SageMaker 훈련 컴파일러를 이용한 PyTorch 훈련 작업 실행

PyTorch 모델을 컴파일하고 훈련시키려면 다음 코드 예시와 같이 SageMaker Training Compiler로 SageMaker AI PyTorch 예측기를 구성하세요.

from sagemaker.pytorch import PyTorch, TrainingCompilerConfig

# the original max batch size that can fit into GPU memory without compiler
batch_size_native=12
learning_rate_native=float('5e-5')

# an updated max batch size that can fit into GPU memory with compiler
batch_size=64

# update learning rate
learning_rate=learning_rate_native/batch_size_native*batch_size

hyperparameters={
    "n_gpus": 1,
    "batch_size": batch_size,
    "learning_rate": learning_rate
}

pytorch_estimator=PyTorch(
    entry_point='train.py',
    source_dir='path-to-requirements-file', # Optional. Add this if need to install additional packages.
    instance_count=1,
    instance_type='ml.p3.2xlarge',
    framework_version='1.13.1',
    py_version='py3',
    hyperparameters=hyperparameters,
    compiler_config=TrainingCompilerConfig(),
    disable_profiler=True,
    debugger_hook_config=False
)

pytorch_estimator.fit()

PyTorch로 트랜스포머 모델을 컴파일하고 훈련시키려면 다음 코드 예시와 같이 SageMaker Training Compiler로 SageMaker AI Hugging Face 예측기를 구성하세요.

from sagemaker.huggingface import HuggingFace, TrainingCompilerConfig

# the original max batch size that can fit into GPU memory without compiler
batch_size_native=12
learning_rate_native=float('5e-5')

# an updated max batch size that can fit into GPU memory with compiler
batch_size=64

# update learning rate
learning_rate=learning_rate_native/batch_size_native*batch_size

hyperparameters={
    "n_gpus": 1,
    "batch_size": batch_size,
    "learning_rate": learning_rate
}

pytorch_huggingface_estimator=HuggingFace(
    entry_point='train.py',
    instance_count=1,
    instance_type='ml.p3.2xlarge',
    transformers_version='4.21.1',
    pytorch_version='1.11.0',
    hyperparameters=hyperparameters,
    compiler_config=TrainingCompilerConfig(),
    disable_profiler=True,
    debugger_hook_config=False
)

pytorch_huggingface_estimator.fit()

훈련 스크립트를 준비하려면 다음 페이지를 참조하세요.

예제 전체를 확인하려면 다음 노트북을 참조하세요.

PyTorch v1.12에서는 SageMaker AI PyTorch 예측기 클래스의 distribution 파라미터에 지정된 pytorch_xla 옵션을 추가하여 SageMaker Training Compiler로 분산 훈련을 실행할 수 있습니다.

from sagemaker.pytorch import PyTorch, TrainingCompilerConfig

# choose an instance type, specify the number of instances you want to use,
# and set the num_gpus variable the number of GPUs per instance.
instance_count=1
instance_type='ml.p3.8xlarge'
num_gpus=4

# the original max batch size that can fit to GPU memory without compiler
batch_size_native=16
learning_rate_native=float('5e-5')

# an updated max batch size that can fit to GPU memory with compiler
batch_size=26

# update learning rate
learning_rate=learning_rate_native/batch_size_native*batch_size*num_gpus*instance_count

hyperparameters={
    "n_gpus": num_gpus,
    "batch_size": batch_size,
    "learning_rate": learning_rate
}

pytorch_estimator=PyTorch(
    entry_point='your_training_script.py',
    source_dir='path-to-requirements-file', # Optional. Add this if need to install additional packages.
    instance_count=instance_count,
    instance_type=instance_type,
    framework_version='1.13.1',
    py_version='py3',
    hyperparameters=hyperparameters,
    compiler_config=TrainingCompilerConfig(),
    distribution ={'pytorchxla' : { 'enabled': True }},
    disable_profiler=True,
    debugger_hook_config=False
)

pytorch_estimator.fit()

PyTorch v1.11 이상에서는 distribution 파라미터에 지정된 pytorch_xla 옵션으로 SageMaker Training Compiler를 분산 훈련에 사용할 수 있습니다.

from sagemaker.huggingface import HuggingFace, TrainingCompilerConfig

# choose an instance type, specify the number of instances you want to use,
# and set the num_gpus variable the number of GPUs per instance.
instance_count=1
instance_type='ml.p3.8xlarge'
num_gpus=4

# the original max batch size that can fit to GPU memory without compiler
batch_size_native=16
learning_rate_native=float('5e-5')

# an updated max batch size that can fit to GPU memory with compiler
batch_size=26

# update learning rate
learning_rate=learning_rate_native/batch_size_native*batch_size*num_gpus*instance_count

hyperparameters={
    "n_gpus": num_gpus,
    "batch_size": batch_size,
    "learning_rate": learning_rate
}

pytorch_huggingface_estimator=HuggingFace(
    entry_point='your_training_script.py',
    instance_count=instance_count,
    instance_type=instance_type,
    transformers_version='4.21.1',
    pytorch_version='1.11.0',
    hyperparameters=hyperparameters,
    compiler_config=TrainingCompilerConfig(),
    distribution ={'pytorchxla' : { 'enabled': True }},
    disable_profiler=True,
    debugger_hook_config=False
)

pytorch_huggingface_estimator.fit()

지원되는 PyTorch v1.10.2 이하 버전의 경우, SageMaker Training Compiler에는 분산 훈련 작업을 시작하기 위한 대체 메커니즘이 필요합니다. 분산 훈련을 실행하려면 SageMaker Training Compiler로 SageMaker AI 분산 훈련 런처 스크립트를 entry_point 인수에 전달하고, 훈련 스크립트를 hyperparameters 인수에 전달해야 합니다. 다음 코드 예시는 필수 변경 사항을 적용하여 SageMaker AI Hugging Face 예측기를 구성하는 방법을 보여줍니다.

from sagemaker.huggingface import HuggingFace, TrainingCompilerConfig

# choose an instance type, specify the number of instances you want to use,
# and set the num_gpus variable the number of GPUs per instance.
instance_count=1
instance_type='ml.p3.8xlarge'
num_gpus=4

# the original max batch size that can fit to GPU memory without compiler
batch_size_native=16
learning_rate_native=float('5e-5')

# an updated max batch size that can fit to GPU memory with compiler
batch_size=26

# update learning rate
learning_rate=learning_rate_native/batch_size_native*batch_size*num_gpus*instance_count

training_script="your_training_script.py"

hyperparameters={
    "n_gpus": num_gpus,
    "batch_size": batch_size,
    "learning_rate": learning_rate,
    "training_script": training_script     # Specify the file name of your training script.
}

pytorch_huggingface_estimator=HuggingFace(
    entry_point='distributed_training_launcher.py',    # Specify the distributed training launcher script.
    instance_count=instance_count,
    instance_type=instance_type,
    transformers_version='4.17.0',
    pytorch_version='1.10.2',
    hyperparameters=hyperparameters,
    compiler_config=TrainingCompilerConfig(),
    disable_profiler=True,
    debugger_hook_config=False
)

pytorch_huggingface_estimator.fit()

런처 스크립트는 다음과 같아야 합니다. 이 스크립트에서는 훈련 스크립트를 래핑하고, 선택된 훈련 인스턴스의 크기에 따라 분산 훈련 환경을 구성합니다.

# distributed_training_launcher.py

#!/bin/python

import subprocess
import sys

if __name__ == "__main__":
    arguments_command = " ".join([arg for arg in sys.argv[1:]])
    """
    The following line takes care of setting up an inter-node communication
    as well as managing intra-node workers for each GPU.
    """
    subprocess.check_call("python -m torch_xla.distributed.sm_dist " + arguments_command, shell=True)