Project import generated by Copybara. (#180)

GitOrigin-RevId: d499cbbe2f2564d706a7c12526cb11f9a0607297

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

Author: sfc-gh-anavalos

CHANGELOG.md

@@ -1,6 +1,17 @@
 # Release History
 
-## 1.14.0
+## 1.15.0
+
+### Bug Fixes
+
+### Behavior Changes
+
+* Registry: Dropping support for deprecated `conversational` task type for Huggingface models.
+  To read more <https://github.com/huggingface/transformers/pull/31165>
+
+### New Features
+
+## 1.14.0 (09-18-2025)
 
 ### Bug Fixes
 

CONTRIBUTING.md

@@ -446,9 +446,9 @@ test will fail if you forget that part.
 
 ## Integration test
 
-### Test in Store Procedure
+### Test in Stored Procedure
 
-To test if your code is working in store procedure or not simply, you could work based on `CommonTestBase` in
+To test if your code is working in stored procedure or not simply, you could work based on `CommonTestBase` in
 `tests/integ/snowflake/ml/test_utils/common_test_base.py`. An example of such test could be found in
 `tests/integ/snowflake/ml/_internal/file_utils_integ_test.py`.
 
@@ -463,32 +463,6 @@ To write a such test, you need to
 store procedure only rather than both locally and in store procedure, set `local=False`. If you don't want to test
 with caller's rights, set `test_callers_rights=False`. (Owner's rights store procedure is always tested)
 
-### Compatibility Test
-
-To test if your code is compatible with previous version simply, you could work based on `CommonTestBase` in
-`tests/integ/snowflake/ml/test_utils/common_test_base.py`.
-
-To write a such test, you need to
-
-1. Your test cannot have a parameter called `_snowml_pkg_ver`.
-1. Let your test case inherit from `common_test_base.CommonTestBase`.
-1. Remove all Snowpark Session creation in your test, and use `self.session` to access the session if needed.
-1. If you write your own `setUp` and `tearDown` method, remember to call `super().setUp()` or
-   `super().tearDown()`.
-1. Write a factory method in your test class that return a tuple of a function and its parameters as a tuple. The
-function will be run as a store procedure in the environment with previous version of library.
-
-    **Note**: Since the function will be created as a store procedure, the first argument must be a Snowpark Session.
-    The arguments tuple you provided via the factory method does not require to include the session object.
-
-    **Note**: To avoid any objects from current environment affecting the result, instead of using `cloudpickle` to
-    pickle the function, the function will be created as a Python file and registered as a store procedure. This means
-    you cannot use any object outside of the function, and if you want to import anything, you need to import inside
-    the function definition. So it would help if you make your prepare function as simple as possible.
-
-1. Decorate your test method with `common_test_base.CommonTestBase.compatibility_test`, providing the factory method
-you created in the above step, optional version range to test with, as well as additional package requirements.
-
 ## `pre-commit`
 
 Pull requests against the main branch are subject to `pre-commit` checks. Those checks enforce the code style.

README.md

@@ -1,18 +1,18 @@
-# Snowpark ML
+# Snowflake ML Python
 
-Snowpark ML is a set of tools including SDKs and underlying infrastructure to build and deploy machine learning models.
-With Snowpark ML, you can pre-process data, train, manage and deploy ML models all within Snowflake, using a single SDK,
+Snowflake ML Python is a set of tools including SDKs and underlying infrastructure to build and deploy machine learning models.
+With Snowflake ML Python, you can pre-process data, train, manage and deploy ML models all within Snowflake,
 and benefit from Snowflake’s proven performance, scalability, stability and governance at every stage of the Machine
 Learning workflow.
 
-## Key Components of Snowpark ML
+## Key Components of Snowflake ML Python
 
-The Snowpark ML Python SDK provides a number of APIs to support each stage of an end-to-end Machine Learning development
-and deployment process, and includes two key components.
+The Snowflake ML Python SDK provides a number of APIs to support each stage of an end-to-end Machine Learning development
+and deployment process.
 
-### Snowpark ML Development
+### Snowflake ML Model Development
 
-[Snowpark ML Development](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index#ml-modeling)
+[Snowflake ML Model Development](https://docs.snowflake.com/developer-guide/snowflake-ml/overview#ml-modeling)
 provides a collection of python APIs enabling efficient ML model development directly in Snowflake:
 
 1. Modeling API (`snowflake.ml.modeling`) for data preprocessing, feature engineering and model training in Snowflake.
@@ -23,19 +23,16 @@ model development classes based on sklearn, xgboost, and lightgbm.
 1. Framework Connectors: Optimized, secure and performant data provisioning for Pytorch and Tensorflow frameworks in
 their native data loader formats.
 
-1. FileSet API: FileSet provides a Python fsspec-compliant API for materializing data into a Snowflake internal stage
-from a query or Snowpark Dataframe along with a number of convenience APIs.
+### Snowflake ML Ops
 
-### Snowflake MLOps
+Snowflake ML Python contains a suite of MLOps tools. It complements
+the Snowflake Modeling API, and provides end to end development to deployment within Snowflake.
+The Snowflake ML Ops suite consists of:
 
-Snowflake MLOps contains suit of tools and objects to make ML development cycle. It complements
-the Snowpark ML Development API, and provides end to end development to deployment within Snowflake.
-Currently, the API consists of:
-
-1. [Registry](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index#snowflake-model-registry): A python API
+1. [Registry](https://docs.snowflake.com/developer-guide/snowflake-ml/overview#snowflake-model-registry): A python API
   allows secure deployment and management of models in Snowflake, supporting models trained both inside and outside of
   Snowflake.
-2. [Feature Store](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index#snowflake-feature-store): A fully
+2. [Feature Store](https://docs.snowflake.com/developer-guide/snowflake-ml/overview#snowflake-feature-store): A fully
   integrated solution for defining, managing, storing and discovering ML features derived from your data. The
   Snowflake Feature Store supports automated, incremental refresh from batch and streaming data sources, so that
   feature pipelines need be defined only once to be continuously updated with new data.
@@ -44,12 +41,19 @@ Currently, the API consists of:
 
 ## Getting started
 
+Learn about all Snowflake ML feature offerings in the [Developer Guide](https://docs.snowflake.com/developer-guide/snowflake-ml/overview).
+
 ### Have your Snowflake account ready
 
 If you don't have a Snowflake account yet, you can [sign up for a 30-day free trial account](https://signup.snowflake.com/).
 
 ### Installation
 
+Snowflake ML Python is pre-installed in Container Runtime notebook environments.
+[Learn more](https://docs.snowflake.com/en/developer-guide/snowflake-ml/notebooks-on-spcs).
+
+In Snowflake Warehouse notebook environments, snowflake-ml-python can be installed using the "Packages" drop-down menu.
+
 Follow the [installation instructions](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index#installing-snowpark-ml)
 in the Snowflake documentation.
 
@@ -59,8 +63,8 @@ or [virtualenv](https://docs.python.org/3/tutorial/venv.html) to create a virtua
 
 ### Conda channels
 
-The [Snowflake Conda Channel](https://repo.anaconda.com/pkgs/snowflake/) contains the official snowpark ML package releases.
-The recommended approach is to install `snowflake-ml-python` this conda channel:
+The [Snowflake Anaconda Channel](https://repo.anaconda.com/pkgs/snowflake/) contains the official snowflake-ml-python package
+releases. To install `snowflake-ml-python` from this conda channel:
 
 ```sh
 conda install \
@@ -69,25 +73,18 @@ conda install \
   snowflake-ml-python
 ```
 
-See [the developer guide](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index) for installation instructions.
-
-The latest version of the `snowpark-ml-python` package is also published in a conda channel in this repository. Package versions
-in this channel may not yet be present in the official Snowflake conda channel.
+See [the developer guide](https://docs.snowflake.com/en/developer-guide/snowpark-ml/index) for detailed installation instructions.
 
-Install `snowflake-ml-python` from this channel with the following (being sure to replace `<version_specifier>` with the
-desired version, e.g. `1.0.10`):
+The snowflake-ml-python package is also published in [conda-forge](https://anaconda.org/conda-forge/snowflake-ml-python).
+To install `snowflake-ml-python` from conda forge:
 
-```bash
+```sh
 conda install \
-  -c https://raw.githubusercontent.com/snowflakedb/snowflake-ml-python/conda/releases/  \
-  -c https://repo.anaconda.com/pkgs/snowflake \
+  -c https://conda.anaconda.org/conda-forge/ \
   --override-channels \
-  snowflake-ml-python==<version_specifier>
+  snowflake-ml-python
 ```
 
-Note that until a `snowflake-ml-python` package version is available in the official Snowflake conda channel, there may
-be compatibility issues. Server-side functionality that `snowflake-ml-python` depends on may not yet be released.
-
 ### Verifying the package
 
 1. Install cosign.

bazel/environments/conda-env-build-test.yml

@@ -6,6 +6,7 @@ channels:
   - https://repo.anaconda.com/pkgs/snowflake
   - nodefaults
 dependencies:
+  - absl-py==1.4.0
   - inflection==0.5.1
   - psutil==5.9.0
   - pytest-rerunfailures==12.0

ci/conda_recipe/meta.yaml

@@ -17,13 +17,12 @@ build:
   noarch: python
 package:
   name: snowflake-ml-python
-  version: 1.14.0
+  version: 1.15.0
 requirements:
   build:
     - python
     - bazel==6.3.2
   run:
-    - absl-py>=0.15,<2
     - aiohttp!=4.0.0a0, !=4.0.0a1
     - anyio>=3.5.0,<5
     - cachetools>=3.1.1,<6

requirements.yml

@@ -66,9 +66,9 @@
 
 - name: absl-py
   dev_version: 1.4.0
-  version_requirements: '>=0.15,<2'
   tags:
     - build_essential
+    - build_test_env
 # For fsspec[http] in conda
 - name_conda: aiohttp
   dev_version_conda: 3.9.5

snowflake/ml/_internal/platform_capabilities.py

@@ -1,8 +1,8 @@
 import json
+import logging
 from contextlib import contextmanager
 from typing import Any, Optional
 
-from absl import logging
 from packaging import version
 
 from snowflake.ml import version as snowml_version
@@ -13,6 +13,8 @@
     session as snowpark_session,
 )
 
+logger = logging.getLogger(__name__)
+
 LIVE_COMMIT_PARAMETER = "ENABLE_LIVE_VERSION_IN_SDK"
 INLINE_DEPLOYMENT_SPEC_PARAMETER = "ENABLE_INLINE_DEPLOYMENT_SPEC_FROM_CLIENT_VERSION"
 
@@ -60,12 +62,12 @@ def clear_mock_features(cls) -> None:
     @classmethod  # type: ignore[arg-type]
     @contextmanager
     def mock_features(cls, features: dict[str, Any] = _dummy_features) -> None:  # type: ignore[misc]
-        logging.debug(f"Setting mock features: {features}")
+        logger.debug(f"Setting mock features: {features}")
         cls.set_mock_features(features)
         try:
             yield
         finally:
-            logging.debug(f"Clearing mock features: {features}")
+            logger.debug(f"Clearing mock features: {features}")
             cls.clear_mock_features()
 
     def is_inlined_deployment_spec_enabled(self) -> bool:
@@ -98,7 +100,7 @@ def _get_features(session: snowpark_session.Session) -> dict[str, Any]:
                         error_code=error_codes.INTERNAL_SNOWML_ERROR, original_exception=RuntimeError(message)
                     )
         except snowpark_exceptions.SnowparkSQLException as e:
-            logging.debug(f"Failed to retrieve platform capabilities: {e}")
+            logger.debug(f"Failed to retrieve platform capabilities: {e}")
             # This can happen is server side is older than 9.2. That is fine.
         return {}
 
@@ -144,15 +146,15 @@ def _get_version_feature(self, feature_name: str) -> version.Version:
 
         value = self.features.get(feature_name)
         if value is None:
-            logging.debug(f"Feature {feature_name} not found, returning large version number")
+            logger.debug(f"Feature {feature_name} not found, returning large version number")
             return large_version
 
         try:
             # Convert to string if it's not already
             version_str = str(value)
             return version.Version(version_str)
         except (version.InvalidVersion, ValueError, TypeError) as e:
-            logging.debug(
+            logger.debug(
                 f"Failed to parse version from feature {feature_name} with value '{value}': {e}. "
                 f"Returning large version number"
             )
@@ -171,7 +173,7 @@ def _is_version_feature_enabled(self, feature_name: str) -> bool:
         feature_version = self._get_version_feature(feature_name)
 
         result = current_version >= feature_version
-        logging.debug(
+        logger.debug(
             f"Version comparison for feature {feature_name}: "
             f"current={current_version}, feature={feature_version}, enabled={result}"
         )

snowflake/ml/_internal/utils/connection_params.py

@@ -1,11 +1,13 @@
 import configparser
+import logging
 import os
 from typing import Optional, Union
 
-from absl import logging
 from cryptography.hazmat import backends
 from cryptography.hazmat.primitives import serialization
 
+logger = logging.getLogger(__name__)
+
 _DEFAULT_CONNECTION_FILE = "~/.snowsql/config"
 
 
@@ -106,7 +108,7 @@ def _load_from_snowsql_config_file(connection_name: str, login_file: str = "") -
     """Loads the dictionary from snowsql config file."""
     snowsql_config_file = login_file if login_file else os.path.expanduser(_DEFAULT_CONNECTION_FILE)
     if not os.path.exists(snowsql_config_file):
-        logging.error(f"Connection name given but snowsql config file is not found at: {snowsql_config_file}")
+        logger.error(f"Connection name given but snowsql config file is not found at: {snowsql_config_file}")
         raise Exception("Snowflake SnowSQL config not found.")
 
     config = configparser.ConfigParser(inline_comment_prefixes="#")
@@ -122,7 +124,7 @@ def _load_from_snowsql_config_file(connection_name: str, login_file: str = "") -
         # See https://docs.snowflake.com/en/user-guide/snowsql-start.html#configuring-default-connection-settings
         connection_name = "connections"
 
-    logging.info(f"Reading {snowsql_config_file} for connection parameters defined as {connection_name}")
+    logger.info(f"Reading {snowsql_config_file} for connection parameters defined as {connection_name}")
     config.read(snowsql_config_file)
     conn_params = dict(config[connection_name])
     # Remap names to appropriate args in Python Connector API

snowflake/ml/_internal/utils/jwt_generator.py

@@ -110,15 +110,16 @@ def get_token(self) -> str:
         }
 
         # Regenerate the actual token
-        token = jwt.encode(payload, key=self.private_key, algorithm=JWTGenerator.ALGORITHM)
+        token = jwt.encode(payload, key=self.private_key, algorithm=JWTGenerator.ALGORITHM)  # type: ignore[arg-type]
         # If you are using a version of PyJWT prior to 2.0, jwt.encode returns a byte string instead of a string.
         # If the token is a byte string, convert it to a string.
         if isinstance(token, bytes):
             token = token.decode("utf-8")
         self.token = token
+        public_key = self.private_key.public_key()
         logger.info(
             "Generated a JWT with the following payload: %s",
-            jwt.decode(self.token, key=self.private_key.public_key(), algorithms=[JWTGenerator.ALGORITHM]),
+            jwt.decode(self.token, key=public_key, algorithms=[JWTGenerator.ALGORITHM]),  # type: ignore[arg-type]
         )
 
         return token

snowflake/ml/_internal/utils/string_matcher.py

@@ -1,7 +1,7 @@
+import logging
 import re
 
 import sqlparse
-from absl.logging import logging
 
 from snowflake.ml._internal.utils.formatting import unwrap