tessl/pypi-google-cloud-aiplatform

Comprehensive Python client library for Google Cloud Vertex AI, offering machine learning tools, generative AI models, and MLOps capabilities

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Dataset Management

Name: tessl/pypi-google-cloud-aiplatform
Author: tessl

Comprehensive dataset creation, management, and preparation for various ML tasks including tabular, image, text, video, and time series data. Vertex AI datasets provide managed data storage with automatic schema detection and data validation.

Capabilities

Tabular Datasets

Structured data management for classification, regression, and forecasting tasks with automatic schema detection and data quality analysis.

class TabularDataset:
    @classmethod
    def create(
        cls,
        display_name: str,
        gcs_source: Union[str, Sequence[str]],
        bq_source: Optional[str] = None,
        project: Optional[str] = None,
        location: Optional[str] = None,
        labels: Optional[Dict[str, str]] = None,
        encryption_spec_key_name: Optional[str] = None,
        sync: bool = True,
        create_request_timeout: Optional[float] = None,
        **kwargs
    ) -> 'TabularDataset': ...

    def import_data(
        self,
        gcs_source: Optional[Union[str, Sequence[str]]] = None,
        bq_source: Optional[str] = None,
        import_schema_uri: Optional[str] = None,
        data_item_labels: Optional[Dict] = None,
        sync: bool = True,
        **kwargs
    ) -> 'TabularDataset': ...

    @property
    def column_names(self) -> List[str]: ...
    @property
    def schema(self) -> Dict[str, str]: ...

Image Datasets

Image data management for classification, object detection, and segmentation tasks with support for various annotation formats.

class ImageDataset:
    @classmethod
    def create(
        cls,
        display_name: str,
        gcs_source: str,
        import_schema_uri: str,
        data_item_labels: Optional[Dict] = None,
        project: Optional[str] = None,
        location: Optional[str] = None,
        labels: Optional[Dict[str, str]] = None,
        encryption_spec_key_name: Optional[str] = None,
        sync: bool = True,
        create_request_timeout: Optional[float] = None,
        **kwargs
    ) -> 'ImageDataset': ...

    def import_data(
        self,
        gcs_source: str,
        import_schema_uri: str,
        data_item_labels: Optional[Dict] = None,
        sync: bool = True,
        **kwargs
    ) -> 'ImageDataset': ...

Text Datasets

Text data management for classification, entity extraction, and sentiment analysis with support for various text formats.

class TextDataset:
    @classmethod
    def create(
        cls,
        display_name: str,
        gcs_source: Union[str, Sequence[str]],
        import_schema_uri: str,
        data_item_labels: Optional[Dict] = None,
        project: Optional[str] = None,
        location: Optional[str] = None,
        labels: Optional[Dict[str, str]] = None,
        encryption_spec_key_name: Optional[str] = None,
        sync: bool = True,
        create_request_timeout: Optional[float] = None,
        **kwargs
    ) -> 'TextDataset': ...

Time Series Datasets

Specialized datasets for forecasting and time series analysis with support for multiple time series and hierarchical forecasting.

class TimeSeriesDataset:
    @classmethod
    def create(
        cls,
        display_name: str,
        gcs_source: Union[str, Sequence[str]],
        bq_source: Optional[str] = None,
        project: Optional[str] = None,
        location: Optional[str] = None,
        labels: Optional[Dict[str, str]] = None,
        encryption_spec_key_name: Optional[str] = None,
        sync: bool = True,
        create_request_timeout: Optional[float] = None,
        **kwargs
    ) -> 'TimeSeriesDataset': ...

Video Datasets

Video data management for action recognition, object tracking, and video classification tasks.

class VideoDataset:
    @classmethod
    def create(
        cls,
        display_name: str,
        gcs_source: Union[str, Sequence[str]],
        import_schema_uri: str,
        data_item_labels: Optional[Dict] = None,
        project: Optional[str] = None,
        location: Optional[str] = None,
        labels: Optional[Dict[str, str]] = None,
        encryption_spec_key_name: Optional[str] = None,
        sync: bool = True,
        create_request_timeout: Optional[float] = None,
        **kwargs
    ) -> 'VideoDataset': ...

Usage Examples

Create tabular dataset:

import google.cloud.aiplatform as aiplatform

aiplatform.init(project='my-project', location='us-central1')

dataset = aiplatform.TabularDataset.create(
    display_name="customer-data",
    gcs_source="gs://my-bucket/customer_data.csv",
    labels={"purpose": "classification", "team": "ml"}
)

print(f"Dataset created: {dataset.resource_name}")
print(f"Column names: {dataset.column_names}")

Create image dataset:

dataset = aiplatform.ImageDataset.create(
    display_name="product-images",
    gcs_source="gs://my-bucket/images/",
    import_schema_uri=aiplatform.schema.dataset.ioformat.image.single_label_classification
)

Install with Tessl CLI

npx tessl i tessl/pypi-google-cloud-aiplatform

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