Inference Libraries

22 Components & Libraries

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Inference Libraries

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with PyTensor

PyMC (formerly PyMC3) is a Python package for Bayesian statistical modeling focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensi…

Open-source Python library for statistical analysis of randomised control trials (A/B tests)

The data structures and functionality of ExpAn are generic such that they can be used by both data scientists optimizing a user interface and biologists running wet-lab experiments. The library is al…

Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.

This notebook contains the code required to conduct a Bayesian data analysis on data collected from a set of multiple-lot online auction events executed in Europen markets, over the course of a year.…

Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

The library is under continuous development. Feedback, bug reports and contributions are very welcome! This material is partially based upon work supported by the Defense Advanced Research Projects A…

Machine Learning inference engine for Microcontrollers and Embedded devices

Machine learning for microcontroller and embedded systems. Train in Python, then do inference on any device with a C99 compiler. Embedded-friendly Inference Convenient Training Supporting libraries C…

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

You can also perform direct policy learning from observational data, using the doubly robust method for offline policy learning. These methods directly predict a recommended treatment, without intern…

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application

With Intel(R) Extension for Scikit-learn, you can: To install Intel(R) Extension for Scikit-learn, run: The software acceleration is achieved through patching. It means, replacing the stock scikit-le…

The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.

As we all know the Machine Learning space has a lot of tools and libraries for creating pipelines to train, test & deploy models, and dealing with these many different APIs can be cumbersome. Our…

Create a smart video application using the Intel Distribution of OpenVINO toolkit. The toolkit uses models and inference to run single-class object detection.

This project will no longer be maintained by Intel. This project has been identified as having known security escapes. Intel has ceased development and contributions including, but not limited to, ma…

Serve machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

Serve machine learning models within a Docker container using Amazon SageMaker. To use the SageMaker Inference Toolkit, you need to do the following: Note, passing context as an argument to the handl…

Machine learning based causal inference/uplift in Python

causeinfer can be downloaded from PyPI via pip or sourced directly from this repository: Separate models for treatment and control groups are trained and combined to derive average treatment effects…

Machine learning prediction in pure Python

Sparse data - supports a custom pure python sparse data object - sparse data is handled as would be expected by the relevent transformers and estimators You can also download the source code:

ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.

ParaMonte has been developed while bearing the following design goals in mind: The ParaMonte library relies on three independent parallelism paradigms for parallel applications: The ParaMonte librari…

DoubleML - Double Machine Learning in Python

It further can be readily extended with regards to To install DoubleML with pip use DoubleML can be installed from source via If you use the DoubleML package a citation is highly appreciated: Bibtex-…

Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. Clou…

DaisyKit is an easy AI toolkit with face mask detection, pose detection, background matting, barcode detection, face recognition and more. - with NCNN, OpenCV, Python wrappers

Install packages from Terminal For Windows, Visual Studio 2019 + Git Bash is recommended. Clone the source code: Build Daisykit: Run face detection example: Build Daisykit: Run face detection example…

An open-source efficient deep learning framework/compiler, written in python.

Hidet is an open-source deep learning compiler, written in Python. It supports end-to-end compilation of DNN models from PyTorch and ONNX to efficient cuda kernels. A series of graph-level and operat…

Python Computer Vision & Video Analytics Framework With Batteries Included

⭐ Star us on GitHub — it motivates us a lot and helps the project become more visible to developers. Savant is an open-source, high-level framework for building real-time, streaming, highly efficient…

PyTriton is a Flask/FastAPI-like interface that simplifies Triton's deployment in Python environments.

The distinct capabilities of PyTriton are summarized in the feature matrix: Before proceeding with the installation of PyTriton, ensure your system meets the following criteria: After the inference i…

An open-source computer vision framework to build and deploy apps in minutes

Pipeless is inspired by modern serverless technologies. You provide some functions and Pipeless takes care of executing them for new video frames and everything involved. With Pipeless you create se…

A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.

You can run Inference on an edge device like an NVIDIA Jetson, or on cloud computing platforms like AWS, GCP, and Azure. Currently, the stream interface only supports object detection Check out Infer…

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