如何使用 SageMaker LightGBM

使用 LightGBM 作為內建演算法

使用 LightGBM 內建演算法來建置 LightGBM 訓練容器，如下列程式碼範例所示。您可以使用 URI SageMaker image_uris.retrieveAPI（如果使用 Amazon SageMaker Python SDK 第 2 版get_image_uriAPI） 自動識別 LightGBM 內建演算法映像。

指定 LightGBM Image 之後URI，您可以使用 LightGBM 容器，使用估算器建構 SageMaker 估算器API並啟動訓練任務。LightGBM 內建演算法會以指令碼模式執行，但訓練指令碼會提供給您，不需要加以取代。如果您有使用指令碼模式建立 SageMaker 訓練任務的豐富經驗，則可以整合自己的 LightGBM 訓練指令碼。

from sagemaker import image_uris, model_uris, script_uris

train_model_id, train_model_version, train_scope = "lightgbm-classification-model", "*", "training"
training_instance_type = "ml.m5.xlarge"

# 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_multiclass/"

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[
    "num_boost_round"
] = "500"
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, # for distributed training, specify an instance_count greater than 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(
    {
        "train": training_dataset_s3_path,
        "validation": validation_dataset_s3_path,
    }, logs=True, job_name=training_job_name
)

如需如何將 LightGBM 設定為內建演算法的詳細資訊，請參閱下列筆記本範例。