SDK Changelog

# Plain dictionary - no validation
payload = {"project_name": "My Project", "data_type": "image", ...}

from labellerr.core.schemas import CreateProjectParams, RotationConfig

# Type-safe with automatic validation
params = CreateProjectParams(
    project_name="My Project",
    data_type="image",
    rotations=RotationConfig(
        annotation_rotation_count=1,
        review_rotation_count=1,
        client_review_rotation_count=1
    )
)

# Everything in one call - datasets created inline
project = create_project(
    client=client,
    payload={
        "project_name": "My Project",
        "folder_to_upload": "./images",
        "annotation_template_id": "template_123",
        ...
    }
)

# Step 1: Create dataset
dataset = create_dataset_from_local(
    client=client,
    dataset_config=DatasetConfig(
        dataset_name="My Dataset",
        data_type="image"
    ),
    folder_to_upload="./images"
)
dataset.status()  # Wait for completion

# Step 2: Get annotation template
template = LabellerrAnnotationTemplate(client, "template_123")

# Step 3: Create project with explicit dependencies
project = create_project(
    client=client,
    params=CreateProjectParams(...),
    datasets=[dataset],
    annotation_template=template
)

from labellerr.core.schemas import CreateExportParams

export = project.create_export(
    export_config=CreateExportParams(
        export_name="Production Export",
        export_format="json",
        connection_id="s3_conn_123",
        export_destination="s3",
        export_folder_path="bucket/exports/"
    )
)
# Automatically polls until completion
final_status = export.status()

from labellerr.core.autolabel import LabellerrAutoLabel
from labellerr.core.schemas import TrainingRequest, Hyperparameters

autolabel = LabellerrAutoLabel(client=client)
job = autolabel.train(
    training_request=TrainingRequest(
        model_id="yolov11",
        hyperparameters=Hyperparameters(
            epochs=100,
            batch_size=16,
            learning_rate=0.001
        ),
        job_name="Custom Training"
    )
)

# Iterate through all datasets automatically
for dataset in list_datasets(client, datatype="image", scope="client", page_size=-1):
    process(dataset)

from labellerr.core.annotation_templates import create_annotation_guideline
from labellerr.core.schemas import CreateTemplateParams, AnnotationQuestion

template = create_annotation_guideline(
    client=client,
    params=CreateTemplateParams(
        data_type="image",
        template_name="Object Detection Template",
        questions=[
            AnnotationQuestion(
                option_type="BoundingBox",
                question_text="Identify objects",
                options=[...]
            )
        ]
    )
)

from labellerr.core.connectors import LabellerrS3Connection
from labellerr.core.schemas import AWSConnectionParams

# Test connection before creating
test_result = LabellerrS3Connection.test_connection(client, params)

# Create connection with type safety
connection = LabellerrS3Connection.create_connection(
    client=client,
    params=AWSConnectionParams(
        aws_access_key="...",
        aws_secrets_key="...",
        s3_path="s3://bucket/path/",
        connection_type="export",
        name="Production S3"
    )
)

project = create_project(
    client=client,
    payload={
        "project_name": "My Project",
        "folder_to_upload": "./images",
        "annotation_template_id": "template_123",
        ...
    }
)

# Step 1: Create dataset
dataset = create_dataset_from_local(
    client=client,
    dataset_config=DatasetConfig(
        dataset_name="My Dataset",
        data_type="image"
    ),
    folder_to_upload="./images"
)
dataset.status()  # Wait for completion

# Step 2: Get annotation template
template = LabellerrAnnotationTemplate(client, "template_123")

# Step 3: Create project
project = create_project(
    client=client,
    params=CreateProjectParams(...),
    datasets=[dataset],
    annotation_template=template
)

dataset = create_dataset(
    client=client,
    dataset_config={...},
    folder_to_upload="./images"
)

from labellerr.core.datasets import create_dataset_from_local
from labellerr.core.schemas import DatasetConfig

dataset = create_dataset_from_local(
    client=client,
    dataset_config=DatasetConfig(
        dataset_name="My Dataset",
        data_type="image"
    ),
    folder_to_upload="./images"
)

# Core imports remain backward compatible
from labellerr import LabellerrClient

# New modular imports
from labellerr.core.datasets import create_dataset_from_local, LabellerrDataset
from labellerr.core.projects import create_project, LabellerrProject
from labellerr.core.schemas import DatasetConfig, CreateProjectParams
from labellerr.core.connectors import LabellerrS3Connection