如何使用 SageMaker AutoGluon-Tabular

使用 AutoGluon-Tabular 作为内置算法

使用 AutoGluon-Tabular 内置算法构建 AutoGluon-Tabular 训练容器，如以下代码示例所示。你可以使用 SageMaker image_uris.retrieveAPI（或者get_image_uriAPI如果URI使用 Amaz on Pyth SageMaker on SDK 版本 2）自动发现 AutoGluon-Tabular 内置算法图像。

指定 AutoGluon-Tabular 图像后URI，您可以使用 AutoGluon-Tabular 容器使用估算器构造估计器并启动训练作 SageMaker 业。API AutoGluon-Tabular 内置算法在脚本模式下运行，但训练脚本是为你提供的，无需替换。如果您在使用脚本模式创建 SageMaker 训练作业方面有丰富的经验，则可以合并自己的 AutoGluon-Tabular 训练脚本。

from sagemaker import image_uris, model_uris, script_uris

train_model_id, train_model_version, train_scope = "autogluon-classification-ensemble", "*", "training"
training_instance_type = "ml.p3.2xlarge"

# Retrieve the docker image
train_image_uri = image_uris.retrieve(
    region=None,
    framework=None,
    model_id=train_model_id,
    model_version=train_model_version,
    image_scope=train_scope,
    instance_type=training_instance_type
)

# Retrieve the training script
train_source_uri = script_uris.retrieve(
    model_id=train_model_id, model_version=train_model_version, script_scope=train_scope
)

train_model_uri = model_uris.retrieve(
    model_id=train_model_id, model_version=train_model_version, model_scope=train_scope
)

# Sample training data is available in this bucket
training_data_bucket = f"jumpstart-cache-prod-{aws_region}"
training_data_prefix = "training-datasets/tabular_binary/"

training_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}/train"
validation_dataset_s3_path = f"s3://{training_data_bucket}/{training_data_prefix}/validation"

output_bucket = sess.default_bucket()
output_prefix = "jumpstart-example-tabular-training"

s3_output_location = f"s3://{output_bucket}/{output_prefix}/output"

from sagemaker import hyperparameters

# Retrieve the default hyperparameters for training the model
hyperparameters = hyperparameters.retrieve_default(
    model_id=train_model_id, model_version=train_model_version
)

# [Optional] Override default hyperparameters with custom values
hyperparameters[
    "auto_stack"
] = "True"
print(hyperparameters)

from sagemaker.estimator import Estimator
from sagemaker.utils import name_from_base

training_job_name = name_from_base(f"built-in-algo-{train_model_id}-training")

# Create SageMaker Estimator instance
tabular_estimator = Estimator(
    role=aws_role,
    image_uri=train_image_uri,
    source_dir=train_source_uri,
    model_uri=train_model_uri,
    entry_point="transfer_learning.py",
    instance_count=1,
    instance_type=training_instance_type,
    max_run=360000,
    hyperparameters=hyperparameters,
    output_path=s3_output_location
)

# Launch a SageMaker Training job by passing the S3 path of the training data
tabular_estimator.fit(
    {
        "training": training_dataset_s3_path,
        "validation": validation_dataset_s3_path,
    }, logs=True, job_name=training_job_name
)

有关如何将 AutoGluon-Tabular 设置为内置算法的更多信息，请参阅以下笔记本示例。这些示例中使用的任何 S3 存储桶都必须与用于运行它们的笔记本实例位于同一 AWS 区域。