def node_should_be_modified(self, node):
"""Checks if the ast.Call node instantiates a framework estimator or model.
It doesn't specify the ``framework_version`` and ``py_version`` parameter,
as appropriate.
This looks for the following formats:
- ``TensorFlow``
- ``sagemaker.tensorflow.TensorFlow``
where "TensorFlow" can be Chainer, MXNet, PyTorch, SKLearn, or TensorFlow.
Args:
node (ast.Call): a node that represents a function call. For more,
see https://docs.python.org/3/library/ast.html#abstract-grammar.
Returns:
bool: If the ``ast.Call`` is instantiating a framework class that
should specify ``framework_version``, but doesn't.
"""
if matching.matches_any(node, ESTIMATORS) or matching.matches_any(
node, MODELS):
return _version_args_needed(node)
return False
def node_should_be_modified(self, node):
"""Checks if the ``ast.Call`` node instantiates a SerDe class.
This looks for the following calls (both with and without namespaces):
- ``sagemaker.predictor._CsvSerializer``
- ``sagemaker.predictor._JsonSerializer``
- ``sagemaker.predictor._NpySerializer``
- ``sagemaker.predictor._CsvDeserializer``
- ``sagemaker.predictor.BytesDeserializer``
- ``sagemaker.predictor.StringDeserializer``
- ``sagemaker.predictor.StreamDeserializer``
- ``sagemaker.predictor._NumpyDeserializer``
- ``sagemaker.predictor._JsonDeserializer``
- ``sagemaker.amazon.common.numpy_to_record_serializer``
- ``sagemaker.amazon.common.record_deserializer``
Args:
node (ast.Call): a node that represents a function call. For more,
see https://docs.python.org/3/library/ast.html#abstract-grammar.
Returns:
bool: If the ``ast.Call`` instantiates a SerDe class.
"""
return matching.matches_any(node, OLD_CLASS_NAME_TO_NAMESPACES)
def test_matches_any():
name_to_namespaces_dict = {
"KMeansPredictor": ("sagemaker", "sagemaker.amazon.kmeans"),
"Predictor": ("sagemaker.tensorflow.serving",),
}
matches = (
"KMeansPredictor()",
"sagemaker.KMeansPredictor()",
"sagemaker.amazon.kmeans.KMeansPredictor()",
"Predictor()",
"sagemaker.tensorflow.serving.Predictor()",
)
for call in matches:
assert matching.matches_any(ast_call(call), name_to_namespaces_dict)
non_matches = ("MXNet()", "sagemaker.mxnet.MXNet()")
for call in non_matches:
assert not matching.matches_any(ast_call(call), name_to_namespaces_dict)
def node_should_be_modified(self, node):
"""Checks if the node matches any of the relevant functions and
contains the parameter to be renamed.
Args:
node (ast.Call): a node that represents a function call. For more,
see https://docs.python.org/3/library/ast.html#abstract-grammar.
Returns:
bool: If the ``ast.Call`` matches the relevant function calls and
contains the parameter to be renamed.
"""
return matching.matches_any(node, self.calls_to_modify) and matching.has_arg(
node, self.old_param_name
)
def node_should_be_modified(self, node):
"""Checks if the ``ast.Call`` node instantiates a class of interest.
This looks for the following calls:
- ``sagemaker.<my>.<namespace>.<MyPredictor>``
- ``sagemaker.<namespace>.<MyPredictor>``
- ``<MyPredictor>``
Args:
node (ast.Call): a node that represents a function call. For more,
see https://docs.python.org/3/library/ast.html#abstract-grammar.
Returns:
bool: If the ``ast.Call`` instantiates a class of interest.
"""
return matching.matches_any(node, PREDICTORS)
def node_should_be_modified(self, node):
"""Function to check Airflow model config and if it contains positional arguments.
Checks if the ``ast.Call`` node creates an Airflow model config and
contains positional arguments. This looks for the following formats:
- ``model_config``
- ``airflow.model_config``
- ``workflow.airflow.model_config``
- ``sagemaker.workflow.airflow.model_config``
where ``model_config`` is either ``model_config`` or ``model_config_from_estimator``.
Args:
node (ast.Call): a node that represents a function call. For more,
see https://docs.python.org/3/library/ast.html#abstract-grammar.
Returns:
bool: If the ``ast.Call`` is either a ``model_config`` call or
a ``model_config_from_estimator`` call and has positional arguments.
"""
return matching.matches_any(node, FUNCTIONS) and len(node.args) > 0
def node_should_be_modified(self, node):
"""Checks if the node is an estimator constructor and contains any relevant parameters.
This looks for the following parameters:
- ``train_instance_count``
- ``train_instance_type``
- ``train_max_run``
- ``train_max_run_wait``
- ``train_use_spot_instances``
- ``train_volume_kms_key``
- ``train_volume_size``
Args:
node (ast.Call): a node that represents a function call. For more,
see https://docs.python.org/3/library/ast.html#abstract-grammar.
Returns:
bool: If the ``ast.Call`` matches the relevant function calls and
contains the parameter to be renamed.
"""
return matching.matches_any(node, ESTIMATORS) and self._has_train_parameter(node)
def node_should_be_modified(self, node):
"""Checks if the ``ast.Call`` node instantiates a TensorFlow Serving class.
This looks for the following calls:
- ``sagemaker.tensorflow.serving.Model``
- ``sagemaker.tensorflow.serving.Predictor``
- ``Predictor``
Because ``Model`` can refer to either ``sagemaker.tensorflow.serving.Model``
or :class:`~sagemaker.model.Model`, ``Model`` on its own is not sufficient
for indicating a TFS Model object.
Args:
node (ast.Call): a node that represents a function call. For more,
see https://docs.python.org/3/library/ast.html#abstract-grammar.
Returns:
bool: If the ``ast.Call`` instantiates a TensorFlow Serving class.
"""
if isinstance(node.func, ast.Name):
return node.func.id == "Predictor"
return matching.matches_any(node, TFS_CLASSES)