Snowflake-ml-python

Bug Fixes

- Model Development & Model Registry: Fix an error related to `pandas.io.json.json_normalize`.
- Allow disabling telemetry.

New Features

- Model Registry: add `create_if_not_exists` parameter in constructor.
- Model Registry: Added get_or_create_model_registry API.
- Model Registry: Added support for using GPU inference when deploying XGBoost (`xgboost.XGBModel` and `xgboost.Booster`
), PyTorch (`torch.nn.Module` and `torch.jit.ScriptModule`) and TensorFlow (`tensorflow.Module` and
`tensorflow.keras.Model`) models to Snowpark Container Services.
- Model Registry: When inferring model signature, `Sequence` of built-in types, `Sequence` of `numpy.ndarray`,
`Sequence` of `torch.Tensor`, `Sequence` of `tensorflow.Tensor` and `Sequence` of `tensorflow.Tensor` can be used
instead of only `List` of them.
- Model Registry: Added `get_training_dataset` API.
- Model Development: Size of metrics result can exceed previous 8MB limit.
- Model Registry: Added support save/load/deploy HuggingFace pipeline object (`transformers.Pipeline`) and our wrapper
(`snowflake.ml.model.models.huggingface_pipeline.HuggingFacePipelineModel`) to it. Using the wrapper to specify
configurations and the model for the pipeline will be loaded dynamically when deploying. Currently, following tasks
are supported to log without manually specifying model signatures:
- "conversational"
- "fill-mask"
- "question-answering"
- "summarization"
- "table-question-answering"
- "text2text-generation"
- "text-classification" (alias "sentiment-analysis" available)
- "text-generation"
- "token-classification" (alias "ner" available)
- "translation"
- "translation_xx_to_yy"
- "zero-shot-classification"

Bug Fixes

- Model Development: Fixed a bug when using simple imputer with numpy >= 1.25.
- Model Development: Fixed a bug when inferring the type of label columns.

Behavior Changes

- Model Registry: `log_model()` now return a `ModelReference` object instead of a model ID.
- Model Registry: When deploying a model with 1 `target method` only, the `target_method` argument can be omitted.
- Model Registry: When using the snowflake-ml-python with version newer than what is available in Snowflake Anaconda
Channel, `embed_local_ml_library` option will be set as `True` automatically if not.
- Model Registry: When deploying a model to Snowpark Container Services and using GPU, the default value of num_workers
will be 1.
- Model Registry: `keep_order` and `output_with_input_features` in the deploy options have been removed. Now the
behavior is controlled by the type of the input when calling `model.predict()`. If the input is a `pandas.DataFrame`,
the behavior will be the same as `keep_order=True` and `output_with_input_features=False` before. If the input is a
`snowpark.DataFrame`, the behavior will be the same as `keep_order=False` and `output_with_input_features=True` before.
- Model Registry: When logging and deploying PyTorch (`torch.nn.Module` and `torch.jit.ScriptModule`) and TensorFlow
(`tensorflow.Module` and `tensorflow.keras.Model`) models, we no longer accept models whose input is a list of tensor
and output is a list of tensors. Instead, now we accept models whose input is 1 or more tensors as positional arguments,
and output is a tensor or a tuple of tensors. The input and output dataframe when predicting keep the same as before,
that is every column is an array feature and contains a tensor.

New Features

- Model Registry: Added support save/load/deploy xgboost Booster model.
- Model Registry: Added support to get the model name and the model version from model references.

Bug Fixes

- Model Registry: Restore the db/schema back to the session after `create_model_registry()`.
- Model Registry: Fixed an issue that the UDF name created when deploying a model is not identical to what is provided
and cannot be correctly dropped when deployment getting dropped.
- connection_params.SnowflakeLoginOptions(): Added support for `private_key_path`.

New Features

- Model Registry: Added support save/load/deploy Tensorflow models (`tensorflow.Module`).
- Model Registry: Added support save/load/deploy MLFlow PyFunc models (`mlflow.pyfunc.PyFuncModel`).
- Model Development: Input dataframes can now be joined against data loaded from staged files.
- Model Development: Added support for non-English languages.

Bug Fixes

- Model Registry: Fix an issue that model dependencies are incorrectly reported as unresolvable on certain platforms.

Behavior Changes

- Model Registry: When predicting a model whose output is a list of NumPy ndarray, the output would not be flattened,
instead, every ndarray will act as a feature(column) in the output.

New Features

- Model Registry: Added support save/load/deploy PyTorch models (`torch.nn.Module` and `torch.jit.ScriptModule`).

Bug Fixes

- Model Registry: Fix an issue that when database or schema name provided to `create_model_registry` contains special
characters, the model registry cannot be created.
- Model Registry: Fix an issue that `get_model_description` returns with additional quotes.
- Model Registry: Fix incorrect error message when attempting to remove a unset tag of a model.
- Model Registry: Fix a typo in the default deployment table name.
- Model Registry: Snowpark dataframe for sample input or input for `predict` method that contains a column with
Snowflake `NUMBER(precision, scale)` data type where `scale = 0` will not lead to error, and will now correctly
recognized as `INT64` data type in model signature.
- Model Registry: Fix an issue that prevent model logged in the system whose default encoding is not UTF-8 compatible
from deploying.
- Model Registry: Added earlier and better error message when any file name in the model or the file name of model
itself contains characters that are unable to be encoded using ASCII. It is currently not supported to deploy such a
model.

Behavior Changes

- Model Registry: Prohibit non-snowflake-native models from being logged.
- Model Registry: `_use_local_snowml` parameter in options of `deploy()` has been removed.
- Model Registry: A default `False` `embed_local_ml_library` parameter has been added to the options of `log_model()`.
With this set to `False` (default), the version of the local snowflake-ml-python library will be recorded and used when
deploying the model. With this set to `True`, local snowflake-ml-python library will be embedded into the logged model,
and will be used when you load or deploy the model.

New Features

- Model Registry: A new optional argument named `code_paths` has been added to the arguments of `log_model()` for users
to specify additional code paths to be imported when loading and deploying the model.
- Model Registry: A new optional argument named `options` has been added to the arguments of `log_model()` to specify
any additional options when saving the model.
- Model Development: Added metrics:
- d2_absolute_error_score
- d2_pinball_score
- explained_variance_score
- mean_absolute_error
- mean_absolute_percentage_error
- mean_squared_error

Bug Fixes

- Model Development: `accuracy_score()` now works when given label column names are lists of a single value.