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Upgrade XGBoost Version 0.90 to Version 1.5

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Upgrade XGBoost Version 0.90 to Version 1.5 - Amazon SageMaker AI

If you are using the SageMaker Python SDK, to upgrade existing XGBoost 0.90 jobs to version 1.5, you must have version 2.x of the SDK installed and change the XGBoost version and framework_version parameters to 1.5-1. If you are using Boto3, you need to update the Docker image, and a few hyperparameters and learning objectives.

Upgrade SageMaker AI Python SDK Version 1.x to Version 2.x

If you are still using Version 1.x of the SageMaker Python SDK, you must to upgrade version 2.x of the SageMaker Python SDK. For information on the latest version of the SageMaker Python SDK, see Use Version 2.x of the SageMaker Python SDK. To install the latest version, run:

python -m pip install --upgrade sagemaker

Change the image tag to 1.5-1

If you are using the SageMaker Python SDK and using the XGBoost build-in algorithm, change the version parameter in image_uris.retrive.

from sagemaker import image_uris image_uris.retrieve(framework="xgboost", region="us-west-2", version="1.5-1") estimator = sagemaker.estimator.Estimator(image_uri=xgboost_container, hyperparameters=hyperparameters, role=sagemaker.get_execution_role(), instance_count=1, instance_type='ml.m5.2xlarge', volume_size=5, # 5 GB output_path=output_path)

If you are using the SageMaker Python SDK and using XGBoost as a framework to run your customized training scripts, change the framework_version parameter in the XGBoost API.

estimator = XGBoost(entry_point = "your_xgboost_abalone_script.py", framework_version='1.5-1', hyperparameters=hyperparameters, role=sagemaker.get_execution_role(), instance_count=1, instance_type='ml.m5.2xlarge', output_path=output_path)

sagemaker.session.s3_input in SageMaker Python SDK version 1.x has been renamed to sagemaker.inputs.TrainingInput. You must use sagemaker.inputs.TrainingInput as in the following example.

content_type = "libsvm" train_input = TrainingInput("s3://{}/{}/{}/".format(bucket, prefix, 'train'), content_type=content_type) validation_input = TrainingInput("s3://{}/{}/{}/".format(bucket, prefix, 'validation'), content_type=content_type)

For the full list of SageMaker Python SDK version 2.x changes, see Use Version 2.x of the SageMaker Python SDK.

Change Docker Image for Boto3

If you are using Boto3 to train or deploy your model, change the docker image tag (1, 0.72, 0.90-1 or 0.90-2) to 1.5-1.

{ "AlgorithmSpecification":: { "TrainingImage": "746614075791.dkr.ecr.us-west-1.amazonaws.com/sagemaker-xgboost:1.5-1" } ... }

If you using the SageMaker Python SDK to retrieve registry path, change the version parameter in image_uris.retrieve.

from sagemaker import image_uris image_uris.retrieve(framework="xgboost", region="us-west-2", version="1.5-1")

Update Hyperparameters and Learning Objectives

The silent parameter has been deprecated and is no longer available in XGBoost 1.5 and later versions. Use verbosity instead. If you were using the reg:linear learning objective, it has been deprecated as well in favor of reg:squarederror. Use reg:squarederror instead.

hyperparameters = { "verbosity": "2", "objective": "reg:squarederror", "num_round": "50", ... } estimator = sagemaker.estimator.Estimator(image_uri=xgboost_container, hyperparameters=hyperparameters, ...)
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