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GitOrigin-RevId: b8f053f94d5a20e5b28e275eac6c9f0cd1b10eee Co-authored-by: Snowflake Authors <[email protected]>
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.bazelrc

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# Make the target platform and the host platform the same
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build --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env
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test --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env --run_under='//bazel:test_wrapper'
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run --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env
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cquery --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env
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build --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env --repo_env=BUILD_CONDA_ENV=build
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test --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env --run_under='//bazel:test_wrapper' --repo_env=BUILD_CONDA_ENV=extended
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run --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env --repo_env=BUILD_CONDA_ENV=extended
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cquery --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env --repo_env=BUILD_CONDA_ENV=extended
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build:typecheck --aspects //bazel/mypy:mypy.bzl%mypy_aspect --output_groups=mypy
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run:pre_build --platforms //bazel/platforms:extended_conda_env --host_platform //bazel/platforms:extended_conda_env --repo_env=BUILD_CONDA_ENV=build
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build:typecheck --aspects //bazel/mypy:mypy.bzl%mypy_aspect --output_groups=mypy --repo_env=BUILD_CONDA_ENV=extended
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# Since integration tests are located in different packages than code under test,
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# the default instrumentation filter would exclude the code under test. This
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# makes bazel consider all the source code in our repo for coverage.
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coverage --instrumentation_filter="-//tests[/:]"
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test:sf_only --platforms //bazel/platforms:snowflake_conda_env --host_platform //bazel/platforms:snowflake_conda_env --run_under='//bazel:test_wrapper' --repo_env=BUILD_CONDA_ENV=sf_only
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run:sf_only --platforms //bazel/platforms:snowflake_conda_env --host_platform //bazel/platforms:snowflake_conda_env --repo_env=BUILD_CONDA_ENV=sf_only
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cquery:sf_only --platforms //bazel/platforms:snowflake_conda_env --host_platform //bazel/platforms:snowflake_conda_env --repo_env=BUILD_CONDA_ENV=sf_only

.gitignore

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lib-cov
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# Coverage directory used by tools like istanbul
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coverage
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coverage*.json
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*.lcov
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# nyc test coverage

CHANGELOG.md

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# Release History
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## 1.0.5
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### New Features
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- Model Registry: Added support save/load/deploy xgboost Booster model.
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- Model Registry: Added support to get the model name and the model version from model references.
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### Bug Fixes
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- Model Registry: Restore the db/schema back to the session after `create_model_registry()`.
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- 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.
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- connection_params.SnowflakeLoginOptions(): Added support for `private_key_path`.
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## 1.0.4
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### New Features
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- Model Registry: Added support save/load/deploy Tensorflow models (`tensorflow.Module`).
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- Model Registry: Added support save/load/deploy MLFlow PyFunc models (`mlflow.pyfunc.PyFuncModel`).
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- Model Development: Input dataframes can now be joined against data loaded from staged files.
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## 1.0.3 (2023-07-14)
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### Behavior Changes
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- 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.
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### New Features
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- Model Registry: Added support save/load/deploy PyTorch models (`torch.nn.Module` and `torch.jit.ScriptModule`).
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### Bug Fixes
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## 1.0.2 (2023-06-22)
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### Behavior Changes
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- Model Registry: Prohibit non-snowflake-native models from being logged.
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- Model Registry: `_use_local_snowml` parameter in options of `deploy()` has been removed.
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- 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.
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### New Features
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- 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.
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- 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.
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- Model Development: Added metrics:
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- Model Development: `accuracy_score()` now works when given label column names are lists of a single value.
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## 1.0.1 (2023-06-16)
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### Behavior Changes
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- Model Development: Changed Metrics APIs to imitate sklearn metrics modules:

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