doc: document that Local Mode + local code doesn't support dependencies arg

doc/overview.rst

@@ -752,6 +752,9 @@ If you want to keep everything local, and not use Amazon S3 either, you can enab
 
     # pass sagemaker_session to your estimator or model
 
+.. note::
+    If you enable "local code," then you cannot use the ``dependencies`` parameter in your estimator or model.
+
 We can take the example in  `Using Estimators <#using-estimators>`__ , and use either ``local`` or ``local_gpu`` as the instance type.
 
 .. code:: python

src/sagemaker/chainer/estimator.py

@@ -200,6 +200,7 @@ def create_model(
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
                 any additional libraries that will be exported to the container.
                 If not specified, the dependencies from training are used.
+                This is not supported with "local code" in Local Mode.
             **kwargs: Additional kwargs passed to the ChainerModel constructor.
 
         Returns:

src/sagemaker/estimator.py

@@ -1528,11 +1528,16 @@ def __init__(
                 copied to SageMaker in the same folder where the entrypoint is
                 copied. If 'git_config' is provided, 'dependencies' should be a
                 list of relative locations to directories with any additional
-                libraries needed in the Git repo. .. admonition:: Example
+                libraries needed in the Git repo.
 
-                    The following call >>> Estimator(entry_point='train.py',
-                    dependencies=['my/libs/common', 'virtual-env']) results in
-                    the following inside the container:
+                .. admonition:: Example
+
+                    The following call
+
+                    >>> Estimator(entry_point='train.py',
+                    ...           dependencies=['my/libs/common', 'virtual-env'])
+
+                    results in the following inside the container:
 
                     >>> $ ls
 
@@ -1541,6 +1546,7 @@ def __init__(
                     >>>     |------ common
                     >>>     |------ virtual-env
 
+                This is not supported with "local code" in Local Mode.
             enable_network_isolation (bool): Specifies whether container will
                 run in network isolation mode. Network isolation mode restricts
                 the container access to outside networks (such as the internet).

src/sagemaker/model.py

@@ -709,18 +709,25 @@ def __init__(
                 list of relative locations to directories with any additional
                 libraries needed in the Git repo. If the ```source_dir``` points
                 to S3, code will be uploaded and the S3 location will be used
-                instead. .. admonition:: Example
+                instead.
 
-                    The following call >>> Estimator(entry_point='inference.py',
-                    dependencies=['my/libs/common', 'virtual-env']) results in
-                    the following inside the container:
+                .. admonition:: Example
+
+                    The following call
+
+                    >>> Model(entry_point='inference.py',
+                    ...       dependencies=['my/libs/common', 'virtual-env'])
+
+                    results in the following inside the container:
 
                     >>> $ ls
 
                     >>> opt/ml/code
                     >>>     |------ inference.py
                     >>>     |------ common
                     >>>     |------ virtual-env
+
+                This is not supported with "local code" in Local Mode.
             git_config (dict[str, str]): Git configurations used for cloning
                 files, including ``repo``, ``branch``, ``commit``,
                 ``2FA_enabled``, ``username``, ``password`` and ``token``. The

src/sagemaker/mxnet/estimator.py

@@ -196,6 +196,7 @@ def create_model(
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
                 any additional libraries that will be exported to the container.
                 If not specified, the dependencies from training are used.
+                This is not supported with "local code" in Local Mode.
             image_name (str): If specified, the estimator will use this image for hosting, instead
                 of selecting the appropriate SageMaker official image based on framework_version
                 and py_version. It can be an ECR url or dockerhub image and tag.

src/sagemaker/pytorch/estimator.py

@@ -159,6 +159,7 @@ def create_model(
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
                 any additional libraries that will be exported to the container.
                 If not specified, the dependencies from training are used.
+                This is not supported with "local code" in Local Mode.
             **kwargs: Additional kwargs passed to the :class:`~sagemaker.pytorch.model.PyTorchModel`
                 constructor.
 

src/sagemaker/rl/estimator.py

@@ -199,6 +199,7 @@ def create_model(
                 folders will be copied to SageMaker in the same folder where the
                 entry_point is copied. If the ```source_dir``` points to S3,
                 code will be uploaded and the S3 location will be used instead.
+                This is not supported with "local code" in Local Mode.
             **kwargs: Additional kwargs passed to the :class:`~sagemaker.model.FrameworkModel`
                 constructor.
 

src/sagemaker/sklearn/estimator.py

@@ -179,6 +179,7 @@ def create_model(
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
                 any additional libraries that will be exported to the container.
                 If not specified, the dependencies from training are used.
+                This is not supported with "local code" in Local Mode.
             **kwargs: Additional kwargs passed to the :class:`~sagemaker.sklearn.model.SKLearnModel`
                 constructor.
 

src/sagemaker/tensorflow/estimator.py

@@ -582,6 +582,7 @@ def create_model(
                 If not specified and ``endpoint_type`` is 'tensorflow-serving', ``dependencies`` is
                 set to ``None``.
                 If ``endpoint_type`` is also ``None``, then the dependencies from training are used.
+                This is not supported with "local code" in Local Mode.
             **kwargs: Additional kwargs passed to :class:`~sagemaker.tensorflow.serving.Model`
                 and :class:`~sagemaker.tensorflow.model.TensorFlowModel` constructors.
 

src/sagemaker/xgboost/estimator.py

@@ -155,6 +155,7 @@ def create_model(
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
                 any additional libraries that will be exported to the container.
                 If not specified, the dependencies from training are used.
+                This is not supported with "local code" in Local Mode.
             **kwargs: Additional kwargs passed to the :class:`~sagemaker.xgboost.model.XGBoostModel`
                 constructor.