amazon-sagemaker-examples README

The project's README from github.com/aws/amazon-sagemaker-examples. As one markdown file: README.md.

SageMaker

:exclamation::fire: Announcing SageMaker-Core: A New Python SDK for Amazon SageMaker :fire::exclamation:

Introduction

Today, Amazon SageMaker is excited to announce the release of SageMaker-Core, a new Python SDK that provides an object-oriented interface for interacting with SageMaker resources such as TrainingJob, Model, and Endpoint. This SDK introduces the resource chaining feature, allowing developers to pass resource objects as parameters, eliminating manual parameter specification and simplifying code management. SageMaker-Core abstracts low-level details like resource state transitions and polling logic, achieving full parity with SageMaker APIs. It also includes usability improvements such as auto code completion, comprehensive documentation, and type hints, enhancing the overall developer experience.

Use Case

SageMaker-Core is ideal for ML practitioners who seek full customization of AWS primitives for their ML workloads. SageMaker-Core is an improvement over Boto3, providing a more intuitive and efficient way to manage SageMaker resources. By providing an intuitive object-oriented interface and resource chaining, the SDK allows for seamless integration and management of SageMaker resources. This flexibility, combined with intelligent defaults enables developers to tailor their ML workloads according to their needs. Comprehensive documentation, and type hints help developers write code faster and with fewer errors without navigating complex API documentation.

Call to Action

To learn more about SageMaker-Core, visit the documentation and example notebooks. Get started today by integrating SageMaker-Core into your machine learning workflows and experience the benefits of a streamlined and efficient development process.

Amazon SageMaker Examples

Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker.

:books: Read this before you proceed further

Amazon SageMaker examples are divided in two repositories:

Planning to submit a PR to this repository? Read this first:

:hammer_and_wrench: Setup

The quickest setup to run example notebooks includes:

:computer: Usage

These example notebooks are automatically loaded into SageMaker Notebook Instances. They can be accessed by clicking on the SageMaker Examples tab in Jupyter or the SageMaker logo in JupyterLab.

Although most examples utilize key Amazon SageMaker functionality like distributed, managed training or real-time hosted endpoints, these notebooks can be run outside of Amazon SageMaker Notebook Instances with minimal modification (updating IAM role definition and installing the necessary libraries).

:notebook: Example Notebook Categories

The notebooks are organized by ML capability, following the lifecycle order of a typical project: train a model, customize it, evaluate it, deploy it, then operationalize it. Every notebook is self-contained: it lives with the scripts, data and images it needs, and all of its references are relative to its own folder.

Training

Amazon SageMaker Training is a fully managed service that helps you train ML models at scale. It containerizes your workload and manages the AWS compute for you, so you can focus on the model rather than the infrastructure. These examples train with ModelTrainer from SageMaker Python SDK v3 and cover script and framework training, distributed training, managed spot training with checkpointing, heterogeneous clusters, bringing your own container, submitting work through AWS Batch training queues, and running local code as a training job with @remote.

Model Customization

Model customization adapts a pre-trained foundation model to your data and your task, which is usually far cheaper and faster than training from scratch. These examples cover the fine-tuning techniques SageMaker supports — supervised fine-tuning (SFT), direct preference optimization (DPO), reinforcement learning with verifiable rewards (RLVR), RL from AI feedback (RLAIF), multi-turn RL (MTRL) and continued pre-training (CPT) — along with recipe overrides, data mixing, JumpStart fine-tuning with a private model hub, and distributed fine-tuning across serverless, serverful and HyperPod compute.

Evaluation

Before you promote a customized model you need to know whether it actually got better. These examples use the SageMaker evaluator surface to score models: standard benchmark evaluation, your own scoring function, an LLM acting as a judge, and Inspect AI. They show how to evaluate a base model and a fine-tuned model on the same footing so the comparison is meaningful.

Inference

With Amazon SageMaker you can get predictions from your trained models through a broad set of deployment options, and scale them without taking on the operational burden yourself. These examples deploy with ModelBuilder and sagemaker-core, and cover real-time, serverless and asynchronous endpoints; in-process and local-container modes for fast iteration; inference pipelines; multi-model and multi-container endpoints; safe rollout through A/B testing, shadow variants and deployment guardrails; autoscaling with inference components; latency and cost optimization with optimize(); batch transform; deployment to Amazon Bedrock; AWS Marketplace model packages; and human review with Augmented AI.

MLOps

MLOps applies DevOps practices to machine learning: continuous integration and deployment, reproducibility, lineage and governance. It accounts for the parts of an AI/ML project that ordinary software delivery does not, which shortens delivery time, reduces defects and makes data science more productive. These examples cover SageMaker Pipelines with the Model Registry, lineage tracking, experiment tracking, Feature Store, processing jobs, EMR Serverless steps, bias and explainability analysis with Clarify, and MLflow.

:balance_scale: License

This library is licensed under the Apache 2.0 License. For more details, please take a look at the LICENSE file.

:handshake: Contributing

Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed. Please read our contributing guidelines if you'd like to open an issue or submit a pull request.

amazon-sagemaker-examples (aws/amazon-sagemaker-examples)

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Amazon-sagemaker-examples
Что это такое?Сборник официальных блокнотов Jupyter от AWS, которые показывают, как создавать, обучать и развёртывать модели машинного обучения с помощью Amazon SageMaker.
Это библиотека или просто набор примеров блокнотов?Это исключительно сборник блокнотов, а не SDK или библиотека. Каждый блокнот самодостаточен и содержит собственные скрипты, данные и изображения. Блокноты разложены по этапам жизненного цикла машинного обучения: обучение, настройка моделей, оценка, инференс и MLOps.
Как запустить эти блокноты?Они загружаются в SageMaker Notebook Instance автоматически и открываются из вкладки SageMaker Examples в Jupyter или JupyterLab. Их также можно запустить вне SageMaker с небольшими изменениями, например, обновив роль IAM и установив нужные библиотеки.
Какая настройка AWS нужна мне перед запуском этих блокнотов?Нужны аккаунт AWS, правильно настроенные пользователь и роль IAM, Amazon SageMaker Notebook Instance и бакет S3.
Могу ли я отправить любой пример блокнота в виде PR?Нет, принимаются только блокноты, которые показывают возможность SageMaker, ещё не описанную в репозитории. Другие примеры нужно отправлять в отдельный репозиторий SageMaker Example Community.
В чём разница между этим репозиторием и репозиторием SageMaker Example Community?Этот репозиторий — официальный набор, который ведёт команда Amazon SageMaker. Он охватывает широкий круг возможностей SageMaker. Репозиторий Community содержит дополнительные примеры и эталонные решения, которые поддерживают инженеры AWS и архитекторы решений.
Какие методы тонкой настройки моделей описаны в блокнотах?Они охватывают контролируемую тонкую настройку (SFT), оптимизацию прямых предпочтений (DPO), обучение с подкреплением на основе проверяемых наград (RLVR), обучение с подкреплением на основе обратной связи от ИИ (RLAIF), многоходовое обучение с подкреплением и продолжение предобучения, а также тонкую настройку через JumpStart и распределённую тонкую настройку на бессерверных, серверных и HyperPod-вычислениях.
Какие варианты развёртывания показывают блокноты инференса?Конечные точки реального времени, бессерверные и асинхронные конечные точки, локальные режимы в контейнерах, конечные точки с несколькими моделями и несколькими контейнерами, A/B-тестирование и теневые выкатки вариантов, автомасштабирование, пакетное преобразование, а также развёртывание в Amazon Bedrock или AWS Marketplace.
Что такое SageMaker-Core, о котором упоминается в начале README?Это новый объектно-ориентированный Python SDK для SageMaker, который добавляет цепочки ресурсов, автодополнение кода и подсказки типов, чтобы упростить управление такими ресурсами, как TrainingJob, Model и Endpoint.
What are similar projects?pytorch-lightning, ludwig, metaflow, TensorFlow-Examples, FinGPT, recommenders, ms-swift, autumn-recruitment
Which topics is it in?rlhf, mlops, jupyter-notebook, examples, training, aws, reinforcement-learning, jupyter notebook, fine-tuning, data-science, inference, computer-vision
Creator
aws
Size
691.7 MB
Stars
11K
Updated
CommitOct 14Releasev1.0.0Sep 08
Age
8y
Commits · 90 Days