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

Overview

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
  • Explainable Boosting Machines - interpretable model developed in Microsoft Research using bagging, gradient boosting, and automatic interaction detection to estimated generalized additive models.

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

Neural Network

  • 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.

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.

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.

Time Series

  • luminol - anomaly detection and correlation library.
  • Greykite - flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

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.

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

Distributed

  • DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.
  • MMLSpark - machine learning library on spark.
  • pyton-ml - a scalable machine learning library on apache spark.
  • TonY - framwork to natively run deep learning frameworks on apache hadoop.

Casual 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.
  • 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.

Optimization

  • ONNXRuntime - cross-platfom, high performance ML inference and training accelerator.
  • Hummingbird - compile trained ml model into tensor computation for faster inference.
  • EdgeML -
  • 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.

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.

Security

  • counterfit - a CLI that provides a generic automation layer for assessing the security of ML models.

Windows

Datasets

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).

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.
  • HivedDScheduler - Kubernetes Scheduler for Deep Learning.
  • Open Platfom for AI (OpenPAI - resource scheduling and cluster management for AI.
  • OpenPAI Runtime - Runtime for deep learning workload.
  • 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.

Tagging

  • TagAnomaly - Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)
  • VoTT - Visual object tagging tool

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

Workshop

🏃 coming soon

Competition

Book

Learning

Blog, News & Webinar



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Owner
Microsoft
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