Instrument your research in Python.

$ pip install synthgraph

import os from synthgraph import SynthGraphClient client = SynthGraphClient(    api_key=os.environ["SYNTHGRAPH_API_KEY"],    # Point at a hosted or self-controlled deployment.    api_url=os.environ.get("SYNTHGRAPH_API_URL"),)

project = client.projects.create(    name="Wildlife Detection",    description="Synthetic rain scenes for detection in adverse weather",) experiment = client.experiments.create(    project_id=project.id,    name="Rainy Scene Generation",)

from synthgraph import Generator, Reproducibility generation = client.generations.create(    experiment_id=experiment.id,    name="Rainy Scene Generation",    generator=Generator(        name="blender",        version="4.2.0",    ),    parameters={        "weather": "rain",        "occlusion": 0.30,        "camera_distance": 12,    },    reproducibility=Reproducibility(        seed=42,    ),)

# The dataset stays where it already lives. SynthGraph records# the reference, the version and the checksum — not the bytes.client.generations.complete(    generation.id,    dataset={        "name": "rain_dataset",        "version": "v7",        "uri": "s3://research/rain-v7",        "checksum": "sha256:9c1f...e40b",    },    assets=[        {"name": "vehicle-model", "version": "v7"},        {"name": "forest-texture", "version": "v4"},    ],)

try:    frames = render_dataset(**parameters)except RenderError as error:    # A failed run keeps its provenance and stays in the history.    client.generations.fail(generation.id, reason=str(error))    raise client.generations.complete(generation.id, frame_count=len(frames))