Snowflake ML release notes¶

Bug fixes¶

Model Registry bug fixes:

• Fix unsupported model type error when logging a scikit-learn model with a score_samples inference method.

• Fix failure of inference service creation on an existing suspended service.

New features¶

New Model Registry features:

• Creating a copy of a model version with log_model with unsupported arguments now raises an exception.

Behavior changes¶

Model Registry behavior changes:

• Automatically-inferred signatures in transformers.Pipeline have been changed to use the FeatureGroupSpec task class, including:

  • Signature for fill-mask tasks:

    Before v1.8.0v1.8.0 and later

      ModelSignature(
          inputs=[
              FeatureSpec(name="inputs", dtype=DataType.STRING),
          ],
          outputs=[
              FeatureSpec(name="outputs", dtype=DataType.STRING),
          ],
    )
    

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  • Signature for token classification tasks:

    Before v1.8.0v1.8.0 and later

    ModelSignature(
        inputs=[
            FeatureSpec(name="inputs", dtype=DataType.STRING),
        ],
        outputs=[
            FeatureSpec(name="outputs", dtype=DataType.STRING),
        ],
    )
    

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  • Signature for question-answering tasks:

    Before v1.8.0v1.8.0 and later

    ModelSignature(
        inputs=[
            FeatureSpec(name="question", dtype=DataType.STRING),
            FeatureSpec(name="context", dtype=DataType.STRING),
        ],
        outputs=[
            FeatureSpec(name="outputs", dtype=DataType.STRING),
        ],
    )
    

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  • Signature for question-answering tasks when top_k is greater than 1:

    Before v1.8.0v1.8.0 and later

    ModelSignature(
        inputs=[
            FeatureSpec(name="question", dtype=DataType.STRING),
            FeatureSpec(name="context", dtype=DataType.STRING),
        ],
        outputs=[
            FeatureSpec(name="outputs", dtype=DataType.STRING),
        ],
    )
    

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  • Signature for text-classification tasks when top_k is None:

    Before v1.8.0v1.8.0 and later

    ModelSignature(
        inputs=[
            FeatureSpec(name="text", dtype=DataType.STRING),
            FeatureSpec(name="text_pair", dtype=DataType.STRING),
        ],
        outputs=[
            FeatureSpec(name="label", dtype=DataType.STRING),
            FeatureSpec(name="score", dtype=DataType.DOUBLE),
        ],
    )
    

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  • Signature for text-classification tasks when top_k is not None:

    Before v1.8.0v1.8.0 and later

    ModelSignature(
        inputs=[
            FeatureSpec(name="text", dtype=DataType.STRING),
            FeatureSpec(name="text_pair", dtype=DataType.STRING),
        ],
        outputs=[
            FeatureSpec(name="outputs", dtype=DataType.STRING),
        ],
    )
    

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  • Signature for text-generation tasks:

    Before v1.8.0v1.8.0 and later

    ModelSignature(
        inputs=[FeatureSpec(name="inputs", dtype=DataType.STRING)],
        outputs=[
            FeatureSpec(name="outputs", dtype=DataType.STRING),
        ],
    )
    

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• PyTorch and TensorFlow models now expect a single tensor input and output by default when they are logged to the Model Registry. To use multiple tensors (previous behavior), set options={"multiple_inputs": True}.

  Example with single tensor input:

  import torch
  
  class TorchModel(torch.nn.Module):
      def __init__(self, n_input: int, n_hidden: int, n_out: int, dtype: torch.dtype = torch.float32) -> None:
          super().__init__()
          self.model = torch.nn.Sequential(
              torch.nn.Linear(n_input, n_hidden, dtype=dtype),
              torch.nn.ReLU(),
              torch.nn.Linear(n_hidden, n_out, dtype=dtype),
              torch.nn.Sigmoid(),
          )
  
      def forward(self, tensor: torch.Tensor) -> torch.Tensor:
          return cast(torch.Tensor, self.model(tensor))
  
  # Sample usage:
  data_x = torch.rand(size=(batch_size, n_input))
  
  # Log model with single tensor
  reg.log_model(
      model=model,
      ...,
      sample_input_data=data_x
  )
  
  # Run inference with single tensor
  mv.run(data_x)
  

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  For multiple tensor inputs or outputs, use:

  reg.log_model(
      model=model,
      ...,
      sample_input_data=[data_x_1, data_x_2],
      options={"multiple_inputs": True}
  )
  

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• enable_explainability now defaults to False when the model can be deployed to Snowpark Container Services.

Bug fixes¶

Modeling bug fixes:

• Fix a bug in some metrics that allowed an unsupported version of numpy to be installed automatically in the stored procedure, resulting in a numpy error on execution.

Model Registry bug fixes:

• Fix a bug that leads to incorrect Model does not have _is_inference_api error message when assigning a supported model as a property of a CustomModel.

• Fix a bug where inference does not work when models with more than 500 input features are deployed to SPCS.

New features¶

New Model Registry features:

• Support for using a single torch.Tensor, tensorflow.Tensor and tensorflow.Variable as input or output data.

• Support for xgboost.DMatrix datatype for XGBoost models.

Bug fixes¶

Model Registry bug fixes:

• Fixed a compatibility issue where, when using snowflake-ml-python 1.7.0 or later to save a tensorflow.keras model with keras 2.x, the model could not be run in Snowflake. This issue occurred when relax_version is set to True (or default) and a new version of snowflake-ml-python is available. If you have logged an affected model, you can recover it by loading it using ModelVerison.load and logging it again with the latest version of snowflake-ml-python.

• Removed the validation that prevents data that does not have non-null values from being passed to ModelVersion.run.

New features¶

New Model Registry features:

• Support for Hugging Face model configurations with auto-mapping functionality.

• Support for keras 3.x models with tensorflow and pytorch backends.

New Model Explainability features:

• Support for native and snowflake-ml-python sklearn pipelines.

Bug fixes¶

Registry bug fixes:

• Fixed an issue in which Hugging Face pipelines were loaded using an incorrect data type.

• Fixed an issue in which only one row was actually used when infering a model signature.

New features¶

New Cortex features:

• New guardrails option on the Complete function for enabling Cortex Guard.

Dependency upgrades¶

• fsspec and s3fs must be 2024.6.1 or later and less than 2026.

• mlflow must be 2.16.0 or later and less than 3.

New features¶

New Cortex features:

• Cortex functions now have “snake_case” names. For example, ClassifyText is now classify_text. The old “CamelCase” names still work, but will be removed in a future release.

New Model Registry features:

• Registry now supports more than 500,000 features.

• Added user_files argument to Registry.log_model for including images or other files with the model.

• Added support for handling Hugging Face model configurations with auto-mapping functionality.

New Data features:

• Added the DataConnector.from_sql constructor.

Bug fixes¶

Registry bug fixes:

• Fixed a bug that occurred when providing a non-range index pandas DataFrame as the input to ModelVersion.run.

• Improved random model registry name generation to avoid collisions.

• Fixed an issue when inferring a signature or running inference with Snowpark DataFrame that has a column whose type is ARRAY and contains a NULL value.

• ModelVersion.run now accepts a fully-qualified service name.

• Fixed an error in log_model for any scikit-learn models with only data preprocessing, including preprocessing-only pipeline models.

Monitoring bug fixes:

• Fixed an issue with creating monitors using fully-qualified names.