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feature: SageMaker Training Compiler does not support p4de instances #3478

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Merged
merged 1 commit into from
Dec 2, 2022
Merged

feature: SageMaker Training Compiler does not support p4de instances #3478

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7 changes: 2 additions & 5 deletions src/sagemaker/huggingface/training_compiler/config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@
class TrainingCompilerConfig(BaseConfig):
"""The SageMaker Training Compiler configuration class."""

SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "g4dn", "p4d", "g5"]
SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "p3dn", "g4dn", "p4d", "g5"]
SUPPORTED_INSTANCE_TYPES_WITH_EFA = [
"ml.g4dn.8xlarge",
"ml.g4dn.12xlarge",
Expand Down Expand Up @@ -87,10 +87,7 @@ def __init__(
super(TrainingCompilerConfig, self).__init__(enabled=enabled, debug=debug)

@classmethod
def validate(
cls,
estimator,
):
def validate(cls, estimator):
"""Checks if SageMaker Training Compiler is configured correctly.

Args:
Expand Down
13 changes: 3 additions & 10 deletions src/sagemaker/tensorflow/training_compiler/config.py
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Expand Up @@ -24,14 +24,10 @@
class TrainingCompilerConfig(BaseConfig):
"""The SageMaker Training Compiler configuration class."""

SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "g4dn", "p4", "g5"]
SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "p3dn", "g4dn", "p4d", "g5"]
MIN_SUPPORTED_VERSION = "2.9"

def __init__(
self,
enabled=True,
debug=False,
):
def __init__(self, enabled=True, debug=False):
"""This class initializes a ``TrainingCompilerConfig`` instance.

`Amazon SageMaker Training Compiler
Expand Down Expand Up @@ -79,10 +75,7 @@ def __init__(
super(TrainingCompilerConfig, self).__init__(enabled=enabled, debug=debug)

@classmethod
def validate(
cls,
estimator,
):
def validate(cls, estimator):
"""Checks if SageMaker Training Compiler is configured correctly.

Args:
Expand Down
26 changes: 10 additions & 16 deletions src/sagemaker/training_compiler/config.py
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Expand Up @@ -23,16 +23,12 @@ class TrainingCompilerConfig(object):
"""The SageMaker Training Compiler configuration class."""

DEBUG_PATH = "/opt/ml/output/data/compiler/"
SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "g4dn", "p4d", "g5"]
SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "p3dn", "g4dn", "p4d", "g5"]

HP_ENABLE_COMPILER = "sagemaker_training_compiler_enabled"
HP_ENABLE_DEBUG = "sagemaker_training_compiler_debug_mode"

def __init__(
self,
enabled=True,
debug=False,
):
def __init__(self, enabled=True, debug=False):
"""This class initializes a ``TrainingCompilerConfig`` instance.

`Amazon SageMaker Training Compiler
Expand Down Expand Up @@ -118,10 +114,7 @@ def _to_hyperparameter_dict(self):
return compiler_config_hyperparameters

@classmethod
def validate(
cls,
estimator,
):
def validate(cls, estimator):
"""Checks if SageMaker Training Compiler is configured correctly.

Args:
Expand All @@ -138,19 +131,20 @@ def validate(
warn_msg = (
"Estimator instance_type is a PipelineVariable (%s), "
"which has to be interpreted as one of the "
"[p3, g4dn, p4d, g5] classes in execution time."
"%s classes in execution time."
)
logger.warning(
warn_msg,
type(estimator.instance_type),
str(cls.SUPPORTED_INSTANCE_CLASS_PREFIXES).replace(",", ""),
)
logger.warning(warn_msg, type(estimator.instance_type))
elif estimator.instance_type:
if "local" not in estimator.instance_type:
requested_instance_class = estimator.instance_type.split(".")[
1
] # Expecting ml.class.size
if not any(
[
requested_instance_class.startswith(i)
for i in cls.SUPPORTED_INSTANCE_CLASS_PREFIXES
]
[requested_instance_class == i for i in cls.SUPPORTED_INSTANCE_CLASS_PREFIXES]
):
error_helper_string = (
"Unsupported Instance class {}."
Expand Down