Date: Wed, 03 Dec 2025 00:20:51 +0000 From: Yuri Victorovich <yuri@FreeBSD.org> To: ports-committers@FreeBSD.org, dev-commits-ports-all@FreeBSD.org, dev-commits-ports-main@FreeBSD.org Subject: git: c5dc7be9e456 - main - misc/py-sagemaker-train: Update WWW and pkg-descr Message-ID: <692f8263.fe74.1ef707cb@gitrepo.freebsd.org>
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The branch main has been updated by yuri: URL: https://cgit.FreeBSD.org/ports/commit/?id=c5dc7be9e456ea96f63191699f1881866358c49e commit c5dc7be9e456ea96f63191699f1881866358c49e Author: Yuri Victorovich <yuri@FreeBSD.org> AuthorDate: 2025-12-02 21:19:09 +0000 Commit: Yuri Victorovich <yuri@FreeBSD.org> CommitDate: 2025-12-03 00:20:48 +0000 misc/py-sagemaker-train: Update WWW and pkg-descr --- misc/py-sagemaker-train/Makefile | 5 ++--- misc/py-sagemaker-train/pkg-descr | 15 +++++---------- 2 files changed, 7 insertions(+), 13 deletions(-) diff --git a/misc/py-sagemaker-train/Makefile b/misc/py-sagemaker-train/Makefile index f457f72a9c19..e098881acf08 100644 --- a/misc/py-sagemaker-train/Makefile +++ b/misc/py-sagemaker-train/Makefile @@ -6,9 +6,8 @@ PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} DISTNAME= ${PORTNAME:S/-/_/}-${PORTVERSION} MAINTAINER= yuri@FreeBSD.org -COMMENT= SageMaker: Library for training & deploying models on Amazon SageMaker -WWW= https://sagemaker.readthedocs.io/en/stable/ \ - https://github.com/aws/sagemaker-python-sdk +COMMENT= SageMaker: Amazon Training Toolkit +WWW= https://github.com/aws/sagemaker-training-toolkit LICENSE= APACHE20 LICENSE_FILE= ${WRKSRC}/LICENSE diff --git a/misc/py-sagemaker-train/pkg-descr b/misc/py-sagemaker-train/pkg-descr index 16dad05472d1..00dd3e52a609 100644 --- a/misc/py-sagemaker-train/pkg-descr +++ b/misc/py-sagemaker-train/pkg-descr @@ -1,11 +1,6 @@ -sagemaker-train is a part of the SageMaker Python SDK. +sagemaker-train is an Amazon SageMaker Training Toolkit. -SageMaker Python SDK is an open source library for training and deploying -machine learning models on Amazon SageMaker. - -With the SDK, you can train and deploy models using popular deep learning -frameworks Apache MXNet and TensorFlow. You can also train and deploy -models with Amazon algorithms, which are scalable implementations of core -machine learning algorithms that are optimized for SageMaker and GPU training. -If you have your own algorithms built into SageMaker compatible Docker -containers, you can train and host models using these as well. +This library allows you to write a script to train a model in Amazon +SageMaker. It provides functionality for your training script to communicate +with the SageMaker training environment, including writing metrics, saving +models, and accessing hyperparameters and other configuration.home | help
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