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@snowflake-connectors-app snowflake-connectors-app released this 20 Mar 18:33
1.8.0
9709d06
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1.8.0

Bug Fixes

  • Modeling: 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
  • Registry: Fix a bug that leads to incorrect Model is does not have _is_inference_api error message when assigning
    a supported model as a property of a CustomModel.
  • Registry: Fix a bug that inference is not working when models with more than 500 input features
    are deployed to SPCS.

Behavior Change

  • Registry: With FeatureGroupSpec support, auto inferred model signature for transformers.Pipeline models have been
    updated, including:

    • Signature for fill-mask task has been changed from

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

      to

      ModelSignature(
    inputs=[
        FeatureSpec(name="inputs", dtype=DataType.STRING),
    ],
    outputs=[
        FeatureGroupSpec(
            name="outputs",
            specs=[
                FeatureSpec(name="sequence", dtype=DataType.STRING),
                FeatureSpec(name="score", dtype=DataType.DOUBLE),
                FeatureSpec(name="token", dtype=DataType.INT64),
                FeatureSpec(name="token_str", dtype=DataType.STRING),
            ],
            shape=(-1,),
        ),
    ],
)

    • Signature for token-classification task has been changed from

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

      to

      ModelSignature(
    inputs=[FeatureSpec(name="inputs", dtype=DataType.STRING)],
    outputs=[
        FeatureGroupSpec(
            name="outputs",
            specs=[
                FeatureSpec(name="word", dtype=DataType.STRING),
                FeatureSpec(name="score", dtype=DataType.DOUBLE),
                FeatureSpec(name="entity", dtype=DataType.STRING),
                FeatureSpec(name="index", dtype=DataType.INT64),
                FeatureSpec(name="start", dtype=DataType.INT64),
                FeatureSpec(name="end", dtype=DataType.INT64),
            ],
            shape=(-1,),
        ),
    ],
)

    • Signature for question-answering task when top_k is larger than 1 has been changed from

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

      to

      ModelSignature(
    inputs=[
        FeatureSpec(name="question", dtype=DataType.STRING),
        FeatureSpec(name="context", dtype=DataType.STRING),
    ],
    outputs=[
        FeatureGroupSpec(
            name="answers",
            specs=[
                FeatureSpec(name="score", dtype=DataType.DOUBLE),
                FeatureSpec(name="start", dtype=DataType.INT64),
                FeatureSpec(name="end", dtype=DataType.INT64),
                FeatureSpec(name="answer", dtype=DataType.STRING),
            ],
            shape=(-1,),
        ),
    ],
)

    • Signature for text-classification task when top_k is None has been changed from

      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),
    ],
)

      to

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

    • Signature for text-classification task when top_k is not None has been changed from

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

      to

      ModelSignature(
    inputs=[
        FeatureSpec(name="text", dtype=DataType.STRING),
    ],
    outputs=[
        FeatureGroupSpec(
            name="labels",
            specs=[
                FeatureSpec(name="label", dtype=DataType.STRING),
                FeatureSpec(name="score", dtype=DataType.DOUBLE),
            ],
            shape=(-1,),
        ),
    ],
)

    • Signature for text-generation task has been changed from

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

      to

      ModelSignature(
    inputs=[
        FeatureGroupSpec(
            name="inputs",
            specs=[
                FeatureSpec(name="role", dtype=DataType.STRING),
                FeatureSpec(name="content", dtype=DataType.STRING),
            ],
            shape=(-1,),
        ),
    ],
    outputs=[
        FeatureGroupSpec(
            name="outputs",
            specs=[
                FeatureSpec(name="generated_text", dtype=DataType.STRING),
            ],
            shape=(-1,),
        )
    ],
)

  • Registry: PyTorch and TensorFlow models now expect a single tensor input/output by default when logging to 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)

    For multiple tensor inputs/outputs, use:

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

  • Registry: Default enable_explainability to False when the model can be deployed to Snowpark Container Services.

New Features

  • Registry: Added support to single torch.Tensor, tensorflow.Tensor and tensorflow.Variable as input or output
    data.
  • Registry: Support xgboost.DMatrix
    datatype for XGBoost models.
Assets 5
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