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awesome-open-source-mlops

An awesome & curated list of best open source MLOps tools for data scientists.
https://github.com/gaocegege/awesome-open-source-mlops

Last synced: 3 days ago
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  • IDEs and Workspaces

    • code server - server.svg?style=social) - Run VS Code on any machine anywhere and access it in the browser.
    • conda - OS-agnostic, system-level binary package manager and ecosystem.
    • Docker - Moby is an open-source project created by Docker to enable and accelerate software containerization.
    • Jupyter Notebooks - The Jupyter notebook is a web-based notebook environment for interactive computing.

  • Frameworks for Training

    • Caffe - A fast open framework for deep learning.
    • ColossalAI - An integrated large-scale model training system with efficient parallelization techniques.
    • DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
    • Horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
    • Kedro - org/kedro.svg?style=social) - Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code.
    • Keras - team/keras.svg?style=social) - Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow.
    • LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
    • MegEngine - MegEngine is a fast, scalable and easy-to-use deep learning framework, with auto-differentiation.
    • MindSpore - ai/mindspore.svg?style=social) - MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
    • MXNet - mxnet.svg?style=social) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler.
    • Oneflow - Inc/oneflow.svg?style=social) - OneFlow is a performance-centered and open-source deep learning framework.
    • PaddlePaddle - Machine Learning Framework from Industrial Practice.
    • PyTorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration.
    • XGBoost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library.
    • TensorFlow - An Open Source Machine Learning Framework for Everyone.
    • VectorFlow - A minimalist neural network library optimized for sparse data and single machine environments.
    • PyTorchLightning - lightning.svg?style=social) - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
    • Jax - Autograd and XLA for high-performance machine learning research.

  • Experiment Tracking

    • Aim - an easy-to-use and performant open-source experiment tracker.
    • Guild AI - Experiment tracking, ML developer tools.
    • MLRun - Machine Learning automation and tracking.
    • Kedro-Viz - Kedro-Viz is an interactive development tool for building data science pipelines with Kedro. Kedro-Viz also allows users to view and compare different runs in the Kedro project.
    • LabNotebook - LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
    • Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments.

  • Visualization

    • Maniford - A model-agnostic visual debugging tool for machine learning.
    • netron - Visualizer for neural network, deep learning, and machine learning models.
    • TensorBoard - TensorFlow's Visualization Toolkit.
    • TensorSpace - team/tensorspace.svg?style=social) - Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js.
    • dtreeviz - A python library for decision tree visualization and model interpretation.
    • Zetane Viewer - ML models and internal tensors 3D visualizer.
    • OpenOps - Bring multiple data streams into one dashboard.

  • Model Management

    • ModelDB - Open Source ML Model Versioning, Metadata, and Experiment Management
    • ormb - Docker for Your ML/DL Models Based on OCI Artifacts

  • Pretrained Model

    • HuggingFace - State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
    • PaddleNLP - Easy-to-use and Fast NLP library with awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications.
    • PyTorch Image Models - image-models.svg?style=social) - PyTorch image models, scripts, pretrained weights.

  • Frameworks/Servers for Serving

    • BentoML - The Unified Model Serving Framework
    • ForestFlow - Policy-driven Machine Learning Model Server.
    • MOSEC - A machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.
    • Multi Model Server - model-server.svg?style=social) - Multi Model Server is a tool for serving neural net models for inference.
    • Neuropod - A uniform interface to run deep learning models from multiple frameworks
    • Pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
    • Service Streamer - streamer.svg?style=social) - Boosting your Web Services of Deep Learning Applications.
    • TFServing - A flexible, high-performance serving system for machine learning models.
    • Torchserve - Serve, optimize and scale PyTorch models in production
    • Triton Server (TRTIS) - inference-server/server.svg?style=social) - The Triton Inference Server provides an optimized cloud and edge inferencing solution.

  • Optimizations

    • FeatherCNN - FeatherCNN is a high performance inference engine for convolutional neural networks.
    • Forward - A library for high performance deep learning inference on NVIDIA GPUs.
    • NCNN - ncnn is a high-performance neural network inference framework optimized for the mobile platform.
    • PocketFlow - use AutoML to do model compression.
    • TNN - A uniform deep learning inference framework for mobile, desktop and server.

  • ML Platforms

    • ClearML - Auto-Magical CI/CD to streamline your ML workflow. Experiment Manager, MLOps and Data-Management.
    • MLflow - Open source platform for the machine learning lifecycle.
    • Kserve - Standardized Serverless ML Inference Platform on Kubernetes
    • Kubeflow - Machine Learning Toolkit for Kubernetes.
    • Polyaxon - Machine Learning Management & Orchestration Platform.
    • Seldon-core - core.svg?style=social) - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

  • Workflow

    • Argo - workflows.svg?style=social) - Workflow engine for Kubernetes.
    • Flyte - Kubernetes-native workflow automation platform for complex, mission-critical data and ML processes at scale.
    • Kubeflow - Machine Learning Pipelines for Kubeflow.
    • Metaflow - Build and manage real-life data science projects with ease!
    • ZenML - io/zenml.svg?style=social) - MLOps framework to create reproducible pipelines.

  • Scheduling

    • Kueue - sigs/kueue.svg?style=social) - Kubernetes-native Job Queueing.
    • Slurm - A Highly Scalable Workload Manager.
    • Volcano - sh/volcano.svg?style=social) - A Cloud Native Batch System (Project under CNCF).
    • Yunikorn - core.svg?style=social) - Light-weight, universal resource scheduler for container orchestrator systems.
    • Adanet - Tensorflow package for AdaNet.
    • Advisor - open-source implementation of Google Vizier for hyper parameters tuning.
    • Archai - a platform for Neural Network Search (NAS) that allows you to generate efficient deep networks for your applications.
    • auptimizer - ARC-AdvancedAI/auptimizer.svg?style=social) - An automatic ML model optimization tool.
    • autoai - A framework to find the best performing AI/ML model for any AI problem.
    • AutoGL - An autoML framework & toolkit for machine learning on graphs
    • automl-gs - gs.svg?style=social) - Provide an input CSV and a target field to predict, generate a model + code to run it.
    • autokeras - team/autokeras.svg?style=social) - AutoML library for deep learning.
    • Auto-PyTorch - PyTorch.svg?style=social) - Automatic architecture search and hyperparameter optimization for PyTorch.
    • auto-sklearn - sklearn.svg?style=social) - an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
    • AutoWeka - hyperparameter search for Weka.
    • Chocolate - Labs/chocolate.svg?style=social) - A fully decentralized hyperparameter optimization framework.
    • Dragonfly - An open source python library for scalable Bayesian optimisation.
    • Determined - ai/determined.svg?style=social) - scalable deep learning training platform with integrated hyperparameter tuning support; includes Hyperband, PBT, and other search methods.
    • DEvol (DeepEvolution) - a basic proof of concept for genetic architecture search in Keras.
    • EvalML - An open source python library for AutoML.
    • FLAML - Fast and lightweight AutoML ([paper](https://www.microsoft.com/en-us/research/publication/flaml-a-fast-and-lightweight-automl-library/)).
    • Goptuna - bata/goptuna.svg?style=social) - A hyperparameter optimization framework, inspired by Optuna.
    • HpBandSter - a framework for distributed hyperparameter optimization.
    • Hyperband - open source code for tuning hyperparams with Hyperband.
    • Hypernets - A General Automated Machine Learning Framework.
    • Hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python.
    • hyperunity - A toolset for black-box hyperparameter optimisation.
    • Katib - Katib is a Kubernetes-native project for automated machine learning (AutoML).
    • Keras Tuner - team/keras-tuner.svg?style=social) - Hyperparameter tuning for humans.
    • learn2learn - PyTorch Meta-learning Framework for Researchers.
    • MOE - a global, black box optimization engine for real world metric optimization by Yelp.
    • Model Search - a framework that implements AutoML algorithms for model architecture search at scale.
    • NASGym - env.svg?style=social) - a proof-of-concept OpenAI Gym environment for Neural Architecture Search (NAS).
    • NNI - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
    • Optuna - A hyperparameter optimization framework.
    • REMBO - Bayesian optimization in high-dimensions via random embedding.
    • RoBO - a Robust Bayesian Optimization framework.
    • scikit-optimize(skopt) - optimize/scikit-optimize.svg?style=social) - Sequential model-based optimization with a `scipy.optimize` interface.
    • Spearmint - a software package to perform Bayesian optimization.
    • Torchmeta - meta.svg?style=social) - A Meta-Learning library for PyTorch.
    • Vegas - noah/vega.svg?style=social) - an AutoML algorithm tool chain by Huawei Noah's Arb Lab.
    • TPOT - one of the very first AutoML methods and open-source software packages.
    • PAI - Resource scheduling and cluster management for AI (Open-sourced by Microsoft).
    • AutoGluon - AutoML for Image, Text, and Tabular Data.
    • FEDOT - itmo/FEDOT.svg?style=social) - AutoML framework for the design of composite pipelines.
    • HPOlib2 - a library for hyperparameter optimization and black box optimization benchmarks.

  • Data Management

    • Dolt - Git for Data.
    • Quilt - A self-organizing data hub for S3.
    • DVC - Data Version Control | Git for Data & Models | ML Experiments Management.
    • Hub - Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size.

  • Data Storage

    • LakeFS - Git-like capabilities for your object storage.

  • Data & Feature enrichment

    • Upgini - Free automated data & feature enrichment library for machine learning: automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features
Programming Languages
Python 61 C++ 16 Go 12 Jupyter Notebook 7 JavaScript 5 Java 4 TypeScript 3 Matlab 1 C 1 HTML 1
Categories
Scheduling 46 Frameworks for Training 18 Frameworks/Servers for Serving 10 Visualization 7 Experiment Tracking 6 ML Platforms 6 Optimizations 5 Workflow 5 Data Management 4 IDEs and Workspaces 4 Pretrained Model 3 Model Management 2 Data Storage 1 Data & Feature enrichment 1
Sub Categories
Keywords
machine-learning 67 deep-learning 44 python 34 pytorch 30 tensorflow 28 automl 23 data-science 20 mlops 19 hyperparameter-optimization 17 kubernetes 16 ai 15 keras 13 ml 13 neural-architecture-search 10 neural-network 10 automated-machine-learning 9 hyperparameter-tuning 9 gpu 9 inference 9 llm 8 scikit-learn 7 bayesian-optimization 7 mxnet 6 artificial-intelligence 6 golang 5 k8s 5 optimization 5 distributed 5 workflow 5 distributed-training 5 model-serving 5 data-version-control 5 visualization 5 data-engineering 5 computer-vision 5 xgboost 4 caffe 4 jax 4 serving 4 kubeflow 4 onnx 4 experiment-tracking 4 model-management 4 machinelearning 4 deeplearning 4 jupyter 4 nas 3 feature-engineering 3 docker 3 notebook 3