Package: aimos
Drop a star to support AimOS ⭐Join AimOS discord communityOpen-source modular observability for AI Systems.Easily log, connect and observe any parts of your AI Systems from experiments to production to prompts to AI system monitoring.SEAMLESSLY INTEGRATES WITH:AimStack offers enterprise support that's beyond core AimOS. Contact viahello@aimstack.ioe-mail.About•Demos•Default logging apps•Quick Start•Examples•Community•Blog•ℹ️ AboutAimOS is an open-source operating system for logs.
With AimOS you can build, run and combine any kind of logging applications - experiment tracking, production monitoring, AI System (LLM-based) monitoring, usage monitoring etc.The Logging applications are typically a combination of these components:The types and relationships of the data being loggedThe observability UI over the data loggedAutomations over the data loggedAimOS comes installed with a number of default logging apps:Base App - a basic generic log exploration and the logging primitivesAI Experiment Tracking App - log and explore your machine learning experiments. Includes integrations with the majority of leading ML frameworks.AI Systems Tracing and Debugging Apps - a combination of variety of apps that log from langchain to llamaindex traces all in one place.Apart from running the logging apps, AimOS comes with explorers and reports.Explorers are advanced logs comparison tools for specific kind of logs - they allow to compare 1000s of sessions of metrics, images, text, audio and other types of data.Reports are embedded knowledge-base that operate with the apps and explorers seamlessly to enable capture the knowledge built on top of the logged  data from the observations through AimOS apps and explorers.With the rise of AI Systems and the challenges it brings forward, logging apps are going to be a crucial part of the software.Our mission is to democratize developer tools for building AI.Base AppA general observability over anything logged with AimOS.Visualize all the logs ever logged with AimOS for the given project 🗺️Base types to log common artifacts such as Images, Audio objects, Figures, MetricsHigh-level overview of the logs, the types logged and the respective sessions/ containersDeep-dive into each type of the logExperiment Tracking AppLog Metadata Across Your ML Pipeline 💾Visualize & Compare Metadata via UI 📊ML experiments and any metadata trackingIntegration with popular ML frameworksEasy migration from other experiment trackersMetadata visualization via AimOS ExplorersGrouping and aggregationQuerying using Python expressionsRun ML Trainings Effectively ⚡Organize Your Experiments 🗂️System info and resource usage trackingReal-time alerting on training progress (upcoming)Detailed run information for easy debuggingCentralized dashboard for holistic viewAI Systems Tracing AppsLog Inputs, Outputs and Actions of Executions 🤖Visualize & Compare Executions Steps via UI 🔍Track all the prompts, generations of LLMsTrack all the inputs, outputs of toolsCapture chains metadataDeep dive into single execution stepsCompare executions side-by-side🎬 DemosCheck out live AimOS demos NOW to see it in action.Tracing LangChain-based chatbot executionsView Demo|View CodeTracing LlamaIndex query executionsView Demo|View CodeTracking PyTorch-based CNN trainingsView Demo|View Code🌍 Default logging appsAimOS comes pre-installed with a wide variety of apps. Here is the full list:App NameDescriptionCategoryDocsSourcebaseBase AimOS app for general observability over anything logged with AimOS. Includes base types to log common artifacts, such as Image, Audio object, Figure, Metric.BasedocssourcedocsUse this AimOS app to access AimOS docs.Docs-sourcelangchain_debuggerDebugger for LangChain that logs LLMs prompts and generations, tools inputs/outputs, and chains metadata.AI Systems Tracingdocssourcellamaindex_observerDebugger and observer for LlamaIndex. Logs metadata like retrieval nodes, queries and responses, embeddings chunks, etc.AI Systems Tracingdocssourceexperiment_trackerApp for tracking and exploring ML experiments. Integrations with various ML libraries, including Acme, CatBoost, fastai, Hugging Face Transformers, Keras, Keras Tuner, LightGBM, MXNet, Optuna, PaddlePaddle, PyTorch Ignite, SDB3, and XGBoost.Experiment Trackingdocssource🏁 Quick startFollow the steps below to get started with AimOS.1. Install AimOS on your training environmentpip3installaimos2. Integrate AimOS with your codefromaimstack.baseimportRun,Metric# Initialize a new runrun=Run()# Log run parametersrun["hparams"]={"learning_rate":0.001,"batch_size":32,}# Init a metricmetric=Metric(run,name='loss',context={'subset':'training'})foriinrange(1000):metric.track(i,epoch=1)3. Start AimOS serveraimosserver4. Start AimOS UIaimosui👥 CommunityAimOS README badgeAdd AimOS badge to your README, if you've enjoyed using AimOS in your work:[![AimOS](https://img.shields.io/badge/powered%20by-AimOS-%231473E6)](https://github.com/aimhubio/aimos)Contributing to AimOSConsidering contibuting to AimOS?
To get started, please take a moment to read theCONTRIBUTING.mdguide.Join AimOS contributors by submitting your first pull request. Happy coding! 😊Made withcontrib.rocks.More questions?Open a feature request or report a bugJoin Discord community server