download operations return expiring signed URLs, not bytes: `datasets export`, `datasets create-export`, `models files`, and a completed `exports retrieve`. Preserve them verbatim and fetch them separately.
- Failures go to stderr: exit 1 for API/connection errors, 2 for argument/file errors, 130 when interrupted. Interrupting does not cancel a submitted job; use its cancel operation (`models delete-training`, `exports delete`, `datasets delete-batch`).
- There is no `--json`, `--fields`, `--dry-run`, or automatic pagination. `datasets list` (`limit`, `include_samples`, `include_image_urls`) and `projects list` (`limit`) take no `offset` or `search`; pass `include_samples=false` to keep dataset listings small. `models list` requires `project=`. Operations that page expose `page`, `offset`, `cursor`, or `page_token`. Follow returned continuation fields until exhausted. A limit-only listing may still be incomplete.
- An omitted path `owner` defaults to the logged-in username after one account lookup. Pass `owner=TEAM` for team workspaces. Account, billing, trash, storage-integration, and Roboflow commands act on the credential's own account, and `account summary` does not list teams for API keys.
- Display names, URL slugs, database IDs, and URIs are distinct. Training data is `ul://OWNER/datasets/DATASET`; starting weights are a checkpoint name or `ul://OWNER/PROJECT/MODEL`. Carry returned IDs and slugs into the next command; retrieve missing identifiers instead of inferring them from names or URLs. Take a named dataset's or project's slug from its listing before retrieving it.

## Working method

1. Resolve the requested outcome and target. Use exact supplied identifiers; otherwise list and pick one unambiguous match. For several matches, inspect distinguishing metadata and ask when the target or consequence stays ambiguous. When a named resource is not found, say so and offer the closest matches; never substitute another one. A bounded listing does not prove absence; broaden discovery or report what was searched. Look up datasets the user names in their own workspace; find new public datasets with `explore search q=... type=datasets`, one short keyword at a time (`aerial`, then `UAV`), narrowed with `task=`. Explore dataset search lists token-autocomplete name matches in `sort` order, then datasets whose images match the term by content; project search matches literal substrings. Before recommending a public dataset for training, verify clone eligibility, labels, and splits. If none match or search fails, say so.
2. Read current state when it affects the change (visibility, status, existing children).
3. Execute the smallest requested change, then verify from the response. Retrieve again when the response omits needed state, the write is uncertain, or the job is asynchronous.
4. Report what actually changed, current status, warnings, and a verified resource link when one exists. Creating an entry, accepting a job, and completing it are separate outcomes.

Execute clearly requested actions without repeated confirmation. For spending, sharing, or irreversible changes, resolve ambiguity about target or consequence first. A request for advice is not authorization to act; "clean things up" is not permission for account-wide deletion. Do not invent commands, flags, or status edits to simulate operations the CLI does not expose; point the user to the Platform UI for those.

## Workflows

Commands below omit the `ul cloud` prefix and the default `owner`.

| Goal               | Commands                                                                                                                                                                                                                                                                                                                                                                                                    |
| ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Inventory          | `projects list`, `datasets list`, `models list project=P`. `explore search q=... type=datasets` searches public content, which is not private inventory. Retrieve a dataset to check task, classes, splits, and readiness.                                                                                                                                                                                  |
| Create             | `projects create project=slug name="Display"`, `datasets create dataset=slug name="Display" task=detect`. Set `visibility=` deliberately.                                                                                                                                                                                                                                                                   |
| Copy               | `datasets clone dataset=D owner_body=DEST_OWNER`, `projects clone project=P owner_body=DEST_OWNER`, `models clone project=P model=M owner_body=DEST_OWNER project_body=DEST_PROJECT`. Path fields name the source; `*_body` fields name the destination. A model's destination project must exist.                                                                                                          |
| Rename, edit, move | `<resource> update` with the changed fields only; a new `name` also changes the URL slug, so use the returned slug afterwards. Move a model with `models update project=P model=M project_id=DEST_ID` as the only changed field.                                                                                                                                                                            |
| Compare runs       | `projects retrieve project=P` for model summaries, `models list project=P`, or `datasets models dataset=D` for runs on a dataset; `models retrieve project=P model=M` only for missing metrics. Tabulate status, dataset/version, configuration, and requested metrics with links. Missing metrics are unknown, not zero. There is no compare command.                                                      |
| Analyze model      | `models retrieve project=P model=M analysis=1`, then `models find-similar-training-images project=P model=M` when needed. See [Model analysis and similarity](#model-analysis-and-similarity) for coverage and hash selection.                                                                                                                                                                              |
| Weights            | `models files project=P model=M` returns a temporary checkpoint URL. No training or export is needed.                                                                                                                                                                                                                                                                                                       |
| Convert            | `exports create project=P model=M format=onnx`, then `exports retrieve project=P model=M export_id=ID` for progress and the download URL. `format=engine` needs `gpu_type`. Exporting does not deploy.                                                                                                                                                                                                      |
| Dataset versions   | `datasets create-export dataset=D` saves an immutable numbered snapshot with a signed NDJSON URL; `datasets export dataset=D` (`v=N` for a saved version) returns a download URL; `datasets restore dataset=D version=N` rolls the dataset back; `datasets update-export dataset=D version=N description="TEXT"` updates a saved version's description.                                                     |
| Deploy             | `deployments create project=P model=M deployment=slug name="Display" region=us-central1`. `deployments update deployment=D body='{"action":"replace","project":"P","model":"M"}'` swaps the model on the same URL; `body='{"action":"stop"}'` and `{"action":"start"}` pause and resume it. `deployments health deployment=D` warms it.                                                                     |
| Usage, services    | `account storage`, `billing usage-summary`, and `billing transactions` inspect the credential's own account usage; `deployments list`, `retrieve`, `logs`, and `metrics` inspect endpoints. See [Usage and billing](#usage-and-billing) and [Compute](#compute) for filters and pagination.                                                                                                                 |
| Inference          | `models predict project=P model=M body='{"file":"@image.jpg"}'` or `deployments predict deployment=D body='{"file":"@image.jpg"}'`. `images predict image_id=ID model_id=ul://...` predicts on a dataset image without saving labels.                                                                                                                                                                       |
| Agents             | `agents save name="Display" graph='{...}' version=0` creates a canvas Agent; read `agents save --help` for blocks first. Use real datasets, models, deployments, and detectable classes; fix returned `errors`. Edit: read the current `graph` and `version` with `agents list id=ID`, then `agents save id=ID name=N version=V graph='{...}'` with the full edited graph.                                  |
| Import data        | `datasets create dataset=D name="Display"`, then `upload signed-url body=` (`assetType`, `assetId`, `filename`, `contentType`, `totalBytes`) for one `.zip`, `.tar`, `.tar.gz`, `.tgz`, or `.ndjson` file, PUT the bytes with the returned headers, `datasets ingest dataset=D body='{"sessionId":"S"}'` (completes the upload; or `{"sourceUrl":...}`), then retrieve until ready. Queued is not imported. |
| Trash              | `projects delete project=P`, `datasets delete dataset=D`, and `models delete project=P model=M` move them to 30-day trash. `lifecycle trash` lists it, `lifecycle restore id=ID type=model` undeletes, `lifecycle delete-trash body='{"id":"ID","type":"model"}'` purges one item, and `body='{"all":true}'` permanently empties all workspace trash.                                                       |

## Cloud training

1. Resolve the workspace, project, a ready dataset, starting weights, and requested settings. Check `training gpu-availability` before choosing `gpu_type`; cloud training spends credits. `data` must use the dataset's actual owner, which may differ from the model's.
2. `models create body=...` with `owner`, `project`, the `model` slug, and `name` for a new experiment. Reuse a pending entry from a failed start after checking its state.
3. `training start model_id=ID gpu_type=... train_args=...` with a fresh `train_args` holding `model`, `data`, and numeric `epochs` plus requested settings. Omit `device`. Do not copy stored worker arguments wholesale; saved data paths may be worker-local.
4. `models training` or `models retrieve` for progress. Return the run link and actual status without waiting for completion unless asked. `models delete-training` cancels a run and keeps its entry; never call `training start` on a model whose run is still active.

## Recover

- **401**: check the credential source without exposing secrets. `ULTRALYTICS_API_KEY` overrides the saved login key, so logging in again does not replace a stale environment value. If the execution environment injects credentials, use its flow. Verify with a read afterwards.
- **403**: read the error for workspace access, permissions, or operation constraints; do not assume an expired login.
- Fix clear validation errors within the requested action. After a timeout or uncertain write, inspect state before retrying. Stop on unresolved inputs, permissions, or repeated failure; report partial results and remaining work instead of duplicating writes.

## Resource links

Resource links use `https://platform.ultralytics.com`: projects `/{owner}/{project}`, datasets `/{owner}/datasets/{dataset}`, models and training `/{owner}/{project}/{model}`, and deployments `/{owner}/deploy/{deployment}` with the returned `deployment` slug. After status is `ready`, report the exact `serviceUrl` returned by `deployments retrieve` for inference; never construct a service URL.

## Operation gotchas

Operation help already says what each command does. These are the side effects and constraints it omits; `--help` and the error text still win when they disagree.

### Clone

- Project, dataset, and model clones need a different destination workspace. Cloning is not how to create a same-workspace experiment; use `models create`.
- Model sources need usable weights and must not be training. Project clones carry completed models with weights but not datasets, deployments, exports, or unfinished runs.

### Visibility

- Creation may default to public and clones may keep the source's visibility, so set `visibility=` explicitly. Public projects expose their models. Public visibility is not collaborator edit access.

### Update

- Arrays and `metadata` objects replace prior values. Send the complete array or object, including unchanged entries.
- An update that includes `starred` ignores every other field; send it alone.
- Starting training again on an existing model clears its prior run metrics and results. Create a new model to keep them. Fine-tuning from existing weights does not resume the optimizer or epoch count.
- `capture_dataset_version=true` saves a new snapshot; it does not select an existing one.

### Delete, trash, restore

- `lifecycle delete-trash` is irreversible. Run it only when the user explicitly requests permanent deletion of the specified items; `body='{"all":true}'` requires an explicit request to empty all workspace trash. Otherwise, leave purging to the user in the UI.
- `projects update archived=true` only organizes; it frees nothing.
- Trashing a project also trashes its models and cancels their training. Both `projects delete` and `models delete` permanently delete attached deployments during the trash operation; restoring the project or model does not restore deployments. If deployments are attached (`lifecycle trash id=ID type=project` or `type=model` previews them), explain this consequence and obtain explicit authorization before trashing, unless the user's request already covers their permanent deletion. A 502 response means cleanup is incomplete; inspect deployments before reporting them deleted.
- Restore a parent before its children; independently trashed children need their own restore. Dataset version restore is separate: `datasets restore dataset=D version=N` replaces the current images, splits, classes, and annotations with the selected snapshot. It cannot be undone unless the current state was first versioned. When preservation matters, first run `datasets create-export dataset=D`. Stop active annotation before restoring, then wait for the dataset to return to `ready`.
- `images delete` and `images delete-bulk` are permanent and bypass trash.
- `datasets create-export` reuses an identical existing snapshot instead of creating a duplicate.

### Labels and auto-annotation

- `images update body='{"labels":[...]}'` replaces every label on the image, and `images predict` alone saves nothing. Retrieve first for a partial edit, and if the retrieve reports `labelsTruncated`, do not update that image's labels, because the replacement would delete the ones you did not see.
- `datasets create-batch dataset=D body='{"modelId":"ul://OWNER/PROJECT/MODEL"}'` auto-labels unlabeled images, persists the labels, and saves a version; add `"includeAnnotated":true` to the body to also process labeled images (their existing labels are kept). `datasets delete-batch` cancels or dismisses a run without undoing labels already saved.
- `datasets create-batch dataset=D body='{"operation":"blur"}'` blurs faces in the dataset's images (or a single image when the body includes `"imageId"`) and saves no version; run `datasets create-export` first when the originals matter.
- Annotation prediction needs compatible tasks and classes, or an explicit mapping: `class_mapping=` for `images predict`, `classMapping` inside `body=` for `datasets create-batch`. It rejects connected, depth, and more-than-three-channel datasets.

### Classes, splits, task

- `class_names` replaces names by index; keep the order for a rename. Merging or deleting classes remaps or removes labels and shifts IDs.
- `datasets redistribute-splits` reshuffles the whole dataset. `images update-bulk` moves selected IDs, and conflict policy `replace` may remove target images.
- `datasets adopt-images` adds image references as unlabeled train images, skipping existing images; only `release=false` (copy), `release=true` (move), or `class_mapping` carries labels, metadata, and splits from a dataset you can edit.
- Changing `task` converts no annotations. Switching to or from depth needs an empty dataset.

### Dataset inspection and analysis

- For duplicates or split leakage, group all image records by hash and compare IDs and splits. Storage deduplication does not prevent duplicate records. Use small pages or a downloaded export; if records are missing or clipped, report partial coverage, not zero duplicates.
- `datasets retrieve dataset=D` returns task, classes, splits, and counts. `datasets class-stats dataset=D` returns distributions and heatmaps; a set `sampleSize` means the stats came from a capped subset, and each histogram bin's `size` is its width. In `objectsPerImageHistogram`, `bin` is the lower bound: `bin=0, size=6` counts images with 0–5 objects, not just zero objects. For the unlabeled-image count, use `datasets images dataset=D has_label=false limit=1` and read `total`.
- List a dataset's images with `datasets images dataset=D` (there is no `images list`); it filters by `split`, `has_label`, `has_error`, `class_ids`, and `search`. For counts alone, use `limit=1` and read `total` (included by default), not the page length. `has_error` means a recorded processing error. Pass `include_thumbnails=false` to keep inventory pages small (`include_image_urls` and `include_labels` default off); pass `nextCursor` as `cursor` while `hasMore`. The cursor works only with `newest` or `oldest` sorting (which includes the default); other sorts page with `offset`. Equal `hash` values mean the same stored bytes, not visual similarity.
- `search=TERM` matches substrings of filenames (extension optional), class names, and metadata keys, scalar values, and array entries, but not values inside nested objects. There is no field selector; retrieve matches to verify which field hit. A 32-hex term matches the content `hash` exactly and a 24-hex term matches the image ID, so `search=HASH` finds every stored copy of an image without exporting.
- Listings omit custom `metadata`; `images retrieve image_id=ID` returns it with labels. Both listing and retrieval can truncate labels: honor `labelsTruncated` and compare returned lengths with `labelCount` or retrieval's `properties.annotationCount`. `datasets selected-images dataset=D image_ids='["ID1","ID2"]'` fetches several known IDs in one call, not one `images retrieve` each, accepting the same `include_*` options as `datasets images`; `images urls image_ids='["ID1","ID2"]'` refreshes signed URLs for up to 100 IDs from one dataset. `datasets export dataset=D` provides NDJSON metadata and annotations for bulk aggregation; On Premise datasets cannot export.
- `images find-similar-images image_id=ID` returns up to 24 near neighbors from public, your own, and team datasets, excluding images the source dataset already holds and near-duplicate copies of the query. It takes only an existing image ID and needs authentication. An image without an embedding is embedded on demand, which can take minutes; a 503 means that preparation failed, so retry.
- `datasets embeddings dataset=D` reports analysis freshness and progress. `datasets clustering dataset=D` returns 2D UMAP points, not raw embeddings; pass `nextOffset` as `offset` while `hasMore`, including with the default page size. `not_analyzed` means no layout exists; use returned IDs with `datasets selected-images` for details. `datasets create-embeddings` starts analysis; `datasets delete-embeddings` cancels it and deletes no images.

### Model analysis and similarity

- `models retrieve project=P model=M analysis=1` requires authentication and returns per-image validation `analysis` instead of model details; null means analysis is unavailable. A completed detection run with recorded per-image results is required; a missing dataset version or manifest returns scores with `traitsAvailable=false` and `coverage.mode="unavailable"`, not null analysis. Before generalizing, compare `population` with `retained` and read `coverage.mode`: only `full` covers every validation image. `traitsAvailable` gates `comparisons` (image traits versus F1); `cohorts.worst` and `.best` give whole-cohort `count` and `metrics` but only the first 100 rows as `examples`, whose labels come from the run's saved manifest, not the current dataset. `scatterSample.rows` are `[f1, width, height, pixels, aspectRatio, instanceCount]` points. Per-image `tp`/`fp`/`fn`/`f1` use IoU 0.50 at a confidence threshold the run did not record, so they cannot rebuild metrics at another threshold. Check the model's reported precision/recall before diagnosing poor performance; these counts alone do not establish deployment precision or explain a particular false positive.
- `models find-similar-training-images project=P model=M` searches public, your own, and team datasets from the run's worst validation images, excluding images its training dataset already holds. Use credentials with access to the model's workspace. The command returns candidates without adding them to the dataset. `hashes=H1,H2` must all come from `analysis.cohorts.worst.examples[].hash`; any other hash rejects the whole request. A run without a captured cohort returns an empty list; missing run/dataset identifiers or unavailable embeddings return errors instead.

### Connected storage

- Connected datasets (cloud storage or On Premise) are indexed in place, not copied. They cannot take appended uploads, versions, clones, batch annotation, or new embedding analysis; do not start analysis to obtain a missing clustering layout.
- Disconnecting an integration leaves provider objects intact but breaks dataset access until the same account reconnects. Roboflow credentials are separate from Platform credentials.

### Usage and billing

- `account storage` returns quotas as `usage.<resource>.current/limit/percent`, where `limit=-1` is unlimited; `details=true` adds the ten largest `breakdown.topItems`, not a full inventory. `billing usage-summary` returns plan usage and credits.
- `billing transactions from_=START to=END` uses `from_`, not `from`, and needs full ISO datetimes such as `2026-09-01T00:00:00Z`; bare dates are rejected. Timestamps are in `createdAt`.

### Compute

- Editing a model's `status` starts or stops nothing.
- `deployments list model=P/M` filters by a project's model, not a model ID or bare name. Use the returned `deployment` slug for `deployments retrieve`, `logs`, and `metrics`. Logs page with `nextPageToken` as `page_token`; `metrics range=` selects the window and `sparkline=true` returns the compact dashboard summary instead of the full response.
- Deployment `delete` is permanent; there is no trash for deployments.
- Export `delete` cancels a running conversion or removes the finished file; the source model is untouched.

The installed CLI is the authority: `ul version` shows the versions, `ul cloud <resource> <operation> --help` lists valid arguments and choices, and error text beats any command shape or caveat in this file. For endpoint semantics, the [Platform API reference](https://docs.ultralytics.com/platform/api) and the live `/openapi.json` win over memory.