From 29ad1b971fe49cdc9796714fe7e77922c2764bcf Mon Sep 17 00:00:00 2001 From: Harutaka Kawamura Date: Thu, 17 Oct 2024 12:50:43 +0900 Subject: [PATCH] Fix `mlflow.pyfunc.save_model` docstring (#13446) Signed-off-by: harupy <17039389+harupy@users.noreply.github.com> --- mlflow/pyfunc/__init__.py | 7 +------ 1 file changed, 1 insertion(+), 6 deletions(-) diff --git a/mlflow/pyfunc/__init__.py b/mlflow/pyfunc/__init__.py index c2071079cd1518..d8c148a529352f 100644 --- a/mlflow/pyfunc/__init__.py +++ b/mlflow/pyfunc/__init__.py @@ -2251,9 +2251,6 @@ def save_model( **kwargs, ): """ - save_model(path, loader_module=None, data_path=None, code_path=None, conda_env=None,\ - mlflow_model=Model(), python_model=None, artifacts=None) - Save a Pyfunc model with custom inference logic and optional data dependencies to a path on the local filesystem. @@ -2386,9 +2383,7 @@ def predict(self, context, model_input: List[str], params=None) -> List[str]: and :func:`PythonModel.predict() `. For example, consider the following ``artifacts`` dictionary:: - { - "my_file": "s3://my-bucket/path/to/my/file" - } + {"my_file": "s3://my-bucket/path/to/my/file"} In this case, the ``"my_file"`` artifact is downloaded from S3. The ``python_model`` can then refer to ``"my_file"`` as an absolute filesystem