PyTorch Trainingsjobs mit dem SageMaker Training Compiler ausführen

Um ein PyTorch Modell zu kompilieren und zu trainieren, konfigurieren Sie einen SageMaker PyTorch AI-Schätzer mit SageMaker Training Compiler, wie im folgenden Codebeispiel gezeigt.

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()

Um ein Transformer-Modell mit zu kompilieren und zu trainieren PyTorch, konfigurieren Sie einen SageMaker AI Hugging Face Estimator mit SageMaker Training Compiler, wie im folgenden Codebeispiel gezeigt.

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()

Informationen zum Erstellen Ihres Trainingsskripts finden Sie auf den folgenden Seiten.

end-to-endBeispiele finden Sie in den folgenden Notizbüchern:

In PyTorch Version 1.12 können Sie verteiltes Training mit dem SageMaker Training Compiler ausführen, indem Sie die angegebene pytorch_xla Option dem distribution Parameter der SageMaker AI-Estimator-Klasse hinzufügen. PyTorch

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()

Für PyTorch Version 1.11 und höher ist der SageMaker Training Compiler für verteiltes Training mit der für den pytorch_xla Parameter angegebenen Option verfügbar. distribution

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()

Für die unterstützte Version von PyTorch v1.10.2 und früher benötigt SageMaker Training Compiler einen alternativen Mechanismus zum Starten eines verteilten Trainingsjobs. Um verteilte Schulungen ausführen zu können, müssen Sie beim SageMaker Training Compiler dem Argument ein Skript für den Start von verteiltem Training von SageMaker KI übergeben und Ihr Trainingsskript an das entry_point Argument übergeben. hyperparameters Das folgende Codebeispiel zeigt, wie ein SageMaker AI Hugging Face Face-Schätzer konfiguriert wird, indem die erforderlichen Änderungen angewendet werden.

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()

Das Startskript sollte wie folgt aussehen. Es umschließt Ihr Trainingsskript und konfiguriert die verteilte Trainingsumgebung in Abhängigkeit von der Größe der Trainings-Instance Ihrer Wahl.

# 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)