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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""Configuration for the SageMaker Training Compiler."""
from __future__ import absolute_import
import logging
from sagemaker.workflow import is_pipeline_variable
logger = logging.getLogger(__name__)
class TrainingCompilerConfig(object):
"""The SageMaker Training Compiler configuration class."""
DEBUG_PATH = "/opt/ml/output/data/compiler/"
SUPPORTED_INSTANCE_CLASS_PREFIXES = ["p3", "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,
):
"""This class initializes a ``TrainingCompilerConfig`` instance.
`Amazon SageMaker Training Compiler
<https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_
is a feature of SageMaker Training
and speeds up training jobs by optimizing model execution graphs.
You can compile Hugging Face models
by passing the object of this configuration class to the ``compiler_config``
parameter of the :class:`~sagemaker.huggingface.HuggingFace`
estimator.
Args:
enabled (bool): Optional. Switch to enable SageMaker Training Compiler.
The default is ``True``.
debug (bool): Optional. Whether to dump detailed logs for debugging.
This comes with a potential performance slowdown.
The default is ``False``.
**Example**: The following code shows the basic usage of the
:class:`sagemaker.huggingface.TrainingCompilerConfig()` class
to run a HuggingFace training job with the compiler.
.. code-block:: python
from sagemaker.huggingface import HuggingFace, TrainingCompilerConfig
huggingface_estimator=HuggingFace(
...
compiler_config=TrainingCompilerConfig()
)
.. seealso::
For more information about how to enable SageMaker Training Compiler
for various training settings such as using TensorFlow-based models,
PyTorch-based models, and distributed training,
see `Enable SageMaker Training Compiler
<https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler-enable.html>`_
in the `Amazon SageMaker Training Compiler developer guide
<https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html>`_.
"""
self.enabled = enabled
self.debug = debug
self.disclaimers_and_warnings()
def __nonzero__(self):
"""Evaluates to 0 if SM Training Compiler is disabled."""
return self.enabled
def disclaimers_and_warnings(self):
"""Disclaimers and warnings.
Logs disclaimers and warnings about the
requested configuration of SageMaker Training Compiler.
"""
if self.enabled and self.debug:
logger.warning(
"Debugging is enabled."
"This will dump detailed logs from compilation to %s"
"This might impair training performance.",
self.DEBUG_PATH,
)
def _to_hyperparameter_dict(self):
"""Converts configuration object into hyperparameters.
Returns:
dict: A portion of the hyperparameters passed to the training job as a dictionary.
"""
compiler_config_hyperparameters = {
self.HP_ENABLE_COMPILER: self.enabled,
self.HP_ENABLE_DEBUG: self.debug,
}
return compiler_config_hyperparameters
@classmethod
def validate(
cls,
estimator,
):
"""Checks if SageMaker Training Compiler is configured correctly.
Args:
estimator (:class:`sagemaker.estimator.Estimator`): An estimator object.
When SageMaker Training Compiler is enabled, it validates if
the estimator is configured to be compatible with Training Compiler.
Raises:
ValueError: Raised if the requested configuration is not compatible
with SageMaker Training Compiler.
"""
if is_pipeline_variable(estimator.instance_type):
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."
)
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
]
):
error_helper_string = (
"Unsupported Instance class {}."
"SageMaker Training Compiler only supports {}"
)
error_helper_string = error_helper_string.format(
requested_instance_class, cls.SUPPORTED_INSTANCE_CLASS_PREFIXES
)
raise ValueError(error_helper_string)
elif estimator.instance_type == "local":
error_helper_string = (
"SageMaker Training Compiler doesn't support local mode."
"It only supports the following GPU instances: {}"
)
error_helper_string = error_helper_string.format(
cls.SUPPORTED_INSTANCE_CLASS_PREFIXES
)
raise ValueError(error_helper_string)
if estimator.distribution and "smdistributed" in estimator.distribution:
raise ValueError(
"SageMaker distributed training configuration is currently not compatible with "
"SageMaker Training Compiler."
)
if estimator.debugger_hook_config or (not estimator.disable_profiler):
helper_string = (
"Using Debugger and/or Profiler with SageMaker Training Compiler "
"might add recompilation overhead and degrade"
"performance. Found debugger_hook_config={} "
"disable_profiler={}. Please set "
"debugger_hook_config=None and disable_profiler=True for optimal "
"performance. For more information, see Training Compiler "
"Performance Considerations "
"(https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler-tips-pitfalls.html"
"#training-compiler-tips-pitfalls-considerations)."
)
helper_string = helper_string.format(
estimator.debugger_hook_config, estimator.disable_profiler
)
logger.warning(helper_string)
if estimator.instance_groups:
raise ValueError(
"SageMaker Training Compiler currently only supports homogeneous clusters of "
"the following GPU instance families: {}. Please use the 'instance_type' "
"and 'instance_count' parameters instead of 'instance_groups'".format(
cls.SUPPORTED_INSTANCE_CLASS_PREFIXES
)
)
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