Building Blocks for FOSS ML¶

This section presents the most widespread, mature and promising open source ML software available. The purpose of this section is just to make you curious to maybe try something that suits you.

ML software comes in many different forms. A lot can be written on the differences on all packages below, the quality or the usability. Truth is however there is never one best solution. Depending your practical use case you should make a motivated choice for what package to use.

As with many other evolving technologies in heavy development: Standards are still lacking, so you must ensure that you can switch to another application with minimal pain involved. By using a real open source solution you already have taken the best step! Using OSS makes you are far more independent than using ML cloud solutions. This because these work as ‘black-box’ solutions and by using OSS you can always build your own migration interfaces if needed. Lock-in for ML is primarily in the data and your data cleansing process. So always make sure that you keep full control of all your data and steps involved in the data preparation steps you follow. The true value for all ML solutions are of course always the initial data sources used.

Open Machine Learning Frameworks¶

There are a number of stable and production ready ML frameworks. But choosing which framework to use depends on the use case. If you want to experiment with the latest research insights implemented you will make another choice than if you need to implement your solution in production into a critical environment. For business use: So doing innovation experiments and creating machine learning application most of the time you want a framework that is stable and widely used.

If you have an edge use case experimenting with different frameworks can be a valid choice.

PyTorch is dominating the research, but is now extending this success to industry applications. TensorFlow is already used for many production business cases. But as it is with all software: Transitions from major versions (from TensorFlow 1.0 to 2.0) is difficult. Interoperability standards to easily switch from ML framework are not mature for production use yet.

ML Frameworks¶

Choosing a machine learning (ML) framework or library to solve your use case is easier said than done. Selecting a ML Framework involves making an assessment to decide what is right for your use case. Several factors are important for this assessment for your use case. E.g.:

Easy of use;
Support in the market. Some major FOSS ML Frameworks are supported by many consultancy firms. But maybe community support using mailing lists or internet forums is sufficient to start.
Short goal versus long term strategy. Doing fast innovation tracks means the cost for starting from scratch again should be low. But if you directly focus on a possible production deployment, whether on premise or using cloud hosting this can significantly delay startup time. Often it is recommended to experiment fast and in a later phase take new requirements like maintenance and production deployment into account.
Research of business use case. Some ML frameworks are focussed on innovation and research. If your company is not trying to develop a better ML algorithms this may not be the best ML framework for experimenting for business use cases.
Closed (Commercial) dependencies. Some FOSS frameworks have a dependency with a commercial data collection. E.g. many translation frameworks need an API key of Google or AWS to function. All costs aspects of these dependencies should be taken into account before starting. There is nothing wrong with using commercial software, but transparency on used data sets and models can be crucial for acceptance of your machine learning application.

A special-purpose framework may be better at one aspect than a general-purpose. But the cost of context switching is high:

different languages or APIs
different data formats
different tuning tricks

Your first model for experimenting should be about getting the infrastructure and development tools right. Simple models are usually interpretable. Interpretable models are easier to debug. Complex model erode boundaries beware of the CACE principle (CACE principle: Changing Anything Changes Everything)

AdaNet¶

AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture, but also for learning to ensemble to obtain even better models.

This project is based on the AdaNet algorithm, presented in “AdaNet: Adaptive Structural Learning of Artificial Neural Networks” at ICML 2017, for learning the structure of a neural network as an ensemble of subnetworks.

AdaNet has the following goals:

Ease of use: Provide familiar APIs (e.g. Keras, Estimator) for training, evaluating, and serving models.
Speed: Scale with available compute and quickly produce high quality models.
Flexibility: Allow researchers and practitioners to extend AdaNet to novel subnetwork architectures, search spaces, and tasks.
Learning guarantees: Optimize an objective that offers theoretical learning guarantees.

Documentation at https://adanet.readthedocs.io/en/latest/

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://adanet.readthedocs.io/en/latest/
Source Location	https://github.com/tensorflow/adanet
Tag(s)	ML, ML Framework

Analytics Zoo¶

Analytics Zoo provides a unified analytics + AI platform that seamlessly unites Spark, TensorFlow, Keras and BigDL programs into an integrated pipeline; the entire pipeline can then transparently scale out to a large Hadoop/Spark cluster for distributed training or inference.

Data wrangling and analysis using PySpark
Deep learning model development using TensorFlow or Keras
Distributed training/inference on Spark and BigDL
All within a single unified pipeline and in a user-transparent fashion!

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://analytics-zoo.github.io/master/
Source Location	https://github.com/intel-analytics/analytics-zoo
Tag(s)	ML, ML Framework, Python

Apache MXNet¶

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more.

All major GPU and CPU vendors support this project, but also the real giants like Amazon, Microsoft, Wolfram and a number of very respected universities. So watch this project or play with it to see if it fits your use case.

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.

MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

Gluon is the high-level interface for MXNet. It is more intuitive and easier to use than the lower level interface. Gluon supports dynamic (define-by-run) graphs with JIT-compilation to achieve both flexibility and efficiency. The perfect starters documentation with a great crash course on deep learning can be found here: https://d2l.ai/index.html An earlier version of this documentation is still available on:` http://gluon.mxnet.io/ <http://gluon.mxnet.io/>`__

Part of the project is also the the Gluon API specification (see https://github.com/gluon-api/gluon-api)

The Gluon API specification (Python based) is an effort to improve speed, flexibility, and accessibility of deep learning technology for all developers, regardless of their deep learning framework of choice. The Gluon API offers a flexible interface that simplifies the process of prototyping, building, and training deep learning models without sacrificing training speed.

SBB License	Apache License 2.0
Core Technology	CPP
Project URL	https://mxnet.apache.org/
Source Location	https://github.com/apache/incubator-mxnet
Tag(s)	ML, ML Framework

Apache Spark MLlib¶

Apache Spark MLlib. MLlib is Apache Spark’s scalable machine learning library. MLlib is a Spark subproject providing machine learning primitives. MLlib is a standard component of Spark providing machine learning primitives on top of Spark platform.

Apache Spark is a FOSS platform for large-scale data processing. The Spark engine is written in Scala and is well suited for applications that reuse a working set of data across multiple parallel operations. It’s designed to work as a standalone cluster or as part of Hadoop YARN cluster. It can access data from sources such as HDFS, Cassandra or Amazon S3.

MLlib can be seen as a core Spark’s APIs and interoperates with NumPy in Python and R libraries. And Spark is very fast! MLlib ships with Spark as a standard component.

MLlib library contains many algorithms and utilities, e.g.:

Classification: logistic regression, naive Bayes.
Regression: generalized linear regression, survival regression.
Decision trees, random forests, and gradient-boosted trees.
Recommendation: alternating least squares (ALS).
Clustering: K-means, Gaussian mixtures (GMMs).
Topic modeling: latent Dirichlet allocation (LDA).
Frequent item sets, association rules, and sequential pattern mining.

Using Spark MLlib gives the following advantages:

Excellent scalability options
Performance
User-friendly APIs
Integration with Spark and its other components

But using Spark means that also the Spark platform must be used.

SBB License	Apache License 2.0
Core Technology	Java
Project URL	https://spark.apache.org/mllib/
Source Location	https://github.com/apache/spark
Tag(s)	ML, ML Framework

auto_ml¶

Automated machine learning for analytics & production.

Automates the whole machine learning process, making it super easy to use for both analytics, and getting real-time predictions in production.

Unfortunate unmaintained currently, but still worth playing with.

SBB License	MIT License
Core Technology	Python
Project URL	http://auto-ml.readthedocs.io
Source Location	https://github.com/ClimbsRocks/auto_ml
Tag(s)	ML, ML Framework

BigDL¶

BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

Rich deep learning support. Modeled after Torch, BigDL provides comprehensive support for deep learning, including numeric computing (via Tensor) and high level neural networks; in addition, users can load pre-trained Caffe or Torch or Keras models into Spark programs using BigDL.
Extremely high performance. To achieve high performance, BigDL uses Intel MKL and multi-threaded programming in each Spark task. Consequently, it is orders of magnitude faster than out-of-box open source Caffe, Torch or TensorFlow on a single-node Xeon (i.e., comparable with mainstream GPU).
Efficiently scale-out. BigDL can efficiently scale out to perform data analytics at “Big Data scale”, by leveraging Apache Spark (a lightning fast distributed data processing framework), as well as efficient implementations of synchronous SGD and all-reduce communications on Spark.

SBB License	Apache License 2.0
Core Technology	Java
Project URL	https://bigdl-project.github.io/master/
Source Location	https://github.com/intel-analytics/BigDL
Tag(s)	ML, ML Framework

Blocks¶

Blocks is a framework that is supposed to make it easier to build complicated neural network models on top of Theano.

Blocks is a framework that helps you build neural network models on top of Theano. Currently it supports and provides:

Constructing parametrized Theano operations, called “bricks”
Pattern matching to select variables and bricks in large models
Algorithms to optimize your model
Saving and resuming of training
Monitoring and analyzing values during training progress (on the training set as well as on test sets)
Application of graph transformations, such as dropout

SBB License	MIT License
Core Technology	Python
Project URL	http://blocks.readthedocs.io/en/latest/
Source Location	https://github.com/mila-udem/blocks
Tag(s)	ML, ML Framework

Caffe¶

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

Caffe is an Open framework, models, and worked examples for deep learning:

4.5 years old
7,000+ citations, 250+ contributors, 24,000+ stars
15,000+ forks, >1 pull request / day average at peak

Focus has been vision, but also handles , reinforcement learning, speech and text.

Why Caffe?

Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convnet implementations available.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	CPP
Project URL	http://caffe.berkeleyvision.org/
Source Location	https://github.com/BVLC/caffe
Tag(s)	ML, ML Framework

ConvNetJS¶

ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you’re training. No software requirements, no compilers, no installations, no GPUs, no sweat.

ConvNetJS is a Javascript implementation of Neural networks, together with nice browser-based demos. It currently supports:

Common Neural Network modules (fully connected layers, non-linearities)
Classification (SVM/Softmax) and Regression (L2) cost functions
Ability to specify and train Convolutional Networks that process images
An experimental Reinforcement Learning module, based on Deep Q Learning

For much more information, see the main page at convnetjs.com

Note: Not actively maintained, but still useful to prevent reinventing the wheel.

SBB License	MIT License
Core Technology	Javascript
Project URL	https://cs.stanford.edu/people/karpathy/convnetjs/
Source Location	https://github.com/karpathy/convnetjs
Tag(s)	Javascript, ML, ML Framework

Datumbox¶

The Datumbox Machine Learning Framework is an open-source framework written in Java which allows the rapid development Machine Learning and Statistical applications. The main focus of the framework is to include a large number of machine learning algorithms & statistical methods and to be able to handle large sized datasets.

Datumbox comes with a large number of pre-trained models which allow you to perform Sentiment Analysis (Document & Twitter), Subjectivity Analysis, Topic Classification, Spam Detection, Adult Content Detection, Language Detection, Commercial Detection, Educational Detection and Gender Detection.

Datumbox is not supported by a large team of commercial developers or large group of FOSS developers. Basically one developer maintains it as a side project. So review this FOSS project before you make large investments building applications on top of it.

SBB License	Apache License 2.0
Core Technology	Java
Project URL	http://www.datumbox.com/
Source Location	https://github.com/datumbox/datumbox-framework
Tag(s)	ML, ML Framework

DeepDetect¶

DeepDetect implements support for supervised and unsupervised deep learning of images, text and other data, with focus on simplicity and ease of use, test and connection into existing applications. It supports classification, object detection, segmentation, regression, autoencoders and more.

It has Python and other client libraries.

Deep Detect has also a REST API for Deep Learning with:

JSON communication format
Pre-trained models
Neural architecture templates
Python, Java, C# clients
Output templating

SBB License	MIT License
Core Technology	C++
Project URL	https://deepdetect.com
Source Location	https://github.com/beniz/deepdetect
Tag(s)	ML, ML Framework

Deeplearning4j¶

Deep Learning for Java, Scala & Clojure on Hadoop & Spark With GPUs.

Eclipse Deeplearning4J is an distributed neural net library written in Java and Scala.

Eclipse Deeplearning4j a commercial-grade, open-source, distributed deep-learning library written for Java and Scala. DL4J is designed to be used in business environments on distributed GPUs and CPUs.

Deeplearning4J integrates with Hadoop and Spark and runs on several backends that enable use of CPUs and GPUs. The aim of this project is to create a plug-and-play solution that is more convention than configuration, and which allows for fast prototyping. This project is created by Skymind who delivers support and offers also the option for machine learning models to be hosted with Skymind’s model server on a cloud environment

SBB License	Apache License 2.0
Core Technology	Java
Project URL	https://deeplearning4j.org
Source Location	https://github.com/deeplearning4j/deeplearning4j
Tag(s)	ML, ML Framework

Detectron2¶

Detectron is Facebook AI Research’s software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. Detectron2 is a ground-up rewrite of Detectron that started with maskrcnn-benchmark. The platform is now implemented in PyTorch. With a new, more modular design. Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection algorithms,

New in Detctron 2:

It is powered by the PyTorch deep learning framework.
Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
Can be used as a library to support different projects on top of it. We’ll open source more research projects in this way.
It trains much faster.

The goal of Detectron is to provide a high-quality, high-performance codebase for object detection research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research.

A number of Facebook teams use this platform to train custom models for a variety of applications including augmented reality and community integrity. Once trained, these models can be deployed in the cloud and on mobile devices, powered by the highly efficient Caffe2 runtime.

Documentation on: https://detectron2.readthedocs.io/index.html

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://github.com/facebookresearch/Detectron2
Source Location	https://github.com/facebookresearch/detectron2
Tag(s)	ML, ML Framework, Python

Dopamine¶

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research).

Our design principles are:

Easy experimentation: Make it easy for new users to run benchmark experiments.
Flexible development: Make it easy for new users to try out research ideas.
Compact and reliable: Provide implementations for a few, battle-tested algorithms.
Reproducible: Facilitate reproducibility in results.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://github.com/google/dopamine
Source Location	https://github.com/google/dopamine
Tag(s)	ML, ML Framework, Reinforcement Learning

Fastai¶

The fastai library simplifies training fast and accurate neural nets using modern best practices. Fast.ai’s mission is to make the power of state of the art deep learning available to anyone. fastai sits on top of PyTorch, which provides the foundation.

fastai is a deep learning library which provides high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance.

Docs can be found on: http://docs.fast.ai/

SBB License	Apache License 2.0
Core Technology	Python
Project URL	http://www.fast.ai/
Source Location	https://github.com/fastai/fastai/
Tag(s)	ML, ML Framework

Featuretools¶

One of the holy grails of machine learning is to automate more and more of the feature engineering process.” ― Pedro

Featuretools is a python library for automated feature engineering. Featuretools automatically creates features from temporal and relational datasets. Featuretools works alongside tools you already use to build machine learning pipelines. You can load in pandas dataframes and automatically create meaningful features in a fraction of the time it would take to do manually.

Featuretools is a python library for automated feature engineering. Featuretools can automatically create a single table of features for any “target entity”.

Featuretools is a framework to perform automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	https://www.featuretools.com/
Source Location	https://github.com/Featuretools/featuretools
Tag(s)	ML, ML Framework, Python

FlyingSquid¶

FlyingSquid is a ML framework for automatically building models from multiple noisy label sources. Users write functions that generate noisy labels for data, and FlyingSquid uses the agreements and disagreements between them to learn a label model of how accurate the labeling functions are. The label model can be used directly for downstream applications, or it can be used to train a powerful end model.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	http://hazyresearch.stanford.edu/flyingsquid
Source Location	https://github.com/HazyResearch/flyingsquid
Tag(s)	ML Framework, Python

Keras¶

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
Supports both convolutional networks and recurrent networks, as well as combinations of the two.
Runs seamlessly on CPU and GPU.

SBB License	MIT License
Core Technology	Python
Project URL	https://keras.io/
Source Location	https://github.com/keras-team/keras
Tag(s)	ML, ML Framework

learn2learn¶

learn2learn is a PyTorch library for meta-learning implementations.

The goal of meta-learning is to enable agents to learn how to learn. That is, we would like our agents to become better learners as they solve more and more tasks.

Features:

learn2learn provides high- and low-level utilities for meta-learning. The high-level utilities allow arbitrary users to take advantage of exisiting meta-learning algorithms. The low-level utilities enable researchers to develop new and better meta-learning algorithms.

Some features of learn2learn include:

Modular API: implement your own training loops with our low-level utilities.
Provides various meta-learning algorithms (e.g. MAML, FOMAML, MetaSGD, ProtoNets, DiCE)
Task generator with unified API, compatible with torchvision, torchtext, torchaudio, and cherry.
Provides standardized meta-learning tasks for vision (Omniglot, mini-ImageNet), reinforcement learning (Particles, Mujoco), and even text (news classification).
100% compatible with PyTorch — use your own modules, datasets, or libraries!

SBB License	MIT License
Core Technology	Python
Project URL	http://learn2learn.net/
Source Location	https://github.com/learnables/learn2learn/
Tag(s)	ML Framework

Lore¶

Lore is a python framework to make machine learning approachable for Engineers and maintainable for Data Scientists.

Features

Models support hyper parameter search over estimators with a data pipeline. They will efficiently utilize multiple GPUs (if available) with a couple different strategies, and can be saved and distributed for horizontal scalability.
Estimators from multiple packages are supported: Keras (TensorFlow/Theano/CNTK), XGBoost and SciKit Learn. They can all be subclassed with build, fit or predict overridden to completely customize your algorithm and architecture, while still benefiting from everything else.
Pipelines avoid information leaks between train and test sets, and one pipeline allows experimentation with many different estimators. A disk based pipeline is available if you exceed your machines available RAM.
Transformers standardize advanced feature engineering. For example, convert an American first name to its statistical age or gender using US Census data. Extract the geographic area code from a free form phone number string. Common date, time and string operations are supported efficiently through pandas.
Encoders offer robust input to your estimators, and avoid common problems with missing and long tail values. They are well tested to save you from garbage in/garbage out.
IO connections are configured and pooled in a standard way across the app for popular (no)sql databases, with transaction management and read write optimizations for bulk data, rather than typical ORM single row operations. Connections share a configurable query cache, in addition to encrypted S3 buckets for distributing models and datasets.
Dependency Management for each individual app in development, that can be 100% replicated to production. No manual activation, or magic env vars, or hidden files that break python for everything else. No knowledge required of venv, pyenv, pyvenv, virtualenv, virtualenvwrapper, pipenv, conda. Ain’t nobody got time for that.
Tests for your models can be run in your Continuous Integration environment, allowing Continuous Deployment for code and training updates, without increased work for your infrastructure team.
Workflow Support whether you prefer the command line, a python console, jupyter notebook, or IDE. Every environment gets readable logging and timing statements configured for both production and development.

SBB License	GNU General Public License (GPL) 2.0
Core Technology	Python
Project URL	https://github.com/instacart/lore
Source Location	https://github.com/instacart/lore
Tag(s)	ML, ML Framework, Python

Microsoft Cognitive Toolkit (CNTK)¶

The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK has been available under an open-source license since April 2015.

Docs on: https://docs.microsoft.com/en-us/cognitive-toolkit/

SBB License	MIT License
Core Technology	C++
Project URL	https://docs.microsoft.com/en-us/cognitive-toolkit/
Source Location	https://github.com/Microsoft/CNTK
Tag(s)	ML, ML Framework

ml5.js¶

ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.

The library is supported by code examples, tutorials, and sample data sets with an emphasis on ethical computing. Bias in data, stereotypical harms, and responsible crowdsourcing are part of the documentation around data collection and usage.

ml5.js is heavily inspired by Processing and p5.js.

SBB License	MIT License
Core Technology	Javascript
Project URL	https://ml5js.org/
Source Location	https://github.com/ml5js/ml5-library
Tag(s)	Javascript, ML, ML Framework

Mljar¶

MLJAR is a platform for rapid prototyping, developing and deploying machine learning models.

MLJAR makes algorithm search and tuning painless. It checks many different algorithms for you. For each algorithm hyper-parameters are separately tuned. All computations run in parallel in MLJAR cloud, so you get your results very quickly. At the end the ensemble of models is created, so your predictive model will be super accurate.

There are two types of interface available in MLJAR:

you can run Machine Learning models in your browser, you don’t need to code anything. Just upload dataset, click which attributes to use, which algorithms to use and go! This makes Machine Learning super easy for everyone and make it possible to get really useful models,
there is a python wrapper over MLJAR API, so you don’t need to open any browser or click on any button, just write fancy python code! We like it and hope you will like it too! To start using MLJAR python package please go to our github.

SBB License	MIT License
Core Technology	Python
Project URL	https://mljar.com/
Source Location	https://github.com/mljar/mljar-supervised
Tag(s)	ML, ML Framework, Python

MLsquare¶

[ML]² – ML Square is a python library that utilises deep learning techniques to:

Enable interoperability between existing standard machine learning frameworks.
Provide explainability as a first-class function.
Make ML self learnable.

The following are the design goals:

Bring Your Own Spec First.
Bring Your Own Experience First.
Consistent.
Compositional.
Modular.
Extensible

See https://arxiv.org/pdf/2001.00818.pdf for a in depth explanation.

SBB License	MIT License
Core Technology	Python
Project URL	https://mlsquare.readthedocs.io/en/latest/
Source Location	https://github.com/mlsquare/mlsquare
Tag(s)	ML Framework

NeuralStructuredLearning¶

Neural Structured Learning (NSL) is a new learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. Structure can be explicit as represented by a graph or implicit as induced by adversarial perturbation.

Structured signals are commonly used to represent relations or similarity among samples that may be labeled or unlabeled. Leveraging these signals during neural network training harnesses both labeled and unlabeled data, which can improve model accuracy, particularly when the amount of labeled data is relatively small. Additionally, models trained with samples that are generated by adversarial perturbation have been shown to be robust against malicious attacks, which are designed to mislead a model’s prediction or classification.

NSL generalizes to Neural Graph Learning as well as to Adversarial Learning. The NSL framework in TensorFlow provides the following easy-to-use APIs and tools for developers to train models with structured signals:

Keras APIs to enable training with graphs (explicit structure) and adversarial pertubations (implicit structure).
TF ops and functions to enable training with structure when using lower-level TensorFlow APIs
Tools to build graphs and construct graph inputs for training

NSL is part of the TensorFlow framework. More info on: https://www.tensorflow.org/neural_structured_learning/

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://w ww.tensorflow.org/neural_structured_learning/
Source Location	https://git hub.com/tensorflow/neural-structured-learning
Tag(s)	ML, ML Framework, Python

NNI (Neural Network Intelligence)¶

NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning (AutoML) experiments. The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud. (Microsoft ML project)

Who should consider using NNI:

Those who want to try different AutoML algorithms in their training code (model) at their local machine.
Those who want to run AutoML trial jobs in different environments to speed up search (e.g. remote servers and cloud).
Researchers and data scientists who want to implement their own AutoML algorithms and compare it with other algorithms.
ML Platform owners who want to support AutoML in their platform.

SBB License	MIT License
Core Technology	Python
Project URL	https://nni.readthedocs.io/en/latest/
Source Location	https://github.com/Microsoft/nni
Tag(s)	ML, ML Framework

NuPIC¶

The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implements the HTM learning algorithms. HTM is a detailed computational theory of the neocortex. At the core of HTM are time-based continuous learning algorithms that store and recall spatial and temporal patterns. NuPIC is suited to a variety of problems, particularly anomaly detection and prediction of streaming data sources.

Note: This project is in Maintenance Mode.

SBB License	GNU Affero General Public License Version 3
Core Technology	Python
Project URL	https://numenta.org/
Source Location	https://github.com/numenta/nupic
Tag(s)	ML Framework, Python

Plato¶

The Plato Research Dialogue System is a flexible framework that can be used to create, train, and evaluate conversational AI agents in various environments. It supports interactions through speech, text, or dialogue acts and each conversational agent can interact with data, human users, or other conversational agents (in a multi-agent setting). Every component of every agent can be trained independently online or offline and Plato provides an easy way of wrapping around virtually any existing model, as long as Plato’s interface is adhered to.

OSS by Uber.

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com /uber-research/plato-research-dialogue-system
Source Location	https://github.com /uber-research/plato-research-dialogue-system
Tag(s)	ML, ML Framework

Polyaxon¶

A platform for reproducible and scalable machine learning and deep learning on kubernetes

Polyaxon is a platform for building, training, and monitoring large scale deep learning applications.

Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.

Polyaxon makes it faster, easier, and more efficient to develop deep learning applications by managing workloads with smart container and node management. And it turns GPU servers into shared, self-service resources for your team or organization.

SBB License	MIT License
Core Technology	Python
Project URL	https://polyaxon.com/
Source Location	https://github.com/polyaxon/polyaxon
Tag(s)	ML, ML Framework

Pylearn2¶

Pylearn2 is a library designed to make machine learning research easy.

This project does not have any current developer

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	http://deeplearning.net/software/pylearn2/
Source Location	https://github.com/lisa-lab/pylearn2
Tag(s)	ML, ML Framework

Pyro¶

Deep universal probabilistic programming with Python and PyTorch. Pyro is in an alpha release. It is developed and used byUber AI Labs.

Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with these key principles:

Universal: Pyro can represent any computable probability distribution.
Scalable: Pyro scales to large data sets with little overhead.
Minimal: Pyro is implemented with a small core of powerful, composable abstractions.
Flexible: Pyro aims for automation when you want it, control when you need it.

Documentation on: http://docs.pyro.ai/

SBB License	GNU General Public License (GPL) 2.0
Core Technology	Python
Project URL	http://pyro.ai/
Source Location	https://github.com/uber/pyro
Tag(s)	ML, ML Framework, Python

Pythia¶

Pythia is a modular framework for supercharging vision and language research built on top of PyTorch created by Facebook.

You can use Pythia to bootstrap for your next vision and language multimodal research project. Pythia can also act as starter codebase for challenges around vision and language datasets (TextVQA challenge, VQA challenge).

It features:

Model Zoo: Reference implementations for state-of-the-art vision and language model including LoRRA (SoTA on VQA and TextVQA), Pythia model (VQA 2018 challenge winner) and BAN.
Multi-Tasking: Support for multi-tasking which allows training on multiple dataset together.
Datasets: Includes support for various datasets built-in including VQA, VizWiz, TextVQA and VisualDialog.
Modules: Provides implementations for many commonly used layers in vision and language domain
Distributed: Support for distributed training based on DataParallel as well as DistributedDataParallel.
Unopinionated: Unopinionated about the dataset and model implementations built on top of it.
Customization: Custom losses, metrics, scheduling, optimizers, tensorboard; suits all your custom needs.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	https://le arnpythia.readthedocs.io/en/latest/index.html
Source Location	https://github.com/facebookresearch/pythia
Tag(s)	ML, ML Framework, Python

PyTorch¶

PyTorch is a Python-first machine learning framework that is utilized heavily towards deep learning. It supports CUDA technology (From NVIDIA) to fully use the the power of the dedicated GPUs in training, analyzing and validating neural networks models.

Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs.

PyTorch is very widely used, and is under active development and support. PyTorch is:

a deep learning framework that puts Python first.
a research-focused framework.
Python package that provides two high-level features:

Pytorch uses tensor computation (like NumPy) with strong GPU acceleration. It can use deep neural networks built on a tape-based autograd system.

PyTorch is a Python package that provides two high-level features:

Tensor computation (like NumPy) with strong GPU acceleration
Deep neural networks built on a tape-based autograd system

You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed. PyTorch has become a popular tool in the deep learning research community by combining a focus on usability with careful performance considerations.

A very good overview of the design principles and architecture of PyTorch can be found in this paper https://arxiv.org/pdf/1912.01703.pdf .

SBB License	MIT License
Core Technology	Python
Project URL	http://pytorch.org/
Source Location	https://github.com/pytorch/pytorch
Tag(s)	ML, ML Framework

ReAgent¶

ReAgent is an open source end-to-end platform for applied reinforcement learning (RL) developed and used at Facebook. ReAgent is built in Python and uses PyTorch for modeling and training and TorchScript for model serving. The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, and optimized serving. For more detailed information about ReAgent see the white paper here.

The platform was once named “Horizon” but we have adopted the name “ReAgent” recently to emphasize its broader scope in decision making and reasoning.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	https://engineering.fb.com/ml-applications/horizon/
Source Location	https://github.com/facebookresearch/ReAgent
Tag(s)	ML, ML Framework, Python

RLCard¶

RLCard is a toolkit for Reinforcement Learning (RL) in card games. It supports multiple card environments with easy-to-use interfaces. The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward. RLCard is developed by DATA Lab at Texas A&M University.

Paper: https://arxiv.org/abs/1910.04376

SBB License	MIT License
Core Technology	Python
Project URL	http://rlcard.org/
Source Location	https://github.com/datamllab/rlcard
Tag(s)	ML Framework, Python

Scikit-learn¶

scikit-learn is a Python module for machine learning. s cikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Key features:

Simple and efficient tools for predictive data analysis
Accessible to everybody, and reusable in various contexts
Built on NumPy, SciPy, and matplotlib
Open source, commercially usable – BSD license

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	http://scikit-learn.org
Source Location	https://github.com/scikit-learn/scikit-learn
Tag(s)	ML, ML Framework

SINGA¶

Distributed deep learning system.

SINGA was initiated by the DB System Group at National University of Singapore in 2014, in collaboration with the database group of Zhejiang University.

SINGA‘s software stack includes three major components, namely, core, IO and model:

The core component provides memory management and tensor operations.
IO has classes for reading (and writing) data from (to) disk and network.
The model component provides data structures and algorithms for machine learning models, e.g., layers for neural network models, optimizers/initializer/metric/loss for general machine learning models.

SBB License	Apache License 2.0
Core Technology	Java
Project URL	http://singa.apache.org/
Source Location	https://github.com/apache/singa
Tag(s)	ML Framework

Streamlit¶

The fastest way to build custom ML tools. Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser.

Documentation on: https://streamlit.io/docs/

SBB License	Apache License 2.0
Core Technology	Javascipt, Python
Project URL	https://streamlit.io/
Source Location	https://github.com/streamlit/streamlit
Tag(s)	ML, ML Framework, ML Hosting, ML Tool, Python

Tensorflow¶

TensorFlow is an Open Source Software Library for Machine Intelligence. TensorFlow is by far the most used and popular ML open source project. And since the first initial release was only just in November 2015 it is expected that the impact of this OSS package will expand even more.

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

TensorFlow comes with a tool called TensorBoard which you can use to get some insight into what is happening. TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs.

There is also a version of TensorFlow that runs in a browser. This is TensorFlow.js (https://js.tensorflow.org/ ). TensorFlow.js is a WebGL accelerated, browser based JavaScript library for training and deploying ML models.

Since privacy is a contentious fight TensorFlow has now (2020) also a library called ‘TensorFlow Privacy’ . This is a python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. See: https://github.com/tensorflow/privacy

SBB License	Apache License 2.0
Core Technology	C
Project URL	https://www.tensorflow.org/
Source Location	https://github.com/tensorflow/tensorflow
Tag(s)	ML, ML Framework

TF Encrypted¶

TF Encrypted is a framework for encrypted machine learning in TensorFlow. It looks and feels like TensorFlow, taking advantage of the ease-of-use of the Keras API while enabling training and prediction over encrypted data via secure multi-party computation and homomorphic encryption. TF Encrypted aims to make privacy-preserving machine learning readily available, without requiring expertise in cryptography, distributed systems, or high performance computing.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://tf-encrypted.io/
Source Location	https://github.com/tf-encrypted/tf-encrypted
Tag(s)	ML, ML Framework, Privacy

Theano¶

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

Note: After almost ten years of development the company behind Theano has stopped development and support(Q4-2017). But this library has been an innovation driver for many other OSS ML packages!

Since a lot of ML libraries and packages use Theano you should check (as always) the health of your ML stack.

SBB License	MIT License
Core Technology	Python
Project URL	http://www.deeplearning.net/
Source Location	https://github.com/Theano/Theano
Tag(s)	ML, ML Framework, Python

Thinc¶

Thinc is the machine learning library powering spaCy. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0.

Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.

Thinc is a practical toolkit for implementing models that follow the “Embed, encode, attend, predict” architecture. It’s designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

SBB License	MIT License
Core Technology	Python
Project URL	https://thinc.ai/
Source Location	https://github.com/explosion/thinc
Tag(s)	ML, ML Framework, NLP, Python

Turi¶

Turi Create simplifies the development of custom machine learning models.Turi is OSS machine learning from Apple.

Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	https://github.com/apple/turicreate
Source Location	https://github.com/apple/turicreate
Tag(s)	ML, ML Framework, ML Hosting

TuriCreate¶

This SBB is from Apple. Apple, is with Siri already for a long time active in machine learning. But even Apple is releasing building blocks under OSS licenses now.

Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.

Easy-to-use: Focus on tasks instead of algorithms
Visual: Built-in, streaming visualizations to explore your data
Flexible: Supports text, images, audio, video and sensor data
Fast and Scalable: Work with large datasets on a single machine
Ready To Deploy: Export models to Core ML for use in iOS, macOS, watchOS, and tvOS apps

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	https://turi.com/index.html
Source Location	https://github.com/apple/turicreate
Tag(s)	ML, ML Framework, Python

Vowpal Wabbit¶

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning with several contextual bandit algorithms implemented and the online nature lending to the problem well. Vowpal Wabbit is a destination for implementing and maturing state of the art algorithms with performance in mind.

Input Format. The input format for the learning algorithm is substantially more flexible than might be expected. Examples can have features consisting of free form text, which is interpreted in a bag-of-words way. There can even be multiple sets of free form text in different namespaces.
Speed. The learning algorithm is fast — similar to the few other online algorithm implementations out there. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function.
Scalability. This is not the same as fast. Instead, the important characteristic here is that the memory footprint of the program is bounded independent of data. This means the training set is not loaded into main memory before learning starts. In addition, the size of the set of features is bounded independent of the amount of training data using the hashing trick.
Feature Interaction. Subsets of features can be internally paired so that the algorithm is linear in the cross-product of the subsets. This is useful for ranking problems. The alternative of explicitly expanding the features before feeding them into the learning algorithm can be both computation and space intensive, depending on how it’s handled.

Microsoft Research is a major contributor to Vowpal Wabbit.

SBB License	MIT License
Core Technology	CPP
Project URL	https://vowpalwabbit.org/
Source Location	https://github.com/VowpalWabbit/vowpal_wabbit
Tag(s)	ML, ML Framework

XAI¶

XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning.

You can find the documentation at https://ethicalml.github.io/xai/index.html.

SBB License	MIT License
Core Technology	Python
Project URL	https://ethical.institute/index.html
Source Location	https://github.com/EthicalML/xai
Tag(s)	ML, ML Framework, Python

Computer vision¶

Computer vision is a field that deals with how computers can be made to gain high-level understanding of digital images and videos. Machine learning is a good match for image classification.

libfacedetection¶

This is an open source library for CNN-based face detection in images. The CNN model has been converted to static variables in C source files. The source code does not depend on any other libraries. What you need is just a C++ compiler. You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler.

SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM.

SBB License	GNU General Public License (GPL) 2.0
Core Technology	CPP
Project URL	https://github.com/ShiqiYu/libfacedetection
Source Location	https://github.com/ShiqiYu/libfacedetection
Tag(s)	Computer vision

YOLOv3¶

A minimal PyTorch implementation of YOLOv3, with support for training, inference and evaluation.

You only look once (YOLO) is a state-of-the-art, real-time object detection system. In depth paper on YOLOv3 is on: https://pjreddie.com/media/files/papers/YOLOv3.pdf

SBB License	GNU General Public License (GPL) 2.0
Core Technology	Python
Project URL	https://pjreddie.com/darknet/yolo/
Source Location	https://github.com/eriklindernoren/PyTorch-YOLOv3
Tag(s)	Computer vision, ML

Raster Vision¶

Raster Vision is an open source Python framework for building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery).

It allows users (who don’t need to be experts in deep learning!) to quickly and repeatably configure experiments that execute a machine learning workflow including: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment.

Some features:

There is built-in support for chip classification, object detection, and semantic segmentation with backends using PyTorch and Tensorflow.
Experiments can be executed on CPUs and GPUs with built-in support for running in the cloud using AWS Batch. The framework is extensible to new data sources, tasks (eg. object detection), backends (eg. TF Object Detection API), and cloud providers.

Documentation on: https://docs.rastervision.io/

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://rastervision.io/
Source Location	https://github.com/azavea/raster-vision
Tag(s)	Computer vision, ML

DeOldify¶

A Deep Learning based project for colorizing and restoring old images (and video!)

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/jantic/DeOldify
Source Location	https://github.com/jantic/DeOldify
Tag(s)	Computer vision, ML

SOD¶

SOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices.

SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

Designed for computational efficiency and with a strong focus on real-time applications. SOD includes a comprehensive set of both classic and state-of-the-art deep-neural networks with their pre-trained models.

SBB License	GNU General Public License (GPL) 3.0
Core Technology	C
Project URL	https://sod.pixlab.io/
Source Location	https://github.com/symisc/sod
Tag(s)	Computer vision, ML

makesense.ai¶

makesense.ai is a free to use online tool for labelling photos. Thanks to the use of a browser it does not require any complicated installation – just visit the website and you are ready to go. It also doesn’t matter which operating system you’re running on – we do our best to be truly cross-platform. It is perfect for small computer vision deeplearning projects, making the process of preparing a dataset much easier and faster.

SBB License	GNU General Public License (GPL) 3.0
Core Technology	Typescript
Project URL	https://www.makesense.ai/
Source Location	https://github.com/SkalskiP/make-sense
Tag(s)	Computer vision, ML, ML Tool, Photos

DeepPrivacy¶

DeepPrivacy is a fully automatic anonymization technique for images.

The DeepPrivacy GAN never sees any privacy sensitive information, ensuring a fully anonymized image. It utilizes bounding box annotation to identify the privacy-sensitive area, and sparse pose information to guide the network in difficult scenarios.

DeepPrivacy detects faces with state-of-the-art detection methods. Mask R-CNN is used to generate a sparse pose information of the face, and DSFD is used to detect faces in the image.

The Github repository contains the source code for the paper “DeepPrivacy: A Generative Adversarial Network for Face Anonymization”, published at ISVC 2019.

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/hukkelas/DeepPrivacy
Source Location	https://github.com/hukkelas/DeepPrivacy
Tag(s)	Computer vision, ML, Privacy, Python

Face_recognition¶

The world’s simplest facial recognition api for Python and the command line.

Recognize and manipulate faces from Python or from the command line with the world’s simplest face recognition library.

Built using dlib‘s state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.

This also provides a simple face_recognition command line tool that lets you do face recognition on a folder of images from the command line!

Full API documentation can be found here: https://face-recognition.readthedocs.io/en/latest/

Git quick-scan report:

Date of git statics quick-scan report: 2019/12/19
Number of files in the git repository: 96
Total Lines of Code (of all files): 70415 total
Most recent commit in this repository: Tue Dec 3 16:53:45 2019 +0530
Number of authors:33

First commit info:

Author: Adam Geitgey
Date: Fri Mar 3 16:29:23 2017 -0800

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/ageitgey/face_recognition
Source Location	https://github.com/ageitgey/face_recognition
Tag(s)	Computer vision, face detection, ML, ML Tool, Python

DeepFaceLab¶

DeepFaceLab is a tool that utilizes machine learning to replace faces in videos.

More than 95% of deepfake videos are created with DeepFaceLab.

SBB License	GNU General Public License (GPL) 3.0
Core Technology	Python
Project URL	https://github.com/iperov/DeepFaceLab
Source Location	https://github.com/iperov/DeepFaceLab
Tag(s)	Computer vision, Deepfakes, ML, Python

FaceSwap¶

FaceSwap is a tool that utilizes deep learning to recognize and swap faces in pictures and videos.

When faceswapping was first developed and published, the technology was groundbreaking, it was a huge step in AI development. It was also completely ignored outside of academia because the code was confusing and fragmentary. It required a thorough understanding of complicated AI techniques and took a lot of effort to figure it out. Until one individual brought it together into a single, cohesive collection. Before “deepfakes” these techniques were like black magic, only practiced by those who could understand all of the inner workings as described in esoteric and endlessly complicated books and papers.

Powered by Tensorflow, Keras and Python; Faceswap will run on Windows, macOS and Linux. And GPL licensed!

SBB License	GNU General Public License (GPL) 3.0
Core Technology	Python
Project URL	https://www.faceswap.dev/
Source Location	https://github.com/deepfakes/faceswap
Tag(s)	Computer vision, Deepfakes, ML, Python

JeelizFaceFilter¶

Javascript/WebGL lightweight face tracking library designed for augmented reality webcam filters. Features : multiple faces detection, rotation, mouth opening. Various integration examples are provided (Three.js, Babylon.js, FaceSwap, Canvas2D, CSS3D…).

Enables developers to solve computer-vision problems directly from the browser.

Features:

face detection,
face tracking,
face rotation detection,
mouth opening detection,
multiple faces detection and tracking,
very robust for all lighting conditions,
video acquisition with HD video ability,
interfaced with 3D engines like THREE.JS, BABYLON.JS, A-FRAME,
interfaced with more accessible APIs like CANVAS, CSS3D.

SBB License	Apache License 2.0
Core Technology	Javascript
Project URL	https://jeeliz.com/
Source Location	https://github.com/jeeliz/jeelizFaceFilter
Tag(s)	Computer vision, face detection, Javascript, ML

OpenCV: Open Source Computer Vision Library¶

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.

The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	C
Project URL	https://opencv.org/
Source Location	https://github.com/opencv/opencv
Tag(s)	Computer vision, ML

Luminoth¶

Luminoth is an open source toolkit for computer vision. Currently, we support object detection and image classification, but we are aiming for much more. It is built in Python, using TensorFlow and Sonnet.

Note: No longer maintained.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	https://luminoth.ai
Source Location	https://github.com/tryolabs/luminoth
Tag(s)	Computer vision, ML

ML Tools¶

Besides FOSS machine learning frameworks there are special tools that save you time when creating ML applications. This section is a opinionated collection of FOSS ML tools that can make creating applications easier.

AI Explainability 360¶

The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics.

It is OSS from IBM (so apache2.0) so mind the history of openness IBM has regarding OSS product development. The documentation can be found here: https://aix360.readthedocs.io/en/latest/

SBB License	Apache License 2.0
Core Technology	Python
Project URL	http://aix360.mybluemix.net/
Source Location	https://github.com/IBM/AIX360
Tag(s)	Data analytics, ML, ML Tool, Python

Apollo¶

Apollo is a high performance, flexible architecture which accelerates the development, testing, and deployment of Autonomous Vehicles.

Apollo 2.0 supports vehicles autonomously driving on simple urban roads. Vehicles are able to cruise on roads safely, avoid collisions with obstacles, stop at traffic lights, and change lanes if needed to reach their destination.

Apollo 5.5 enhances the complex urban road autonomous driving capabilities of previous Apollo releases, by introducing curb-to-curb driving support. With this new addition, Apollo is now a leap closer to fully autonomous urban road driving. The car has complete 360-degree visibility, along with upgraded perception deep learning model and a brand new prediction model to handle the changing conditions of complex road and junction scenarios, making the car more secure and aware.

SBB License	Apache License 2.0
Core Technology	C++
Project URL	http://apollo.auto/
Source Location	https://github.com/ApolloAuto/apollo
Tag(s)	ML, ML Tool

Data Science Version Control (DVC)¶

Data Science Version Control or DVC is an open-source tool for data science and machine learning projects. With a simple and flexible Git-like architecture and interface it helps data scientists:

manage machine learning models – versioning, including data sets and transformations (scripts) that were used to generate models;
make projects reproducible;
make projects shareable;
manage experiments with branching and metrics tracking;

It aims to replace tools like Excel and Docs that are being commonly used as a knowledge repo and a ledger for the team, ad-hoc scripts to track and move deploy different model versions, ad-hoc data file suffixes and prefixes.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://dvc.org/
Source Location	https://github.com/iterative/dvc
Tag(s)	ML, ML Tool, Python

Espresso¶

Espresso is an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit `fairseq <https://github.com/pytorch/fairseq>`__. Espresso supports distributed training across GPUs and computing nodes, and features various decoding approaches commonly employed in ASR, including look-ahead word-based language model fusion, for which a fast, parallelized decoder is implemented.

Research paper can be found at https://arxiv.org/pdf/1909.08723.pdf

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/freewym/espresso
Source Location	https://github.com/freewym/espresso
Tag(s)	ML, ML Tool, Python, speech recognition

EuclidesDB¶

EuclidesDB is a multi-model machine learning feature database that is tight coupled with PyTorch and provides a backend for including and querying data on the model feature space. Some features of EuclidesDB are listed below:

Written in C++ for performance;
Uses protobuf for data serialization;
Uses gRPC for communication;
LevelDB integration for database serialization;
Many indexing methods implemented (Annoy, Faiss, etc);
Tight PyTorch integration through libtorch;
Easy integration for new custom fine-tuned models;
Easy client language binding generation;
Free and open-source with permissive license;

SBB License	Apache License 2.0
Core Technology	CPP
Project URL	https://e uclidesdb.readthedocs.io/en/latest/index.html
Source Location	https://github.com/perone/euclidesdb
Tag(s)	ML, ML Tool

Fabrik¶

Fabrik is an online collaborative platform to build, visualize and train deep learning models via a simple drag-and-drop interface. It allows researchers to collaboratively develop and debug models using a web GUI that supports importing, editing and exporting networks written in widely popular frameworks like Caffe, Keras, and TensorFlow.

SBB License	GNU General Public License (GPL) 3.0
Core Technology	Javascript, Python
Project URL	http://fabrik.cloudcv.org/
Source Location	https://github.com/Cloud-CV/Fabrik
Tag(s)	Data Visualization, ML, ML Tool

Face_recognition¶

The world’s simplest facial recognition api for Python and the command line.

Recognize and manipulate faces from Python or from the command line with the world’s simplest face recognition library.

Built using dlib‘s state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark.

This also provides a simple face_recognition command line tool that lets you do face recognition on a folder of images from the command line!

Full API documentation can be found here: https://face-recognition.readthedocs.io/en/latest/

Git quick-scan report:

Date of git statics quick-scan report: 2019/12/19
Number of files in the git repository: 96
Total Lines of Code (of all files): 70415 total
Most recent commit in this repository: Tue Dec 3 16:53:45 2019 +0530
Number of authors:33

First commit info:

Author: Adam Geitgey
Date: Fri Mar 3 16:29:23 2017 -0800

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/ageitgey/face_recognition
Source Location	https://github.com/ageitgey/face_recognition
Tag(s)	Computer vision, face detection, ML, ML Tool, Python

Kedro¶

Kedro is a workflow development tool that helps you build data pipelines that are robust, scalable, deployable, reproducible and versioned. We provide a standard approach so that you can:

spend more time building your data pipeline,
worry less about how to write production-ready code,
standardise the way that your team collaborates across your project,
work more efficiently.

Features:

A standard and easy-to-use project template, allowing your collaborators to spend less time understanding how you’ve set up your analytics project
Data abstraction, managing how you load and save data so that you don’t have to worry about the reproducibility of your code in different environments
Configuration management, helping you keep credentials out of your code base
Pipeline visualisation with Kedro-Viz:(https://github.com/quantumblacklabs/kedro-viz) making it easy to see how your data pipeline is constructed
Seamless packaging, allowing you to ship your projects to production, e.g. using Docker (https://github.com/quantumblacklabs/kedro-docker) or Kedro-Airflow (https://github.com/quantumblacklabs/kedro-airflow)
Versioning for your datasets and machine learning models whenever your pipeline runs

Features:

A standard and easy-to-use project template, allowing your collaborators to spend less time understanding how you’ve set up your analytics project
Data abstraction, managing how you load and save data so that you don’t have to worry about the reproducibility of your code in different environments
Configuration management, helping you keep credentials out of your code base
Pipeline visualisation with [Kedro-Viz](https://github.com/quantumblacklabs/kedro-viz) making it easy to see how your data pipeline is constructed
Seamless packaging, allowing you to ship your projects to production, e.g. using [Kedro-Docker](https://github.com/quantumblacklabs/kedro-docker) or [Kedro-Airflow](https://github.com/quantumblacklabs/kedro-airflow)
Versioning for your data sets and machine learning models whenever your pipeline runs

Documentation on: https://kedro.readthedocs.io/

The REACT visualization for Kedro is on: https://github.com/quantumblacklabs/kedro-viz

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://github.com/quantumblacklabs/kedro
Source Location	https://github.com/quantumblacklabs/kedro
Tag(s)	ML, ML Tool, Python

Ludwig¶

Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. Ludwig provides two main functionalities: training models and using them to predict. It is based on datatype abstraction, so that the same data preprocessing and postprocessing will be performed on different datasets that share data types and the same encoding and decoding models developed for one task can be reused for different tasks.

All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data.

A programmatic API is also available in order to use Ludwig from your python code. A suite of visualization tools allows you to analyze models’ training and test performance and to compare them.

Ludwig is built with extensibility principles in mind and is based on data type abstractions, making it easy to add support for new data types as well as new model architectures.

It can be used by practitioners to quickly train and test deep learning models as well as by researchers to obtain strong baselines to compare against and have an experimentation setting that ensures comparability by performing standard data preprocessing and visualization.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://uber.github.io/ludwig/
Source Location	https://github.com/uber/ludwig
Tag(s)	ML, ML Tool

makesense.ai¶

makesense.ai is a free to use online tool for labelling photos. Thanks to the use of a browser it does not require any complicated installation – just visit the website and you are ready to go. It also doesn’t matter which operating system you’re running on – we do our best to be truly cross-platform. It is perfect for small computer vision deeplearning projects, making the process of preparing a dataset much easier and faster.

SBB License	GNU General Public License (GPL) 3.0
Core Technology	Typescript
Project URL	https://www.makesense.ai/
Source Location	https://github.com/SkalskiP/make-sense
Tag(s)	Computer vision, ML, ML Tool, Photos

MLflow¶

MLflow offers a way to simplify ML development by making it easy to track, reproduce, manage, and deploy models. MLflow (currently in alpha) is an open source platform designed to manage the entire machine learning lifecycle and work with any machine learning library. It offers:

Record and query experiments: code, data, config, results
Packaging format for reproducible runs on any platform
General format for sending models to diverse deploy tools

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://mlflow.org/
Source Location	https://github.com/mlflow/mlflow
Tag(s)	ML, ML Tool, Python

MLPerf¶

A broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms.

The MLPerf effort aims to build a common set of benchmarks that enables the machine learning (ML) field to measure system performance for both training and inference from mobile devices to cloud services. We believe that a widely accepted benchmark suite will benefit the entire community, including researchers, developers, builders of machine learning frameworks, cloud service providers, hardware manufacturers, application providers, and end users.

SBB License	MIT License
Core Technology	Python
Project URL	https://mlperf.org/
Source Location	https://github.com/mlperf/reference
Tag(s)	ML, ML Tool, Performance

ModelDB¶

A system to manage machine learning models.

ModelDB is an end-to-end system to manage machine learning models. It ingests models and associated metadata as models are being trained, stores model data in a structured format, and surfaces it through a web-frontend for rich querying. ModelDB can be used with any ML environment via the ModelDB Light API. ModelDB native clients can be used for advanced support in spark.ml and scikit-learn.

The ModelDB frontend provides rich summaries and graphs showing model data. The frontend provides functionality to slice and dice this data along various attributes (e.g. operations like filter by hyperparameter, group by datasets) and to build custom charts showing model performance.

SBB License	MIT License
Core Technology	Python, Javascript
Project URL	https://mitdbg.github.io/modeldb/
Source Location	https://github.com/mitdbg/modeldb
Tag(s)	Administration, ML, ML Tool

Netron¶

Netron is a viewer for neural network, deep learning and machine learning models.

Netron supports ONNX (.onnx, .pb), Keras (.h5, .keras), CoreML (.mlmodel) and TensorFlow Lite (.tflite). Netron has experimental support for Caffe (.caffemodel), Caffe2 (predict_net.pb), MXNet (-symbol.json), TensorFlow.js (model.json, .pb) and TensorFlow (.pb, .meta).

SBB License	GNU General Public License (GPL) 2.0
Core Technology	Python, Javascript
Project URL	https://www.lutzroeder.com/ai/
Source Location	https://github.com/lutzroeder/Netron
Tag(s)	Data viewer, ML, ML Tool

NLP Architect¶

NLP Architect is an open-source Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration.

Features:

Core NLP models used in many NLP tasks and useful in many NLP applications
Novel NLU models showcasing novel topologies and techniques
Optimized NLP/NLU models showcasing different optimization algorithms on neural NLP/NLU models
Model-oriented design:
- Train and run models from command-line.
- API for using models for inference in python.
- Procedures to define custom processes for training, inference or anything related to processing.
- CLI sub-system for running procedures
Based on optimized Deep Learning frameworks:
- TensorFlow
- PyTorch
- Dynet
Essential utilities for working with NLP models – Text/String pre-processing, IO, data-manipulation, metrics, embeddings.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	http://nlp_architect.nervanasys.com/
Source Location	https://github.com/NervanaSystems/nlp-architect
Tag(s)	ML, ML Tool, NLP, Python

ONNX¶

ONNX provides an open source format for AI models. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. Initially we focus on the capabilities needed for inferencing (evaluation).

Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models. ONNX is supported by a community of partners who have implemented it in many frameworks and tools.

Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. Enabling interoperability between different frameworks and streamlining the path from research to production will increase the speed of innovation in the AI community. We are an early stage and we invite the community to submit feedback and help us further evolve ONNX.

Companies behind ONNX are AWS, Facebook and Microsoft Corporation and more.

SBB License	MIT License
Core Technology	Python
Project URL	http://onnx.ai/
Source Location	https://github.com/onnx/onnx
Tag(s)	ML, ML Tool

OpenML¶

OpenML is an on-line machine learning platform for sharing and organizing data, machine learning algorithms and experiments. It claims to be designed to create a frictionless, networked ecosystem, so that you can readily integrate into your existing processes/code/environments. It also allows people from all over the world to collaborate and build directly on each other’s latest ideas, data and results, irrespective of the tools and infrastructure they happen to use. So nice ideas to build an open science movement. The people behind OpemML are mostly (data)scientist. So using this product for real world business use cases will take some extra effort.

Altrhough OpenML is exposed as an foundation based on openness, a quick inspection learned that the OpenML platform is not as open as you want. Also the OSS software is not created to be run on premise. So be aware when doing large (time) investments into this OpenML platform.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Java
Project URL	https://openml.org
Source Location	https://github.com/openml/OpenML
Tag(s)	ML, ML Tool

Orange¶

Orange is a comprehensive, component-based software suite for machine learning and data mining, developed at Bioinformatics Laboratory.

Orange is available by default on Anaconda Navigator dashboard. Orange is a component-based data mining software. It includes a range of data visualization, exploration, preprocessing and modeling techniques. It can be used through a nice and intuitive user interface or, for more advanced users, as a module for the Python programming language.

One of the nice features is the option for visual programming. Can you do visual interactive data exploration for rapid qualitative analysis with clean visualizations. The graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy.

SBB License	GNU General Public License (GPL) 3.0
Core Technology
Project URL	https://orange.biolab.si/
Source Location	https://github.com/biolab/orange3
Tag(s)	Data Visualization, ML, ML Tool, Python

PySyft¶

A library for encrypted, privacy preserving deep learning. PySyft is a Python library for secure, private Deep Learning. PySyft decouples private data from model training, using Multi-Party Computation (MPC) within PyTorch. View the paper on Arxiv.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://github.com/OpenMined/PySyft
Source Location	https://github.com/OpenMined/PySyft
Tag(s)	ML, ML Tool, Python, Security

RAPIDS¶

The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs–. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

SBB License	Apache License 2.0
Core Technology	C++
Project URL	http://rapids.ai/
Source Location	https://github.com/rapidsai/
Tag(s)	ML, ML Hosting, ML Tool

SHAP¶

SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods [1-7] and representing the only possible consistent and locally accurate additive feature attribution method based on expectations (see our papers for details and citations).

There are also sample notebooks that demonstrate different use cases for SHAP in the github repro.

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/slundberg/shap
Source Location	https://github.com/slundberg/shap
Tag(s)	ML, ML Tool

Skater¶

Skater is a python package for model agnostic interpretation of predictive models. With Skater, you can unpack the internal mechanics of arbitrary models; as long as you can obtain inputs, and use a function to obtain outputs, you can use Skater to learn about the models internal decision policies.

The project was started as a research idea to find ways to enable better interpretability(preferably human interpretability) to predictive “black boxes” both for researchers and practioners.

Documentation at: https://datascienceinc.github.io/Skater/overview.html

SBB License	MIT License
Core Technology	Python
Project URL	https://www.datascience.com/resources/tools/skater
Source Location	https://github.com/datascienceinc/Skater
Tag(s)	ML, ML Tool

Snorkel¶

Snorkel is a system for rapidly creating, modeling, and managing training data, currently focused on accelerating the development of structured or “dark” data extraction applications for domains in which large labeled training sets are not available or easy to obtain.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://www.snorkel.org/
Source Location	https://github.com/HazyResearch/snorkel
Tag(s)	ML, ML Tool

Streamlit¶

The fastest way to build custom ML tools. Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser.

Documentation on: https://streamlit.io/docs/

SBB License	Apache License 2.0
Core Technology	Javascipt, Python
Project URL	https://streamlit.io/
Source Location	https://github.com/streamlit/streamlit
Tag(s)	ML, ML Framework, ML Hosting, ML Tool, Python

TensorWatch¶

TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data.

TensorWatch is designed to be flexible and extensible so you can also build your own custom visualizations, UIs, and dashboards. Besides traditional “what-you-see-is-what-you-log” approach, it also has a unique capability to execute arbitrary queries against your live ML training process, return a stream as a result of the query and view this stream using your choice of a visualizer (we call this Lazy Logging Mode).

TensorWatch is under heavy development with a goal of providing a platform for debugging machine learning in one easy to use, extensible, and hackable package.

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/microsoft/tensorwatch
Source Location	https://github.com/microsoft/tensorwatch
Tag(s)	ML, ML Tool

VisualDL¶

VisualDL is an open-source cross-framework web dashboard that richly visualizes the performance and data flowing through your neural network training. VisualDL is a deep learning visualization tool that can help design deep learning jobs. It includes features such as scalar, parameter distribution, model structure and image visualization.

SBB License	Apache License 2.0
Core Technology	C++
Project URL	http://visualdl.paddlepaddle.org/
Source Location	https://github.com/PaddlePaddle/VisualDL
Tag(s)	ML, ML Tool

What-If Tool¶

The What-If Tool (WIT) provides an easy-to-use interface for expanding understanding of a black-box ML model. With the plugin, you can perform inference on a large set of examples and immediately visualize the results in a variety of ways. Additionally, examples can be edited manually or programatically and re-run through the model in order to see the results of the changes. It contains tooling for investigating model performance and fairness over subsets of a dataset.

The purpose of the tool is that give people a simple, intuitive, and powerful way to play with a trained ML model on a set of data through a visual interface with absolutely no code required.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://pair-code.github.io/what-if-tool/
Source Location	https ://github.com/tensorflow/tensorboard/tree/mas ter/tensorboard/plugins/interactive_inference
Tag(s)	ML, ML Tool

ML hosting¶

Machine learning needs an infrastructure stack and various components to run. For training and for production. Some open frameworks makes creating ML solutions easier and faster.

BentoML¶

BentoML makes it easy to serve and deploy machine learning models in the cloud.

It is an open source framework for machine learning teams to build cloud-native prediction API services that are ready for production. BentoML supports most popular ML training frameworks and common deployment platforms including major cloud providers and docker/kubernetes.

Documentation on: https://bentoml.readthedocs.io/en/latest/index.html

SBB License	Apache License 2.0
Core Technology	Python
Project URL	http://BentoML.ai
Source Location	https://github.com/bentoml/BentoML
Tag(s)	ML, ML Hosting, Python

Streamlit¶

The fastest way to build custom ML tools. Streamlit lets you create apps for your machine learning projects with deceptively simple Python scripts. It supports hot-reloading, so your app updates live as you edit and save your file. No need to mess with HTTP requests, HTML, JavaScript, etc. All you need is your favorite editor and a browser.

Documentation on: https://streamlit.io/docs/

SBB License	Apache License 2.0
Core Technology	Javascipt, Python
Project URL	https://streamlit.io/
Source Location	https://github.com/streamlit/streamlit
Tag(s)	ML, ML Framework, ML Hosting, ML Tool, Python

RAPIDS¶

The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs–. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

SBB License	Apache License 2.0
Core Technology	C++
Project URL	http://rapids.ai/
Source Location	https://github.com/rapidsai/
Tag(s)	ML, ML Hosting, ML Tool

Acumos AI¶

Acumos AI is a platform and open source framework that makes it easy to build, share, and deploy AI apps. Acumos standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment.

Acumos is a platform which enhances the development, training and deployment of AI models. Its purpose is to scale up the introduction of AI-based software across a wide range of industrial and commercial problems in order to reach a critical mass of applications. In this way, Acumos will drive toward a data-centric process for producing software based upon machine learning as the central paradigm. The platform seeks to empower data scientists to publish more adaptive AI models and shield them from the task of custom development of fully integrated solutions. Ideally, software developers will use Acumos to change the process of software development from a code-writing and editing exercise into a classroom-like code training process in which models will be trained and graded on their ability to successfully analyze datasets that they are fed. Then, the best model can be selected for the job and integrated into a complete application.

Acumos is part of the LF Deep Learning Foundation, an umbrella organization within The Linux Foundation that supports and sustains open source innovation in artificial intelligence, machine learning, and deep learning while striving to make these critical new technologies available to developers and data scientists everywhere.

SBB License	Apache License 2.0
Core Technology	Java
Project URL	https://www.acumos.org/
Source Location	https://gerrit.acumos.org/r/#/admin/projects/
Tag(s)	ML, ML Hosting

Ray¶

Ray is a flexible, high-performance distributed execution framework for AI applications. Ray is currently under heavy development. But Ray has already a good start, with good documentation (http://ray.readthedocs.io/en/latest/index.html) and a tutorial. Also Ray is backed by scientific researchers and published papers.

Ray comes with libraries that accelerate deep learning and reinforcement learning development:

Ray Tune: Hyperparameter Optimization Framework
Ray RLlib: A Scalable Reinforcement Learning Library

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://ray-project.github.io/
Source Location	https://github.com/ray-project/ray
Tag(s)	ML, ML Hosting

Turi¶

Turi Create simplifies the development of custom machine learning models.Turi is OSS machine learning from Apple.

Turi Create simplifies the development of custom machine learning models. You don’t have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	https://github.com/apple/turicreate
Source Location	https://github.com/apple/turicreate
Tag(s)	ML, ML Framework, ML Hosting

NLP Frameworks¶

Natural language processing (NLP) is a field located at the intersection of data science and machine learning (ML). It is focussed on teaching machines how to understand human languages and extract meaning from text. Using good open FOSS NLP software saves you time and has major benefits above using closed solutions.

NLP tools make it simple to handle NLP-related tasks such as document classification, topic modeling, part-of-speech (POS) tagging, word vectors, and sentiment analysis.

AllenNLP¶

An open-source NLP research library, built on PyTorch. AllenNLP is a NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop.

AllenNLP was designed with the following principles:

Hyper-modular and lightweight. Use the parts which you like seamlessly with PyTorch.
Extensively tested and easy to extend. Test coverage is above 90% and the example models provide a template for contributions.
Take padding and masking seriously, making it easy to implement correct models without the pain.
Experiment friendly. Run reproducible experiments from a json specification with comprehensive logging.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	http://allennlp.org/
Source Location	https://github.com/allenai/allennlp
Tag(s)	ML, NLP, Python

Apache OpenNLP¶

The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text.

The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services. OpenNLP also included maximum entropy and perceptron based machine learning.

The goal of the OpenNLP project will be to create a mature toolkit for the abovementioned tasks. An additional goal is to provide a large number of pre-built models for a variety of languages, as well as the annotated text resources that those models are derived from.

SBB License	Apache License 2.0
Core Technology	Java
Project URL	http://opennlp.apache.org/
Source Location	http://opennlp.apache.org/source-code.html
Tag(s)	NLP

Apache Tika¶

The Apache Tika™ toolkit detects and extracts metadata and text from over a thousand different file types (such as PPT, XLS, and PDF). All of these file types can be parsed through a single interface, making Tika useful for search engine indexing, content analysis, translation, and much more.

Several wrappers are available to use Tika in another programming language, such as Julia or Python

SBB License	Apache License 2.0
Core Technology	Java
Project URL	https://tika.apache.org/
Source Location	https://tika.apache.org/
Tag(s)	NLP

BERT¶

BERT, or Bidirectional Encoder Representations from Transformers, is a new method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks.

Our academic paper which describes BERT in detail and provides full results on a number of tasks can be found here: https://arxiv.org/abs/1810.04805.

OSS NLP training models from Google Research.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://github.com/google-research/bert
Source Location	https://github.com/google-research/bert
Tag(s)	NLP

Bling Fire¶

A lightning fast Finite State machine and REgular expression manipulation library. Bling Fire Tokenizer is a tokenizer designed for fast-speed and quality tokenization of Natural Language text. It mostly follows the tokenization logic of NLTK, except hyphenated words are split and a few errors are fixed.

SBB License	MIT License
Core Technology	CPP
Project URL	https://github.com/Microsoft/BlingFire
Source Location	https://github.com/Microsoft/BlingFire
Tag(s)	NLP

ERNIE¶

An Implementation of ERNIE For Language Understanding (including Pre-training models and Fine-tuning tools)

ERNIE 2.0is a continual pre-training framework for language understanding in which pre-training tasks can be incrementally built and learned through multi-task learning. In this framework, different customized tasks can be incrementally introduced at any time. For example, the tasks including named entity prediction, discourse relation recognition, sentence order prediction are leveraged in order to enable the models to learn language representations.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://github.com/PaddlePaddle/ERNIE
Source Location	https://github.com/PaddlePaddle/ERNIE
Tag(s)	NLP, Python

fastText¶

fastText is a library for efficient learning of word representations and sentence classification. Models can later be reduced in size to even fit on mobile devices.

Created by Facebook Opensource, now available for us all. Also used for the new search on StackOverflow, see https://stackoverflow.blog/2019/08/14/crokage-a-new-way-to-search-stack-overflow/

SBB License	MIT License
Core Technology	CPP, Python
Project URL	https://fasttext.cc/
Source Location	https://github.com/facebookresearch/fastText
Tag(s)	NLP

Flair¶

A very simple framework for state-of-the-art NLP. Developed by Zalando Research.

Flair is:

A powerful NLP library. Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification.
Multilingual. Thanks to the Flair community, we support a rapidly growing number of languages. We also now include ‘one model, many languages‘ taggers, i.e. single models that predict PoS or NER tags for input text in various languages.
A text embedding library. Flair has simple interfaces that allow you to use and combine different word and document embeddings, including our proposed Flair embeddings, BERT embeddings and ELMo embeddings.
A Pytorch NLP framework. Our framework builds directly on Pytorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/zalandoresearch/flair
Source Location	https://github.com/zalandoresearch/flair
Tag(s)	ML, NLP, Python

Gensim¶

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/RaRe-Technologies/gensim
Source Location	https://github.com/RaRe-Technologies/gensim
Tag(s)	ML, NLP, Python

Icecaps¶

Microsoft Icecaps is an open-source toolkit for building neural conversational systems. Icecaps provides an array of tools from recent conversation modeling and general NLP literature within a flexible paradigm that enables complex multi-task learning setups.

Background information can be found here https://www.aclweb.org/anthology/P19-3021

SBB License	MIT License
Core Technology	Python
Project URL	https://www.microsoft. com/en-us/research/project/microsoft-icecaps/
Source Location	https://github.com/microsoft/icecaps
Tag(s)	NLP, Python

jiant¶

jiant is a software toolkit for natural language processing research, designed to facilitate work on multitask learning and transfer learning for sentence understanding tasks.

New software for the The General Language Understanding Evaluation (GLUE) benchmark. This software can be used for evaluating, and analyzing natural language understanding systems.

SBB License	MIT License
Core Technology	Python
Project URL	https://jiant.info/
Source Location	https://github.com/nyu-mll/jiant
Tag(s)	NLP, Python, Research

Klassify¶

Redis based text classification service with real-time web interface.

What is Text Classification: Text classification, document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories.

SBB License	MIT License
Core Technology	Python
Project URL	https://github.com/fatiherikli/klassify
Source Location	https://github.com/fatiherikli/klassify
Tag(s)	ML, NLP, Text classification

Neuralcoref¶

State-of-the-art coreference resolution based on neural nets and spaCy.

NeuralCoref is a pipeline extension for spaCy 2.0 that annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy’s NLP pipeline and easily extensible to new training datasets.

SBB License	MIT License
Core Technology	Python
Project URL	https://huggingface.co/coref/
Source Location	https://github.com/huggingface/neuralcoref
Tag(s)	ML, NLP, Python

NLP Architect¶

NLP Architect is an open-source Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration.

Features:

Core NLP models used in many NLP tasks and useful in many NLP applications
Novel NLU models showcasing novel topologies and techniques
Optimized NLP/NLU models showcasing different optimization algorithms on neural NLP/NLU models
Model-oriented design:
- Train and run models from command-line.
- API for using models for inference in python.
- Procedures to define custom processes for training, inference or anything related to processing.
- CLI sub-system for running procedures
Based on optimized Deep Learning frameworks:
- TensorFlow
- PyTorch
- Dynet
Essential utilities for working with NLP models – Text/String pre-processing, IO, data-manipulation, metrics, embeddings.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	http://nlp_architect.nervanasys.com/
Source Location	https://github.com/NervanaSystems/nlp-architect
Tag(s)	ML, ML Tool, NLP, Python

NLTK (Natural Language Toolkit)¶

NLTK is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.

Check also the (free) online Book (OReily published)

SBB License	Apache License 2.0
Core Technology	Python
Project URL	http://www.nltk.org
Source Location	https://github.com/nltk/nltk
Tag(s)	NLP

Pattern¶

Pattern is a web mining module for Python. It has tools for:

Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM parser
Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet
Machine Learning: vector space model, clustering, classification (KNN, SVM, Perceptron)
Network Analysis: graph centrality and visualization.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology	Python
Project URL	https://www.clips.uantwerpen.be/pages/pattern
Source Location	https://github.com/clips/pattern
Tag(s)	ML, NLP, Web scraping

Rant¶

Rant is an all-purpose procedural text engine that is most simply described as the opposite of Regex. It has been refined to include a dizzying array of features for handling everything from the most basic of string generation tasks to advanced dialogue generation, code templating, automatic formatting, and more.

The goal of the project is to enable developers of all kinds to automate repetitive writing tasks with a high degree of creative freedom.

Features:

Recursive, weighted branching with several selection modes
Queryable dictionaries
Automatic capitalization, rhyming, English indefinite articles, and multi-lingual number verbalization
Print to multiple separate outputs
Probability modifiers for pattern elements
Loops, conditional statements, and subroutines
Fully-functional object model
Import/Export resources easily with the .rantpkg format
Compatible with Unity 2017

SBB License	MIT License
Core Technology	.NET
Project URL	https://berkin.me/rant/
Source Location	https://github.com/TheBerkin/rant
Tag(s)	.NET, ML, NLP, text generation

SpaCy¶

Industrial-strength Natural Language Processing (NLP) with Python and Cython

Features:

Non-destructive tokenization
Named entity recognition
Support for 26+ languages
13 statistical models for 8 languages
Pre-trained word vectors
Easy deep learning integration
Part-of-speech tagging
Labelled dependency parsing
Syntax-driven sentence segmentation
Built in visualizers for syntax and NER
Convenient string-to-hash mapping
Export to numpy data arrays
Efficient binary serialization
Easy model packaging and deployment
State-of-the-art speed
Robust, rigorously evaluated accuracy

SBB License	MIT License
Core Technology	Python
Project URL	https://spacy.io/
Source Location	https://github.com/explosion/spaCy
Tag(s)	NLP

Stanford CoreNLP¶

Stanford CoreNLP provides a set of human language technology tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and syntactic dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract particular or open-class relations between entity mentions, get the quotes people said, etc.

Choose Stanford CoreNLP if you need:

An integrated NLP toolkit with a broad range of grammatical analysis tools
A fast, robust annotator for arbitrary texts, widely used in production
A modern, regularly updated package, with the overall highest quality text analytics
Support for a number of major (human) languages
Available APIs for most major modern programming languages
Ability to run as a simple web service

SBB License	GNU General Public License (GPL) 3.0
Core Technology	Java
Project URL	https://stanfordnlp.github.io/CoreNLP/
Source Location	https://github.com/stanfordnlp/CoreNLP
Tag(s)	NLP

Sumeval¶

Well tested & Multi-language evaluation framework for text summarization. Multi-language.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://github.com/chakki-works/sumeval
Source Location	https://github.com/chakki-works/sumeval
Tag(s)	NLP, Python

Texar-PyTorch¶

Texar-PyTorch is a toolkit aiming to support a broad set of machine learning, especially natural language processing and text generation tasks. Texar provides a library of easy-to-use ML modules and functionalities for composing whatever models and algorithms. The tool is designed for both researchers and practitioners for fast prototyping and experimentation.

Texar-PyTorch integrates many of the best features of TensorFlow into PyTorch, delivering highly usable and customizable modules superior to PyTorch native ones.

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://asyml.io/
Source Location	https://github.com/asyml/texar-pytorch
Tag(s)	ML, NLP, Python

TextBlob: Simplified Text Processing¶

TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

Features¶

Noun phrase extraction
Part-of-speech tagging
Sentiment analysis
Classification (Naive Bayes, Decision Tree)
Language translation and detection powered by Google Translate
Tokenization (splitting text into words and sentences)
Word and phrase frequencies
Parsing
n-grams
Word inflection (pluralization and singularization) and lemmatization
Spelling correction
Add new models or languages through extensions
WordNet integration

SBB License	MIT License
Core Technology	Python
Project URL	https://textblob.readthedocs.io/en/dev/
Source Location	https://github.com/sloria/textblob
Tag(s)	NLP, Python

Thinc¶

Thinc is the machine learning library powering spaCy. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0.

Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models.

Thinc is a practical toolkit for implementing models that follow the “Embed, encode, attend, predict” architecture. It’s designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

SBB License	MIT License
Core Technology	Python
Project URL	https://thinc.ai/
Source Location	https://github.com/explosion/thinc
Tag(s)	ML, ML Framework, NLP, Python

Torchtext¶

Data loaders and abstractions for text and NLP. Build on PyTorch.

SBB License	BSD License 2.0 (3-clause, New or Revised) License
Core Technology
Project URL	https://github.com/pytorch/text
Source Location	https://github.com/pytorch/text
Tag(s)	NLP

Transformers¶

Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.

Features:

As easy to use as pytorch-transformers
As powerful and concise as Keras
High performance on NLU and NLG tasks
Low barrier to entry for educators and practitioners

State-of-the-art NLP for everyone:

Deep learning researchers
Hands-on practitioners
AI/ML/NLP teachers and educators

Lower compute costs, smaller carbon footprint

Researchers can share trained models instead of always retraining
Practitioners can reduce compute time and production costs
8 architectures with over 30 pretrained models, some in more than 100 languages

SBB License	Apache License 2.0
Core Technology	Python
Project URL	https://huggingface.co/transformers/
Source Location	https://github.com/huggingface/transformers
Tag(s)	NLP, Python

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2019	2020	2021