[1.0.8]

1.0.8

Bug Fixes

• Model Development: Ordinal encoder can be used with mixed input column types.
• Model Registry: Fix an issue that incorrect docker executable is used when building images.
• Model Registry: Fix an issue that specifying token argument when using
  snowflake.ml.model.models.huggingface_pipeline.HuggingFacePipelineModel with transformers < 4.32.0 is not effective.
• Model Registry: Fix an issue that incorrect system function call is used when deploying to SPCS.
• Model Registry: Fix an issue when using a transformers.pipeline that does not have a tokenizer.
• Model Registry: Fix incorrectly-inferred image repository name during model deployment to SPCS.
• Model Registry: Fix GPU resource retention issue caused by failed or stuck previous deployments in SPCS.

[1.0.7]

Bug Fixes

• Model Development & Model Registry: Fix an error related to pandas.io.json.json_normalize.

[1.0.6]

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.

[1.0.5] Release

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.

[1.0.4] Release

1.0.4

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.

[1.0.3] Release

1.0.3 (2023-07-14)

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.

[1.0.2] Release

1.0.2 (2023-06-22)

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.

[1.0.1]. Release

Behavior Changes

• Model Development: Changed Metrics APIs to imitate sklearn metrics modules:
  • accuracy_score(), confusion_matrix(), precision_recall_fscore_support(), precision_score() methods move from respective modules to metrics.classification.
• Model Registry: The dafault table/stage created by the Registry now uses "SYSTEM" as a prefix.
• Model Registry: get_model_history() method as been enhanced to include the history of model deployment.

New Features

• Model Registry: A default False flag named replace_udf has been added to the options of deploy(). Setting this to True will allow overwrite existing UDF with the same name when deploying.
• Model Development: Added metrics:
  • f1_score
  • fbeta_score
  • recall_score
  • roc_auc_score
  • roc_curve
  • log_loss
  • precision_recall_curve
• Model Registry: A new argument named permanent has been added to the arguemnt of deploy(). Setting this to True allows the creation of a permanent deployment without needing to specify the UDF location.
• Model Registry: A new method list_deployments() has been added to enumerate all permanent deployments originating from a specific model.
• Model Registry: A new method get_deployment() has been added to fetch a deployment by its deployment name.
• Model Registry: A new method delete_deployment() has been added to remove an existing permanent deployment.

[1.0.0]. Release

Behavior Changes

• Model Development: Preprocessing and Metrics move to the modeling package: snowflake.ml.modeling.preprocessing and snowflake.ml.modeling.metrics.
• Model Development: get_sklearn_object() method is renamed to to_sklearn(), to_xgboost(), and to_lightgbm() for respective native models.
• Model Registry: predict() method moves from Registry to ModelReference.
• Model Registry: _snowml_wheel_path parameter in options of deploy(), is replaced with _use_local_snowml with default value of False. Setting this to True will have the same effect of uploading local SnowML code when executing a model in the warehouse.
• Model Registry: Removed id field from ModelReference constructor.

New Features

• Added PolynomialFeatures transformer to snowflake.ml.modeling.preprocessing module.
• Added metrics:
  • accuracy_score
  • confusion_matrix
  • precision_recall_fscore_support
  • precision_score

Bug Fixes

• Model Registry: Model version can now be any string (not required to be a valid identifier)
• Model Development: deploy() & predict() methods now correctly escapes identifiers

[0.3.3] Release

Project import generated by Copybara. (#18)

GitOrigin-RevId: 288c0c4da10ce230b81b6eb80316011cbb76252b

Co-authored-by: Snowflake Authors <[email protected]>