doc: clarify that entry_point must be in the root of source_dir (if applicable)

Author: laurenyu

src/sagemaker/chainer/estimator.py

@@ -76,7 +76,8 @@ def __init__(
         Args:
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to training.
-                This should be compatible with either Python 2.7 or Python 3.5.
+                If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             use_mpi (bool): If true, entry point is run as an MPI script. By
                 default, the Chainer Framework runs the entry point with
                 'mpirun' if more than one instance is used.
@@ -186,11 +187,14 @@ def create_model(
                 role from the Estimator will be used.
             vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on
                 the model. Default: use subnets and security groups from this Estimator.
+
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+
             entry_point (str): Path (absolute or relative) to the local Python source file which
-                should be executed as the entry point to training. If not specified, the training
-                entry point is used.
+                should be executed as the entry point to training. If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
+                If not specified, the training entry point is used.
             source_dir (str): Path (absolute or relative) to a directory with any other serving
                 source code dependencies aside from the entry point file.
                 If not specified, the model source directory from training is used.

src/sagemaker/chainer/model.py

@@ -85,8 +85,8 @@ def __init__(
                 might use the IAM role, if it needs to access an AWS resource.
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to model
-                hosting. This should be compatible with either Python 2.7 or
-                Python 3.5.
+                hosting. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             image (str): A Docker image URI (default: None). If not specified, a
                 default image for Chainer will be used.
             py_version (str): Python version you want to use for executing your

src/sagemaker/estimator.py

@@ -1467,10 +1467,11 @@ def __init__(
         Args:
             entry_point (str): Path (absolute or relative) to the local Python
                 source file which should be executed as the entry point to
-                training. This should be compatible with either Python 2.7 or
-                Python 3.5. If 'git_config' is provided, 'entry_point' should be
+                training. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
+                If 'git_config' is provided, 'entry_point' should be
                 a relative location to the Python source file in the Git repo.
-                Example
+                Example:
 
                     With the following GitHub repo directory structure:
 
@@ -1487,6 +1488,7 @@ def __init__(
                 when training on Amazon SageMaker. If 'git_config' is provided,
                 'source_dir' should be a relative location to a directory in the Git
                 repo.
+
                 .. admonition:: Example
 
                     With the following GitHub repo directory structure:
@@ -1968,8 +1970,9 @@ def transformer(
             volume_kms_key (str): Optional. KMS key ID for encrypting the volume
                 attached to the ML compute instance (default: None).
             entry_point (str): Path (absolute or relative) to the local Python source file which
-                should be executed as the entry point to training. If not specified, the training
-                entry point is used.
+                should be executed as the entry point to training. If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
+                If not specified, the training entry point is used.
             vpc_config_override (dict[str, list[str]]): Optional override for
                 the VpcConfig set on the model.
                 Default: use subnets and security groups from this Estimator.

src/sagemaker/model.py

@@ -648,10 +648,11 @@ def __init__(
                 resources on your behalf.
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to model
-                hosting. This should be compatible with either Python 2.7 or
-                Python 3.5. If 'git_config' is provided, 'entry_point' should be
+                hosting. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
+                If 'git_config' is provided, 'entry_point' should be
                 a relative location to the Python source file in the Git repo.
-                Example
+                Example:
 
                     With the following GitHub repo directory structure:
 

src/sagemaker/mxnet/estimator.py

@@ -71,7 +71,8 @@ def __init__(
         Args:
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to training.
-                This should be compatible with either Python 2.7 or Python 3.5.
+                If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             source_dir (str): Path (absolute, relative or an S3 URI) to a directory
                 with any other training source code dependencies aside from the entry
                 point file (default: None). If ``source_dir`` is an S3 URI, it must
@@ -186,8 +187,9 @@ def create_model(
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
 
             entry_point (str): Path (absolute or relative) to the local Python source file which
-                should be executed as the entry point to training. If not specified, the training
-                entry point is used.
+                should be executed as the entry point to training. If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
+                If not specified, the training entry point is used.
             source_dir (str): Path (absolute or relative) to a directory with any other serving
                 source code dependencies aside from the entry point file.
                 If not specified, the model source directory from training is used.

src/sagemaker/mxnet/model.py

@@ -84,8 +84,8 @@ def __init__(
                 might use the IAM role, if it needs to access an AWS resource.
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to model
-                hosting. This should be compatible with either Python 2.7 or
-                Python 3.5.
+                hosting. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             image (str): A Docker image URI (default: None). If not specified, a
                 default image for MXNet will be used.
             py_version (str): Python version you want to use for executing your

src/sagemaker/pytorch/estimator.py

@@ -67,7 +67,8 @@ def __init__(
         Args:
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to training.
-                This should be compatible with either Python 2.7 or Python 3.5.
+                If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             source_dir (str): Path (absolute, relative or an S3 URI) to a directory
                 with any other training source code dependencies aside from the entry
                 point file (default: None). If ``source_dir`` is an S3 URI, it must
@@ -150,8 +151,9 @@ def create_model(
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
             entry_point (str): Path (absolute or relative) to the local Python source file which
-                should be executed as the entry point to training. If not specified, the training
-                entry point is used.
+                should be executed as the entry point to training. If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
+                If not specified, the training entry point is used.
             source_dir (str): Path (absolute or relative) to a directory with any other serving
                 source code dependencies aside from the entry point file.
                 If not specified, the model source directory from training is used.

src/sagemaker/pytorch/model.py

@@ -85,8 +85,8 @@ def __init__(
                 might use the IAM role, if it needs to access an AWS resource.
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to model
-                hosting. This should be compatible with either Python 2.7 or
-                Python 3.5.
+                hosting. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             image (str): A Docker image URI (default: None). If not specified, a
                 default image for PyTorch will be used.
             py_version (str): Python version you want to use for executing your

src/sagemaker/rl/estimator.py

@@ -100,8 +100,8 @@ def __init__(
         Args:
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to training.
-                This should be compatible with Python 3.5 for MXNet or Python
-                3.6 for TensorFlow.
+                If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             toolkit (sagemaker.rl.RLToolkit): RL toolkit you want to use for
                 executing your model training code.
             toolkit_version (str): RL toolkit version you want to be use for
@@ -187,8 +187,8 @@ def create_model(
 
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point for MXNet
-                hosting. This should be compatible with Python 3.5 (default:
-                self.entry_point)
+                hosting (default: self.entry_point). If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
             source_dir (str): Path (absolute or relative) to a directory with
                 any other training source code dependencies aside from the entry
                 point file (default: self.source_dir). Structure within this

src/sagemaker/sklearn/estimator.py

@@ -65,7 +65,8 @@ def __init__(
         Args:
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to training.
-                This should be compatible with either Python 2.7 or Python 3.5.
+                If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             framework_version (str): Scikit-learn version you want to use for
                 executing your model training code. List of supported versions
                 https://github.com/aws/sagemaker-python-sdk#sklearn-sagemaker-estimators

src/sagemaker/sklearn/model.py

@@ -81,12 +81,12 @@ def __init__(
                 might use the IAM role, if it needs to access an AWS resource.
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to model
-                hosting. This should be compatible with either Python 2.7 or
-                Python 3.5.
+                hosting. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             image (str): A Docker image URI (default: None). If not specified, a
                 default image for Scikit-learn will be used.
             py_version (str): Python version you want to use for executing your
-                model training code (default: 'py2').
+                model training code (default: 'py3').
             framework_version (str): Scikit-learn version you want to use for
                 executing your model training code.
             predictor_cls (callable[str, sagemaker.session.Session]): A function

src/sagemaker/tensorflow/estimator.py

@@ -558,19 +558,22 @@ def create_model(
             model_server_workers (int): Optional. The number of worker processes used by the
                 inference server. If None, server will use one worker per vCPU.
             vpc_config_override (dict[str, list[str]]): Optional override for VpcConfig set on the
-                model.
-                Default: use subnets and security groups from this Estimator.
+                model. Default: use subnets and security groups from this Estimator.
+
                 * 'Subnets' (list[str]): List of subnet ids.
                 * 'SecurityGroupIds' (list[str]): List of security group ids.
+
             endpoint_type (str): Optional. Selects the software stack used by the inference server.
                 If  not specified, the model will be configured to use the default
                 SageMaker model server. If 'tensorflow-serving', the model will be configured to
                 use the SageMaker Tensorflow Serving container.
             entry_point (str): Path (absolute or relative) to the local Python source file which
-                should be executed as the entry point to training. If not specified and
-                ``endpoint_type`` is 'tensorflow-serving', no entry point is used. If
-                ``endpoint_type`` is also ``None``, then the training entry point is used.
-            source_dir (str): Path (absolute or relative or an S3 URI ) to a directory with any
+                should be executed as the entry point to training. If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
+                If not specified and ``endpoint_type`` is 'tensorflow-serving',
+                no entry point is used. If ``endpoint_type`` is also ``None``,
+                then the training entry point is used.
+            source_dir (str): Path (absolute or relative or an S3 URI) to a directory with any
                 other serving source code dependencies aside from the entry point file. If
                 ``source_dir`` is an S3 URI, it must point to a tar.gz file. If not specified
                 and ``endpoint_type`` is 'tensorflow-serving', no source_dir is used. If
@@ -828,9 +831,11 @@ def transformer(
                 If 'tensorflow-serving', the model will be configured to
                 use the SageMaker Tensorflow Serving container.
             entry_point (str): Path (absolute or relative) to the local Python source file which
-                should be executed as the entry point to training. If not specified and
-                ``endpoint_type`` is 'tensorflow-serving', no entry point is used. If
-                ``endpoint_type`` is also ``None``, then the training entry point is used.
+                should be executed as the entry point to training. If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
+                If not specified and ``endpoint_type`` is 'tensorflow-serving',
+                no entry point is used. If ``endpoint_type`` is also ``None``,
+                then the training entry point is used.
             vpc_config_override (dict[str, list[str]]): Optional override for
                 the VpcConfig set on the model.
                 Default: use subnets and security groups from this Estimator.

src/sagemaker/tensorflow/model.py

@@ -82,8 +82,8 @@ def __init__(
                 might use the IAM role, if it needs to access an AWS resource.
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to model
-                hosting. This should be compatible with either Python 2.7 or
-                Python 3.5.
+                hosting. If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
             image (str): A Docker image URI (default: None). If not specified, a
                 default image for TensorFlow will be used.
             py_version (str): Python version you want to use for executing your

src/sagemaker/xgboost/estimator.py

@@ -70,8 +70,8 @@ def __init__(
 
         Args:
             entry_point (str): Path (absolute or relative) to the Python source file which should
-                be executed as the entry point to training.
-                This should be compatible with either Python 2.7 or Python 3.5.
+                be executed as the entry point to training.  If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
             framework_version (str): XGBoost version you want to use for executing your model
                 training code.  List of supported versions
                 https://github.com/aws/sagemaker-python-sdk#xgboost-sagemaker-estimators

src/sagemaker/xgboost/model.py

@@ -74,8 +74,8 @@ def __init__(
                 training data and model artifacts. After the endpoint is created, the inference
                 code might use the IAM role, if it needs to access an AWS resource.
             entry_point (str): Path (absolute or relative) to the Python source file which should
-                be executed  as the entry point to model hosting.
-                This should be compatible with either Python 2.7 or Python 3.5.
+                be executed  as the entry point to model hosting. If ``source_dir`` is specified,
+                then ``entry_point`` must point to a file located at the root of ``source_dir``.
             image (str): A Docker image URI (default: None). If not specified, a default image for
                 XGBoos will be used.
             py_version (str): Python version you want to use for executing your model training code