Reverse Dependencies of sagemaker

The following projects have a declared dependency on sagemaker:

• accelerate — Accelerate
• adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• agent-gpt-aws — AgentGPT CLI for training and inference on AWS SageMaker
• aissemble-extensions-model-training-api-sagemaker — no summary
• anymodality — Make running Multi-Modal LLM easy.
• autogluon.cloud — Train and deploy AutoGluon backed models on the cloud
• aws-fortuna — A Library for Uncertainty Quantification.
• aws-sagemaker-remote — Simplify running processing and training remotely on AWS SageMaker
• baram — AWS Framework for python
• batcat — BatCat, A Cat Looks Like A Bat.
• cody-adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• cohere-aws — A Python library for the Cohere endpoints in AWS Sagemaker & Bedrock
• cohere-sagemaker — A Python library for the Cohere endpoints in AWS Sagemaker
• crecs — no summary
• css-mini — Customer Success Scorecard
• cyint-aws-ml-ops-tools — Tools for assisting with managing modern ml-ops-pipelines in AWS Sagemaker
• deepchecks-llm-client — The SDK client for communicating with Deepchecks LLM service
• deeppype — The AWS based Deep Learning Pipeline Framework
• deepsea-ai — DeepSeaAI is a Python package to simplify processing deep sea video in AWS from a command line.
• easy-sm — Easy Sagemaker Ops
• easy-smr — Easy Sagemaker Ops for R projects
• exasol-sagemaker-extension — Exasol SageMaker Integration
• ezsmdeploy — Amazon SageMaker and Bedrock custom model deployments made easy
• ezsmdeploydev — SageMaker custom deployments made easy
• fahr — Tool for running remote machine learning jobs remotely.
• FloTorch-core — A Python project for FloTorch
• fmbench — Benchmark performance of **any Foundation Model (FM)** deployed on **any AWS Generative AI service**, be it **Amazon SageMaker**, **Amazon Bedrock**, **Amazon EKS**, or **Amazon EC2**. The FMs could be deployed on these platforms either directly through `FMbench`, or, if they are already deployed then also they could be benchmarked through the **Bring your own endpoint** mode supported by `FMBench`.
• fmbt — Benchmark performance of **any model** on **any supported instance type** on Amazon SageMaker.
• fmeval — Amazon Foundation Model Evaluations
• fondant — Fondant - Large-scale data processing made easy and reusable
• gab-kedro-sagemaker — Kedro plugin with AWS SageMaker Pipelines support
• gluonts — Probabilistic time series modeling in Python.
• gmlutil — General Machine Learning Utility Package
• gmlutil-data-extraction — General Machine Learning Utility Package for Data Extraction
• hydro-integrations — HydroSDK integrations
• iquaflow — Image quality framework
• jenfi-pipeline-data-app — no summary
• kedro-sagemaker — Kedro plugin with AWS SageMaker Pipelines support
• kumuniverse — Data team shared library for accessing services
• lingo-fit — no summary
• LocalCat — Fine-tune Large Language Models locally.
• mab-ranking — Online Ranking with Multi-Armed-Bandits
• magemaker — A CLI tool to fine-tuning and deploying open-source models
• mapintel — MapIntel is a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus.
• metamaker — Simple CLI to train and deploy your ML models with AWS SageMaker
• ml-comp — An engine for running component based ML pipelines
• mlapp — IBM Services Framework for ML Applications Python 3 framework for building robust, production-ready machine learning applications. Official ML accelerator within the larger RAD-ML methodology.
• mlem — Version and deploy your models following GitOps principles
• mlops-utilities — no summary
• mlpiper — An engine for running component based ML pipelines
• mw-adapter-transformers — A friendly fork of HuggingFace's Transformers, adding Adapters to PyTorch language models
• peelml — Peel away the pain of ml deployment
• productivity-stack — Python Productivity stack for simple environment set up
• py-dreambooth — Easily create your own AI avatar images!
• rastervision-aws-sagemaker — A rastervision plugin that adds an AWS SageMaker pipeline runner
• redbrick-sagemaker — RedBrick AI and AWS Sagemaker integration
• remoterl — Remote RL for training and inference on AWS SageMaker
• renate — Library for Continual Learning for Practitioners
• rkt-mlops-common — doc1
• rudderlabs.data.apps — Rudderlabs data apps library
• sagemaker-experiments-logger — PyTorch Lightning Experiment Logger
• sagemaker-huggingface-inference-toolkit — Open source library for running inference workload with Hugging Face Deep Learning Containers on Amazon SageMaker.
• sagemaker-local — Some dependencies for running sagemaker local.
• sagemaker-mlops-toolkit — A short description of the package.
• sagemaker-rightline — Python package to easily validate properties of a SageMaker Pipeline.
• sagemaker-ssh-helper — A helper library to connect into Amazon SageMaker with AWS Systems Manager and SSH (Secure Shell)
• sagemaker-tensorflow — Amazon Sagemaker specific TensorFlow extensions.
• sagemaker-tidymodels — Sagemaker framework for Tidymodels
• sagemaker-utils — Helper functions to work with SageMaker
• sagemakerify — High level framework to simplify the use of SageMaker
• SageMakerWrapper — A simple wrapper for AWS Sagemaker
• sagemode — Deploy, scale, and monitor your ML models all with one click. Native to AWS.
• sagerender — A library for configuring SageMaker pipelines using hierarchical configuration pattern.
• sagesand — SageMaker Sandbox
• sageworks — SageWorks: A Dashboard and Python API for creating and deploying AWS SageMaker Model Pipelines
• sageworks-bridges — SageWorks Bridges: End User Application Bridges to SageWorks/AWS
• sagify — Machine Learning Training, Tuning and Deployment on AWS
• sapiens-accelerator — no summary
• simple-sagemaker — A **simpler** and **cheaper** way to distribute work (python/shell/training) work on machines of your choice in the (AWS) cloud
• skellyai — generated labeled data
• sm-serverless-benchmarking — Benchmark sagemaker serverless endpoints for cost and performance
• smjsindustry — Open source library for industry machine learning on Amazon SageMaker.
• smspider — SageMaker Spider
• stepfunctions-without-sagemaker — Open source library for developing data science workflows on AWS Step Functions.
• superai — super.AI API
• sych-llm-playground — Sych LLM Playground is a command line tool deploy and interact with language models on the cloud.
• syne-tune — Distributed Hyperparameter Optimization on SageMaker
• tdapiclient — Teradata API Client Python package
• tfduck-bsd — A small example package
• torchx — TorchX SDK and Components
• torchx-nightly — TorchX SDK and Components
• training-grounds — The framework for featurization and model training
• transformers — State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow
• workbench — Workbench: A Dashboard and Python API for creating and deploying AWS SageMaker Model Pipelines
• zenml — ZenML: Write production-ready ML code.
• zenml-nightly — ZenML: Write production-ready ML code.