1.7.0

1.7.0

Behavior Change

• Generic: Require python >= 3.9.
• Data Connector: Update to_torch_dataset and to_torch_datapipe to add a dimension for scalar data.
  This allows for more seamless integration with PyTorch DataLoader, which creates batches by stacking inputs of each batch.

Examples:

ds = connector.to_torch_dataset(shuffle=False, batch_size=3)

• Input: "col1": [10, 11, 12]

  • Previous batch: array([10., 11., 12.]) with shape (3,)
  • New batch: array([[10.], [11.], [12.]]) with shape (3, 1)

• Input: "col2": [[0, 100], [1, 110], [2, 200]]

  • Previous batch: array([[ 0, 100], [ 1, 110], [ 2, 200]]) with shape (3,2)
  • New batch: No change

• Model Registry: External access integrations are optional when creating a model inference service in
  Snowflake >= 8.40.0.

• Model Registry: Deprecate build_external_access_integration with build_external_access_integrations in
  ModelVersion.create_service().

Bug Fixes

• Registry: Updated log_model API to accept both signature and sample_input_data parameters.
• Feature Store: ExampleHelper uses fully qualified path for table name. change weather features aggregation from 1d to 1h.
• Data Connector: Return numpy array with appropriate object type instead of list for multi-dimensional
  data from to_torch_dataset and to_torch_datapipe
• Model explainability: Incompatibility between SHAP 0.42.1 and XGB 2.1.1 resolved by using latest SHAP 0.46.0.

New Features

• Registry: Provide pass keyworded variable length of arguments to class ModelContext. Example usage:

mc = custom_model.ModelContext(
    config = 'local_model_dir/config.json',
    m1 = model1
)

class ExamplePipelineModel(custom_model.CustomModel):
    def __init__(self, context: custom_model.ModelContext) -> None:
      super().__init__(context)
      v = open(self.context['config']).read()
      self.bias = json.loads(v)['bias']

    @custom_model.inference_api
    def predict(self, input: pd.DataFrame) -> pd.DataFrame:
      model_output = self.context['m1'].predict(input)
      return pd.DataFrame({'output': model_output + self.bias})

• Model Development: Upgrade scikit-learn in UDTF backend for log_loss metric. As a result, eps argument is now ignored.
• Data Connector: Add the option of passing a None sized batch to to_torch_dataset for better
  interoperability with PyTorch DataLoader.
• Model Registry: Support pandas.CategoricalDtype
• Registry: It is now possible to pass signatures and sample_input_data at the same time to capture background
  data from explainablity and data lineage.