2.0.0

2.0.0

Release candidate for the snowflake-ml-python 2.0 major release. This is a breaking
release that removes deprecated APIs and slims down the default install footprint.

New Features

• Feature Store: added alter_online_service(size=...), which changes the size of an existing Online Service. The
  call returns as soon as the request is recorded; the change runs in the background and can take hours on a large
  Online Service, which keeps serving online reads and stream ingestion throughout. Poll
  get_online_service_status() until status is RUNNING at the requested size.

Bug Fixes

• Feature Store: delete_feature_view now drops the online feature table before the
  offline dynamic table or view, so a dependent FeatureGroup (error 099940) does not
  leave an orphaned offline object.

Behavior Changes

• Registry: Models logged from a Snowflake Container Runtime notebook without an explicit
  target_platforms now default to both Warehouse and Snowpark Container Services instead of
  Snowpark Container Services only. Warehouse is no longer dropped from the default target
  platforms.
• Dependencies: scikit-learn, xgboost, and shap are no longer required dependencies of
  snowflake-ml-python. They are now optional extras. Install them with
  pip install snowflake-ml-python[scikit-learn], [xgboost], [shap], or [all] when using
  the corresponding functionality. Extras are independent: [xgboost] and [lightgbm] install
  only those libraries, not scikit-learn. Native XGBoost or LightGBM models (for example
  logging to the Model Registry) only need [xgboost] or [lightgbm].
  snowflake.ml.modeling estimators always require scikit-learn, so the XGBoost and LightGBM
  wrappers need pip install "snowflake-ml-python[xgboost,scikit-learn]" or
  "snowflake-ml-python[lightgbm,scikit-learn]".
• Feature Store: Added an optional feature_store extra. Install snowflake-ml-python[feature_store]
  to pull in httpx, which is required for online feature serving reads. httpx is not installed by
  the default snowflake-ml-python install. Conda users still need to install httpx separately
  (for example conda install httpx) to use the online Feature Store.
• Modeling: Importing a snowflake.ml.modeling subpackage (for example
  snowflake.ml.modeling.linear_model, snowflake.ml.modeling.xgboost,
  snowflake.ml.modeling.lightgbm, or snowflake.ml.modeling.preprocessing) without the optional
  dependencies it needs now raises an actionable ImportError that names only the missing
  package(s), instead of a confusing missing-attribute error.
  scikit-learn and xgboost are attributed to the 2.0 dependency change; lightgbm was
  already an optional extra before 2.0.

Deprecations

• Modeling: The snowflake.ml.modeling estimators and preprocessing transformers are deprecated and
  will be removed in a future release. Importing a snowflake.ml.modeling subpackage now emits a
  DeprecationWarning. Train models with the native scikit-learn, XGBoost, or LightGBM estimators
  and log them to the Snowflake Model Registry (snowflake.ml.registry) instead.

Breaking Changes

• Generic: Require python >= 3.10. Python 3.9 is no longer supported.
• Removed the deprecated snowflake.ml.utils.connection_params.SnowflakeLoginOptions public API
  (deprecated since 1.8.5).
• Registry: ModelVersion.run_batch now runs on EXECUTE INFERENCE JOB SERVICE. The single
  job_spec argument is replaced by the resources_spec, inference_spec, and image_build_spec
  blocks, and input may be supplied as either a DataFrame (X) or an existing stage path
  (input_stage_location). output_spec.stage_location is treated as a base: results are written
  under a per-job subdirectory, <stage_location>/<job_name>/. output_spec.base_stage_location,
  job_spec.job_name_prefix, and job_spec.block no longer exist.
• Registry: Removed the snowflake.ml.model.batch module. Use snowflake.ml.model.batch_inference,
  which exports BatchInferenceTask alongside the input, output, resources, inference, and image
  build spec classes.
• Feature Store: Feature views created before 1.42.0 that use the last_distinct_n or
  first_distinct_n aggregation function can no longer be read offline. generate_dataset,
  generate_training_set, retrieve_feature_values, and read_feature_view now raise a
  ValueError for such feature views. Re-create the feature view to restore offline reads.
• Dependencies: snowflake-snowpark-python must now be >=1.37.0. Earlier releases import
  pkg_resources, which setuptools removed in 82.0.0, so they fail to import in environments
  with a current setuptools.