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GitOrigin-RevId: fff2865353d805e86c35abc970949424d1c07833 Co-authored-by: Snowflake Authors <[email protected]>
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‎CHANGELOG.md‎

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# Release History
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3-
## 1.7.5
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## 1.8.0
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### Bug Fixes
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- Modeling: Fix a bug in some metrics that allowed an unsupported version of numpy to be installed
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automatically in the stored procedure, resulting in a numpy error on execution
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- Registry: Fix a bug that leads to incorrect `Model is does not have _is_inference_api` error message when assigning
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a supported model as a property of a CustomModel.
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- Registry: Fix a bug that inference is not working when models with more than 500 input features
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are deployed to SPCS.
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### Behavior Change
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- Registry: With FeatureGroupSpec support, auto inferred model signature for `transformers.Pipeline` models have been
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updated, including:
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- Signature for fill-mask task has been changed from
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="inputs", dtype=DataType.STRING),
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],
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outputs=[
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FeatureSpec(name="outputs", dtype=DataType.STRING),
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],
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)
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```
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to
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="inputs", dtype=DataType.STRING),
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],
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outputs=[
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FeatureGroupSpec(
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name="outputs",
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specs=[
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FeatureSpec(name="sequence", dtype=DataType.STRING),
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FeatureSpec(name="score", dtype=DataType.DOUBLE),
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FeatureSpec(name="token", dtype=DataType.INT64),
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FeatureSpec(name="token_str", dtype=DataType.STRING),
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],
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shape=(-1,),
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),
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],
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)
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```
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- Signature for token-classification task has been changed from
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="inputs", dtype=DataType.STRING),
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],
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outputs=[
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FeatureSpec(name="outputs", dtype=DataType.STRING),
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],
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)
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```
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to
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```python
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ModelSignature(
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inputs=[FeatureSpec(name="inputs", dtype=DataType.STRING)],
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outputs=[
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FeatureGroupSpec(
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name="outputs",
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specs=[
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FeatureSpec(name="word", dtype=DataType.STRING),
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FeatureSpec(name="score", dtype=DataType.DOUBLE),
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FeatureSpec(name="entity", dtype=DataType.STRING),
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FeatureSpec(name="index", dtype=DataType.INT64),
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FeatureSpec(name="start", dtype=DataType.INT64),
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FeatureSpec(name="end", dtype=DataType.INT64),
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],
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shape=(-1,),
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),
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],
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)
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```
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- Signature for question-answering task when top_k is larger than 1 has been changed from
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="question", dtype=DataType.STRING),
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FeatureSpec(name="context", dtype=DataType.STRING),
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],
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outputs=[
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FeatureSpec(name="outputs", dtype=DataType.STRING),
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],
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)
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```
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to
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="question", dtype=DataType.STRING),
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FeatureSpec(name="context", dtype=DataType.STRING),
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],
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outputs=[
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FeatureGroupSpec(
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name="answers",
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specs=[
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FeatureSpec(name="score", dtype=DataType.DOUBLE),
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FeatureSpec(name="start", dtype=DataType.INT64),
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FeatureSpec(name="end", dtype=DataType.INT64),
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FeatureSpec(name="answer", dtype=DataType.STRING),
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],
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shape=(-1,),
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),
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],
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)
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```
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- Signature for text-classification task when top_k is `None` has been changed from
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="text", dtype=DataType.STRING),
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FeatureSpec(name="text_pair", dtype=DataType.STRING),
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],
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outputs=[
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FeatureSpec(name="label", dtype=DataType.STRING),
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FeatureSpec(name="score", dtype=DataType.DOUBLE),
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],
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)
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```
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to
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="text", dtype=DataType.STRING),
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],
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outputs=[
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FeatureSpec(name="label", dtype=DataType.STRING),
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FeatureSpec(name="score", dtype=DataType.DOUBLE),
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],
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)
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```
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- Signature for text-classification task when top_k is not `None` has been changed from
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="text", dtype=DataType.STRING),
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FeatureSpec(name="text_pair", dtype=DataType.STRING),
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],
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outputs=[
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FeatureSpec(name="outputs", dtype=DataType.STRING),
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],
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)
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```
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to
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```python
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ModelSignature(
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inputs=[
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FeatureSpec(name="text", dtype=DataType.STRING),
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],
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outputs=[
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FeatureGroupSpec(
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name="labels",
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specs=[
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FeatureSpec(name="label", dtype=DataType.STRING),
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FeatureSpec(name="score", dtype=DataType.DOUBLE),
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],
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shape=(-1,),
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),
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],
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)
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```
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- Signature for text-generation task has been changed from
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```python
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ModelSignature(
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inputs=[FeatureSpec(name="inputs", dtype=DataType.STRING)],
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outputs=[
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FeatureSpec(name="outputs", dtype=DataType.STRING),
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],
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)
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```
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to
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```python
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ModelSignature(
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inputs=[
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FeatureGroupSpec(
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name="inputs",
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specs=[
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FeatureSpec(name="role", dtype=DataType.STRING),
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FeatureSpec(name="content", dtype=DataType.STRING),
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],
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shape=(-1,),
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),
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],
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outputs=[
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FeatureGroupSpec(
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name="outputs",
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specs=[
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FeatureSpec(name="generated_text", dtype=DataType.STRING),
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],
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shape=(-1,),
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)
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],
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)
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```
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- Registry: PyTorch and TensorFlow models now expect a single tensor input/output by default when logging to Model
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Registry. To use multiple tensors (previous behavior), set `options={"multiple_inputs": True}`.
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Example with single tensor input:
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```python
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import torch
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class TorchModel(torch.nn.Module):
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def __init__(self, n_input: int, n_hidden: int, n_out: int, dtype: torch.dtype = torch.float32) -> None:
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super().__init__()
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self.model = torch.nn.Sequential(
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torch.nn.Linear(n_input, n_hidden, dtype=dtype),
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torch.nn.ReLU(),
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torch.nn.Linear(n_hidden, n_out, dtype=dtype),
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torch.nn.Sigmoid(),
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)
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def forward(self, tensor: torch.Tensor) -> torch.Tensor:
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return cast(torch.Tensor, self.model(tensor))
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# Sample usage:
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data_x = torch.rand(size=(batch_size, n_input))
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# Log model with single tensor
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reg.log_model(
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model=model,
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...,
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sample_input_data=data_x
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)
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# Run inference with single tensor
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mv.run(data_x)
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```
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For multiple tensor inputs/outputs, use:
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```python
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reg.log_model(
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model=model,
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...,
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sample_input_data=[data_x_1, data_x_2],
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options={"multiple_inputs": True}
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)
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```
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- Registry: Default `enable_explainability` to False when the model can be deployed to Snowpark Container Services.
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### New Features
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- Registry: Added support to single `torch.Tensor`, `tensorflow.Tensor` and `tensorflow.Variable` as input or output
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data.
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- Registry: Support [`xgboost.DMatrix`](https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.DMatrix)
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datatype for XGBoost models.
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## 1.7.5 (03-06-2025)
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- Support Python 3.12.
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- Explainability: Support native and snowml sklearn pipeline
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- Explainability: Support native and snowflake.ml.modeling sklearn pipeline
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### Bug Fixes
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`ValueError(f"{self.entrypoint} must be a subpath of {self.source}")`
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- ML Job (PrPr): Fixed a bug in Ray cluster startup config which caused certain Runtime APIs to fail
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### Behavior Change
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### New Features
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- Registry: Added support for handling Hugging Face model configurations with auto-mapping functionality.

‎CONTRIBUTING.md‎

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@@ -161,7 +161,7 @@ For example, to run all autogenerated tests locally:
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```sh
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# Then run all autogenerated tests
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bazel test //... --test_tag_filters=autogen
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bazel test //... --test_tag_filters=autogen --config=core
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```
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### Coverage

‎bazel/environments/conda-env-all.yml‎

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- sentence-transformers==2.7.0
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- sentencepiece==0.1.99
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- shap==0.46.0
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- snowflake-connector-python==3.10.0
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- snowflake-snowpark-python==1.17.0
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- snowflake-connector-python==3.12.0
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- snowflake-snowpark-python==1.28.0
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- sphinx==5.0.2
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- sqlparse==0.4.4
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- starlette==0.27.0
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- tensorflow==2.17.0
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- tokenizers==0.15.1
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- toml==0.10.2
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- torchdata==0.6.1
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- transformers==4.37.2
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- transformers==4.39.3
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- types-PyYAML==6.0.12.12
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- types-protobuf==4.23.0.1
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- types-requests==2.30.0.0

‎bazel/environments/conda-env-core.yml‎

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- s3fs==2024.6.1
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- scikit-learn==1.5.1
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- scipy==1.10.1
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- snowflake-connector-python==3.10.0
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- snowflake-snowpark-python==1.17.0
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- snowflake-connector-python==3.12.0
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- snowflake-snowpark-python==1.28.0
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- sphinx==5.0.2
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- sqlparse==0.4.4
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- starlette==0.27.0

‎bazel/environments/conda-env-keras.yml‎

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- s3fs==2024.6.1
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- scikit-learn==1.5.1
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- scipy==1.10.1
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- snowflake-connector-python==3.10.0
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- snowflake-snowpark-python==1.17.0
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- snowflake-connector-python==3.12.0
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- snowflake-snowpark-python==1.28.0
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- sphinx==5.0.2
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- sqlparse==0.4.4
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- starlette==0.27.0

‎bazel/environments/conda-env-ml.yml‎

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- scikit-learn==1.5.1
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- scipy==1.10.1
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- shap==0.46.0
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- snowflake-connector-python==3.10.0
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- snowflake-snowpark-python==1.17.0
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- snowflake-connector-python==3.12.0
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- snowflake-snowpark-python==1.28.0
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- sphinx==5.0.2
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- sqlparse==0.4.4
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- starlette==0.27.0

‎bazel/environments/conda-env-torch.yml‎

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- scipy==1.10.1
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- sentence-transformers==2.7.0
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- sentencepiece==0.1.99
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- snowflake-connector-python==3.10.0
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- snowflake-snowpark-python==1.17.0
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- snowflake-connector-python==3.12.0
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- snowflake-snowpark-python==1.28.0
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- sphinx==5.0.2
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- sqlparse==0.4.4
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- starlette==0.27.0
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- tokenizers==0.15.1
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- toml==0.10.2
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- torchdata==0.6.1
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- transformers==4.37.2
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- transformers==4.39.3
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- types-PyYAML==6.0.12.12
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- types-protobuf==4.23.0.1
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- types-requests==2.30.0.0

‎bazel/environments/conda-optional-dependency-torch.yml‎

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dependencies:
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- mlflow>=2.16.0, <3
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- pytorch>=2.0.1,<3
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- sentence-transformers>=2.7.0,<3
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- sentence-transformers>=2.7.0,<4
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- sentencepiece>=0.1.95,<0.2.0
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- tokenizers>=0.15.1,<1
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- torchdata>=0.4,<1
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- transformers>=4.37.2,<5
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- transformers>=4.39.3,<5

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