Models

Models are produced by training runs. The SDK doesn’t insert model rows directly — you train, then read.

from pictograph import Client
client = Client()

list / iter

models = client.models.list(limit=20)
for m in models:
    print(m.name, m.architecture, m.status, m.metrics)

# Or auto-page:
for m in client.models.iter(page_size=50):
    print(m.id, m.model_type)

Arg	Type	Default	Notes
limit	int	50	Backend cap: 500
status	ModelStatus | None	None	"training" / "ready" / "failed" / "archived"
model_type	ModelType | None	None	"object_detection" / "semantic_segmentation" / "instance_segmentation" / "classification"

get

model = client.models.get("model-uuid")
print(model.architecture, model.metrics["mAP"], model.class_mapping)

Returns Model. Inspect metrics (mAP, precision, recall) and class_mapping (index → class name) for inference setup.

download

Stream the ONNX weights to a local file. Only status="ready" models are downloadable — training / failed raise 409 ConflictError.

from pathlib import Path

client.models.download("model-uuid", output_path=Path("./yolox.onnx"))

Arg	Type
model_id	str
output_path	str | Path

The download is chunked and checksummed against the object MD5. Safe for multi-GB models.

delete

client.models.delete("model-uuid")

Soft-delete (sets status="archived"). Weights are not purged on soft-delete — contact support to permanently remove them.

Status lifecycle

status	Meaning
training	Training is in progress; download returns 409.
ready	Trained successfully; weights downloadable via download().
failed	Training stopped with an error. Inspect the source TrainingRun.error_message.
archived	Soft-deleted. Hidden from list() unless status="archived" filter passed.

Inference

The SDK ships read access only — there is no client.models.infer() endpoint in v1.0.0. To run inference, download the ONNX file and use your own runtime (onnxruntime, tensorrt, etc.):

import onnxruntime as ort

client.models.download(model.id, output_path="./model.onnx")
session = ort.InferenceSession("./model.onnx")
# … standard ORT inference loop

The image_inference credit operation (~1 cr/image) reserved for a future managed inference endpoint — not yet exposed.

Common errors

Status	Exception	Cause
404	NotFoundError	model_id missing or belongs to another org
409	ConflictError	download on a non-ready model