Snowpark ML release notes¶
This article contains the release notes for the Snowpark ML, including the following when applicable:
Behavior changes
New features
Customer-facing bug fixes
Note
These notes do not include changes in features that have not been publicly released.
Version 1.5.0 (2024-05-01)¶
Behavior changes¶
Model Registry behavior changes:
The
fit_transform
method can now return either a Snowpark DataFrame or a pandas DataFrame, matching the kind of DataFrame passed to the method.
New features¶
New Model Registry features:
Added support for exporting models from the registry (
ModelVersion.export
).Added support for loading the underlying model object (
ModelVersion.load
).Added support for renaming models (
Model.rename
).
Bug fixes¶
Model Registry bug fixes:
Fixed the “invalid parameter
SHOW_MODEL_DETAILS_IN_SHOW_VERSIONS_IN_MODEL
” error.
Version 1.4.1 (2024-04-18)¶
New features¶
New Model Registry features:
Added support for catboost models (
catboost.CatBoostClassifier
,catboost.CatBoostRegressor
).Added support for lightgbm models (
lightgbm.Booster
,lightgbm.LightGBMClassifier
,lightgbm.LightGBMRegressor
).
Bug fixes¶
Model Registry bug fixes:
Fixed bug that caused
relax_version
option to not work.
Version 1.4.0 (2024-04-08)¶
Behavior changes¶
Model Registry behavior changes:
The
apply
method is no longer included as a target method by default when logging an XGBoost model. If you need this method available in logged models, included it manually in thetarget-methods
option:log_model(..., options={"target_methods": ["apply", ...]})
New features¶
New model registry features:
The registry now supports logging sentence transformer models (
sentence_transformers.SentenceTransformer
).The
version_name
argument is no longer required when logging a model. A random human-readable ID is generated if none is provided.
Bug fixes¶
Model registry bug fixes:
Fix issue where, when multiple models are called in the same query, models after the first returned incorrect results. This fix is applied when models are logged and does not benefit existing models; you must log your models again to correct this behavior.
Modeling bug fixes:
Fix bug in registering a model where only methods mentioned in
save_model
were added to the model signature for Snowpark ML models.Fix bug in batch inference methods such as such as
predict
andpredict_log_probe
where, whenn_jobs
was not 1, the methods would not be executed.Fix bug in batch inference methods where they could not infer datatypes when the first row of data contained NULL.
The output column names from distributed hyperparameter optimization are now correctly matched with the Snowflake identifier.
Relaxed the versions of dependencies of distributed hyperparameter optimization methods; these were too strict and caused these methods to fail.
scikit-learn is now listed as a dependency of the LightGBM package.
Version 1.3.1 (2024-03-21)¶
New features¶
FileSet/FileSystem updates:
snowflake.ml.fileset.sfcfs.SFFileSystem
can now be used in UDFs and stored procedures.
Version 1.3.0 (2024-03-12)¶
Behavior changes¶
Model registry behavior changes:
As previously announced, the default for the
relax_version
option (in theoptions
argument oflog_model
) is nowTrue
, allowing more reliable deployment in most cases by permitting dependency versions available in Snowflake.When running model methods, value range based input validation (which prevents input from overflowing) is now optional. This should improve performance and should not lead to issues for most types of models. To enable validation, pass the named argument
strict_input_validation=True
when calling the model’srun
method.
Model development behavior changes:
The
fit_predict
method now returns either a pandas or a Snowpark DataFrame, depending on the type of the input data, and is available on all classes where it is available in the underlying scikit-learn, xgboost, or lightgbm class.
New features and updates¶
FileSet/FileSystem updates:
Instances of
snowflake.ml.fileset.sfcfs.SFFileSystem
can now be serialized withpickle
.
Bug fixes¶
Model registry bug fixes:
Fix a problem with importing
log_model
in some circumstances.Fix an incorrect error message when validating input Snowpark DataFrame with an array feature.
Model development bug fixes:
Relax package versions for all inference methods when the installed version of a dependency is not available in the Snowflake conda channel.
Version 1.2.3 (2024-02-26)¶
New features and updates¶
Model development updates:
All modeling classes now include a
score_samples
method to calculate the log-likelihood of the given samples.
Model registry updates:
Decimal type features are automatically cast (with a warning) to a DOUBLE or FLOAT instead of producing an error.
Improve error message for currently-unsupported
pip-requirements
option.You can now delete a version of a model.
Bug fixes¶
Model development fixes:
precision_recall_fscore_support
returned incorrect results withaverage="samples"
.
Model registry fixes:
Descriptions, models, and tags were not retrieved correctly in newly-created registries under the private preview model registry API due to a recent Snowflake behavior change.
Version 1.2.2 (2024-02-13)¶
New features and updates¶
Model registry updates:
You can now specify external access integrations when deploying a model to Snowpark Container Services using the private preview registry API, allowing models to access the internet to retrieve dependencies during deployment. The following endpoints are required for all deployments:
docker.com:80
docker.com:443
anaconda.com:80
anaconda.com:443
anaconda.org:80
anaconda.org:443
pypi.org:80
pypi.org:443
For models derived from
HuggingFacePipeLineModel
, the following endpoints are required.huggingface.com:80
huggingface.com:443
huggingface.co:80
huggingface.co:443
Version 1.2.1 (2024-01-25)¶
New features and updates¶
Model development updates:
Infer column data type for transformers when possible.
Model registry updates:
relax_version
option (inoptions
argument oflog_model
) relaxes dependencies of stated versions to allow newer minor versions when set toTrue
.
Version 1.2.0 (2024-01-12)¶
New features and updates¶
Public preview release of model registry. See Snowflake Model Registry (Snowpark ML Ops). The previous private preview release of the model registry has been deprecated, but will continue to be supported while it includes features not yet available in the public preview version.
Model development updates:
Added support for
fit_predict
method in AgglomerativeClustering, DBSCAN, and OPTICS classes.Added support for
fit_transform
method in MDS, SpectralEmbedding and TSNE class.