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πŸ“•machine learning tech collections at Microsoft and subsidiaries.
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Keita Onabuta 4e81cc5118
Merge pull request #42 from microsoft/dev 
Prompt Engine & EcoFlows
 		2022-09-08 16:15:48 +09:00
.gitignore Initial commit 2021-03-25 22:05:09 +09:00
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README.md add Prompt Engine 2022-08-16 08:56:02 +00:00
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README.md

Machine Learning Collection

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Table of Contents

Boosting

  • LightGBM - A fast, distributed, high performance gradient boosting framework.
  • LightGBM benchmarking suite - Benchmark tools for LightGBM.
  • Explainable Boosting Machines - interpretable model developed in Microsoft Research using bagging, gradient boosting, and automatic interaction detection to estimated generalized additive models.
  • Cyclic Boosting Machines - An explainable supervised machine learning algorithm specifically for predicting count-data, such as sales and demand.

AutoML

  • Neural Network Intelligence - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
  • Archai - Reproducible Rapid Research for Neural Architecture Search (NAS).
  • FLAML - A fast and lightweight AutoML library.
  • Azure Automated Machine Learning - Automated Machine Learning for Tabular data (regression, classification and forecasting) by Azure Machine Learning
  • Cream - A collection of Microsoft NAS and Vision Transformer work.

Neural Network

  • PyMarlin - Lightweight Deep Learning Model Training library based on PyTorch.
  • bayesianize - A Bayesian neural network wrapper in pytorch.
  • O-CNN - Octree-based convolutional neural networks for 3D shape analysis.
  • ResNet - deep residual network.
  • CNTK - microsoft cognitive toolkit (CNTK), open source deep-learning toolkit.
  • InfiniBatch - Efficient, check-pointed data loading for deep learning with massive data sets.
  • Models under Hugging Face - Microsoft shares transformer models at Hugging Face. 51 pretrained models (as of June 28, 2021).
  • Muzic - Music Understanding and Generation with Artificial Intelligence.

Graph & Network

  • graspologic - utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms.
  • TF Graph Neural Network Samples - tensorFlow implementations of graph neural networks.
  • ptgnn - PyTorch Graph Neural Network Library
  • StemGNN - spectral temporal graph neural network (StemGNN) for multivariate time-series forecasting.
  • SPTAG - a distributed approximate nearest neighborhood search (ANN) library.
  • DiskANN - Scalable graph based indices for approximate nearest neighbor search.

Vision

  • Microsoft Vision Model ResNet50 - a large pretrained vision ResNet-50 model using search engine's web-scale image data.
  • Oscar - Object-Semantics Aligned Pre-training for Vision-Language Tasks.
  • TorchGeo - a PyTorch domain library, similar to torchvision, that provides datasets, transforms, samplers, and pre-trained models specific to geospatial data.
  • Swin Transformer - an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Time Series

NLP

  • T-ULRv2 - Turing multilingual language model.
  • Turing-NLG - Turing Natural Language Generation, 17 billion-parameter language model.
  • DeBERTa - Decoding-enhanced BERT with Disentangled Attention
  • UniLM - Unified Language Model Pre-training / Pre-training for NLP and Beyond
  • Unicoder - Unicoder model for understanding and generation.
  • NeuronBlocks - building your nlp dnn models like playing lego
  • Multilingual Model Transfer - new deep learning models for bootstrapping language understanding models for languages with no labeled data using labeled data from other languages.
  • MT-DNN - multi-task deep neural networks for natural language understanding.
  • inmt - interactive neural machine trainslation-lite
  • OpenKP - automatically extracting keyphrases that are salient to the document meanings is an essential step in semantic document understanding.
  • DeText - a deep neural text understanding framework for ranking and classification tasks.
  • Genalog - an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.
  • FastFormers - highly efficient transformer models for NLU.
  • VERSEAGILITY - a Python-based toolkit to ramp up your custom natural language processing (NLP) task, allowing you to bring your own data and bring models into production. It is a central component of the Microsoft Data Science Toolkit.
  • DPU Utilities - Utilities used by the Deep Program Understanding team.
  • KEAR - Official code for achieving human parity on CommonsenseQA with External Attention.
  • Prompt Engine - A utility library for creating and maintaining prompts for Large Language Models.

Online Machine Learning

  • Vowpal Wabbit - fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more.

Recommendation

  • Recommenders - examples and best practics for building recommendation systems (A2SVD, DKN, xDeepFM, LightGBM, LSTUR, NAML, NPA, NRMS, RLRMC, SAR, Vowpal Wabbit are invented/contributed by Microsoft).
  • GDMIX - A deep ranking personalization framework
  • rankerEval - A fast numpy-based implementation of ranking metrics for information retrieval and recommendation.

Distributed

  • DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
  • MMLSpark - machine learning library on spark.
  • photon-ml - a scalable machine learning library on apache spark.
  • TonY - framework to natively run deep learning frameworks on apache hadoop.
  • isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.

Causal Inference

  • EconML - Python package for estimating heterogeneous treatment effects from observational data via machine learning.
  • DoWhy - Python library for causal inference that supports explicit modeling and testing of causal assumptions.

Responsible AI

  • InterpretML - a toolkit to help understand models and enable responsbile machine learning.
    • Interpret Community - extends interpret repo with additional interpretability techniques and utility functions.
    • DiCE - diverse counterfactual explanations.
    • Interpret-Text - state-of-the-art explainers for text-based ml models and visualize with dashboard.
  • fairlearn - python package to assess and improve fairness of machine learning models.
  • LiFT - linkedin fairness toolkit.
  • RobustDG - Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
  • SHAP - a game theoretic approach to explain the output of any machine learning model (scott lundbert, Microsoft Research).
  • LIME - explaining the predictions of any machine learning classifier (Marco, Microsoft Research).
  • BackwardCompatibilityML - Project for open sourcing research efforts on Backward Compatibility in Machine Learning
  • confidential-ml-utils - Python utilities for training and deploying ML models against data you can't see.
  • presidio - context aware, pluggable and customizable data protection and anonymization service for text and images.
    • Presidio-research - This package features data-science related tasks for developing new recognizers for Presidio.
  • Confidential ONNX Inference Server - An Open Enclave port of the ONNX inference server with data encryption and attestation capabilities to enable confidential inference on Azure Confidential Computing.
  • Responsible-AI-Widgets - responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
  • Error Analysis - A toolkit to help analyze and improve model accuracy.
  • Secure Data Sandbox - A toolkit for conducting machine learning trials against confidential data.
  • shrike - Python utilities to aid "compliant experiment" in Azure Machine Learning - training ML models without seeing the training data.
  • HAX Toolkit - The Human-AI eXperience (HAX) Toolkit is a set of practical tools for creating human-AI experiences with people in mind from the beginning.
  • GAM Changer - Edit machine learning models to reflect human knowledge and values.
  • AdaTest - Find and fix bugs in natural language machine learning models using adaptive testing.

Optimization

  • ONNXRuntime - cross-platfom, high performance ML inference and training accelerator.
  • Hummingbird - compile trained ml model into tensor computation for faster inference.
  • EdgeML - provides code for machine learning algorithms for edge devices developed at Microsoft Research India.
  • DirectML - high-performance, hardware-accelerated DirectX 12 library for machine learning.
  • MMdnn - MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization.
  • inifinibatch - Efficient, check-pointed data loading for deep learning with massive data sets.
  • InferenceSchema - Schema decoration for inference code
  • nnfusion - flexible and efficient deep neural network compiler.
  • Accera - Open source cross-platform compiler for compute-intensive loops used in AI algorithms, from Microsoft Research.

Reinforcement Learning

  • AirSim - open source simulator for autonomous vehicles build on unreal engine / unity from microsoft research.

  • TextWorld - TextWorld is a sandbox learning environment for the training and evaluation of reinforcement learning (RL) agents on text-based games.

  • Moab - Project Moab, a new open-source balancing robot to help engineers and developers learn how to build real-world autonomous control systems with Project Bonsai.

  • MARO - multi-agent resource optimization (MARO) platfom.

  • Training Data-Driven or Surrogate Simulators - build simulation from data for use in RL and Bonsai platform for machine teaching.

  • Bonsai - low code industrial machine teaching platform.

    • Bonsai Python SDK - A python library for integrating data sources with Bonsai BRAIN.

  • SEGAR - Sandbox environment for generalizable agent research.

Security

  • counterfit - a CLI that provides a generic automation layer for assessing the security of ML models.
  • Federated Learning Simulation Framework - a flexible framework for running experiments with PyTorch models in a simulated Federated Learning (FL) environment.
  • FLUTE - a platform for conducting high-performance federated learning simulations.

Windows

Datasets

  • COCO Dataset - COCO is a large-scale object detection, segmentation, and captioning dataset.
  • MS MARCO - collection of datasets focused on deep learning in search.
  • InnerEye CreateDataset - InnerEye dataset creation tool for InnerEye-DeepLearning library. Transforms DICOM data into mask for training Deep Learning models.
  • Sepsis Cohort from MIMIC III - Sepsis cohort from MIMIC dataset.
  • MIND : Microsoft News Dataset - a large-scale dataset for news recommendation research.
  • Dataset for AI for Earth - AIForEarthDataSets is a collection of datasets for AI research.
  • ORBIT - a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone.
  • EcoFlows - Community-representation to collaborate on labelled AI data for ecological and agricultural scenarios in APAC, updated monthly.

Debug & Benchmark

  • tensorwatch - debugging, monitoring and visualization for python machine learning and data science.
  • PYRIGHT - static type checker for python.
  • Bench ML - Python library to benchmark popular pre-built cloud AI APIs.
  • debugpy - An implementation of the Debug Adapter Protocol for Python
  • kineto - A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters contributed by Azure AI Platform team.
  • SuperBenchmark - a benchmarking and diagnosis tool for AI infrastructure (software & hardware).
  • tempeh - tempeh is a framework to TEst Machine learning PErformance exHaustively which includes tracking memory usage and run time.

Pipeline

  • GitHub Actions - Automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub.
  • Azure Pipelines - Automate your builds and deployments with Pipelines so you spend less time with the nuts and bolts and more time being creative.
  • Dagli - framework for defining machine learning models, including feature generation and transformations as DAG.

Platform

  • AI for Earth API Platform - distributed infrastructure designed to provide a secure, scalable, and customizable API hosting, designed to handle the needs of long-running/asynchronous machine learning model inference.

  • Open Platfom for AI (OpenPAI) - resource scheduling and cluster management for AI.

    • OpenPAI Runtime - Runtime for deep learning workload.
    • OpenPAI Protocol - OpenPAI protocol enables job sharing and portability.
    • Openpaimarketplace - A marketplace which stores examples and job templates of openpai.
    • OpenPAI FrameworkController - built to orchestrate all kinds of applications on Kubernetes by a single controller.
    • HivedDScheduler - Kubernetes Scheduler for Deep Learning.
    • OpenPAI JS SDK - The JavaScript SDK is designed to facilitate the developers of OpenPAI to offer user friendly experience.
    • OpenPAI VS Code Client - Extension to connect OpenPAI clusters, submit AI jobs, simulate jobs locally, manage files, and so on.

  • MLOS - Data Science powered infrastructure and methodology to democratize and automate Performance Engineering.

  • Platform for Situated Intelligence - an open-source framework for multimodal, integrative AI.

  • Qlib - an AI-oriented quantitative investment platform.

Feature Engineering

  • Feast on Azure - Azure plugins for Feast (FEAture STore).
  • Feathr - An Enterprise-Grade, High Performance Feature Store.

Tagging

  • TagAnomaly - Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)
  • VoTT - Visual object tagging tool
  • Satellite imagery annotation tool - A lightweight web-interface for creating and sharing vector annotations over satellite/aerial imagery scenes.

Developer tool

  • Visual Studio Code - Code editor redefined and optimized for building and debugging modern web and cloud applications.
  • Gather - adds gather functionality in the Python language to the Jupyter Extension.
  • Pylance - an extension that works alongside Python in Visual Studio Code to provide performant language support.
  • Azure ML Snippets - VSCode snippets for Azure Machine Learning

Sample Code

Community

  • AI@Edge Community - find the resources you need to create solutions using intelligence at the edge through combinations of hardware, machine learning (ML), artificial intelligence (AI) and Microsoft Azure service.
  • Global AI Community - empowers developers who are passionate about AI to share knowledge through events and meetups.
  • Deep Learning Lab (Japan) - provides information on development cases and the latest technology trends related to deep learning.

Workshop

Competition

Book

Learning

Blog, News & Webinar


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Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.