Awesome Open Source MLOps

An awesome & curated list of best open source MLOps tools for data scientists.

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• Table of Contents
• Training
  • IDEs and Workspaces
  • Frameworks for Training
  • Experiment Tracking
  • Visualization
• Model
  • Model Management
  • Pretrained Model
• Serving
  • Frameworks for Serving
  • Optimizations
  • Observability
• Large Scale Deployment
  • ML Platforms
  • Workflow
  • Scheduling
• AutoML
• Data
  • Data Management
  • Data Ingestion
  • Data Storage
  • Data Transformation
  • Feature Engineering
• Performance
  • ML Compiler
  • Profiling

• code server - 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.

• 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.
• Jax - Autograd and XLA for high-performance machine learning research.
• Kedro - Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code.
• Keras - 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 - MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
• MXNet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler.
• Oneflow - 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.
• PyTorchLightning - The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.
• 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.

• 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.

• 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 - 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.

• dvc - Data Version Control | Git for Data & Models | ML Experiments Management
• ModelDB - Open Source ML Model Versioning, Metadata, and Experiment Management
• ormb - Docker for Your ML/DL Models Based on OCI Artifacts

• 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 - PyTorch image models, scripts, pretrained weights.

• 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 - Multi Model Server is a tool for serving neural net models for inference.
• Service Streamer - Boosting your Web Services of Deep Learning Applications.
• TFServing - A flexible, high-performance serving system for machine learning models.
• Triton Server (TRTIS) - The Triton Inference Server provides an optimized cloud and edge inferencing solution.

• 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.

• 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.
• Kubeflow - Machine Learning Toolkit for Kubernetes.
• PAI - Resource scheduling and cluster management for AI.
• Polyaxon - Machine Learning Management & Orchestration Platform.

• Argo - 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 - MLOps framework to create reproducible pipelines.

• Kueue - Kubernetes-native Job Queueing.
• PAI - Resource scheduling and cluster management for AI (Open-sourced by Microsoft).
• Slurm - A Highly Scalable Workload Manager.
• Volcano - A Cloud Native Batch System (Project under CNCF).
• Yunikorn - 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 - 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
• AutoGluon - AutoML for Image, Text, and Tabular Data.
• automl-gs - Provide an input CSV and a target field to predict, generate a model + code to run it.
• autokeras - AutoML library for deep learning.
• Auto-PyTorch - Automatic architecture search and hyperparameter optimization for PyTorch.
• auto-sklearn - an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator.
• AutoWeka - hyperparameter search for Weka.
• Chocolate - A fully decentralized hyperparameter optimization framework.
• Dragonfly - An open source python library for scalable Bayesian optimisation.
• Determined - 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.
• FEDOT - AutoML framework for the design of composite pipelines.
• FLAML - Fast and lightweight AutoML (paper).
• Goptuna - A hyperparameter optimization framework, inspired by Optuna.
• HpBandSter - a framework for distributed hyperparameter optimization.
• HPOlib2 - a library for hyperparameter optimization and black box optimization benchmarks.
• 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 - Hyperparameter tuning for humans.
• learn2learn - PyTorch Meta-learning Framework for Researchers.
• Ludwig - a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code.
• 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 - 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.
• Ray Tune - Scalable Hyperparameter Tuning.
• REMBO - Bayesian optimization in high-dimensions via random embedding.
• RoBO - a Robust Bayesian Optimization framework.
• scikit-optimize(skopt) - Sequential model-based optimization with a scipy.optimize interface.
• Spearmint - a software package to perform Bayesian optimization.
• TPOT - one of the very first AutoML methods and open-source software packages.
• Torchmeta - A Meta-Learning library for PyTorch.
• Vegas - an AutoML algorithm tool chain by Huawei Noah's Arb Lab.

• Dolt - Git for Data.
• 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.
• Quilt - A self-organizing data hub for S3.

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

• FeatureTools - An open source python framework for automated feature engineering

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