Package: clearml
ClearML- Auto-Magical Suite of tools to streamline your AI workflowExperiment Manager, MLOps/LLMOps and Data-ManagementClearMLFormerly known as Allegro TrainsClearML is a ML/DL development and production suite. It contains FIVE main modules:Experiment Manager- Automagical experiment tracking, environments and resultsMLOps / LLMOps- Orchestration, Automation & Pipelines solution for ML/DL/GenAI jobs (Kubernetes / Cloud / bare-metal)Data-Management- Fully differentiable data management & version control solution on top of object-storage
(S3 / GS / Azure / NAS)Model-Serving-cloud-readyScalable model serving solution!Deploy new model endpoints in under 5 minutesIncludes optimized GPU serving support backed by Nvidia-Tritonwith out-of-the-box  Model MonitoringReports- Create and share rich MarkDown documents supporting embeddable online contentNEW:fire:Orchestration Dashboard- Live rich dashboard for your entire compute cluster (Cloud / Kubernetes / On-Prem)Instrumenting these components is theClearML-server, seeSelf-Hosting&Free tier HostingSign up&Start usingin under 2 minutesFriendly tutorials to get you startedStep 1- Experiment ManagementStep 2- Remote Execution Agent SetupStep 3- Remotely Execute TasksExperiment ManagementDatasetsOrchestrationPipelinesClearML Experiment ManagerAdding only 2 lines to your code gets you the followingComplete experiment setup logFull source control info, including non-committed local changesExecution environment (including specific packages & versions)Hyper-parametersargparse/Click/PythonFirefor command line parameters with currently used valuesExplicit parameters dictionaryTensorflow Defines (absl-py)Hydraconfiguration and overridesInitial model weights fileFull experiment output automatic capturestdout and stderrResource Monitoring (CPU/GPU utilization, temperature, IO, network, etc.)Model snapshots (With optional automatic upload to central storage: Shared folder, S3, GS, Azure, Http)Artifacts log & store (Shared folder, S3, GS, Azure, Http)Tensorboard/TensorboardXscalars, metrics, histograms,images, audio and video samplesMatplotlib & SeabornClearML Loggerinterface for complete flexibility.Extensive platform support and integrationsSupported ML/DL frameworks:PyTorch(incl'ignite/lightning),Tensorflow,Keras,AutoKeras,FastAI,XGBoost,LightGBM,MegEngineandScikit-LearnSeamless integration (including version control) withJupyter NotebookandPyCharmremote debuggingStart using ClearMLSign up for free to theClearML Hosted Service(alternatively, you can set up your own server, seehere).ClearML Demo Server:ClearML no longer uses the demo server by default. To enable the demo server, set theCLEARML_NO_DEFAULT_SERVER=0environment variable. Credentials aren't needed, but experiments launched to the demo server are public, so make sure not
to launch sensitive experiments if using the demo server.Install theclearmlpython package:pipinstallclearmlConnect the ClearML SDK to the server bycreating credentials, then execute the command
below and follow the instructions:clearml-initAdd two lines to your code:fromclearmlimportTasktask=Task.init(project_name='examples',task_name='hello world')And you are done! Everything your process outputs is now automagically logged into ClearML.Next step, automation!Learn more about ClearML's two-click automationhere.ClearML ArchitectureThe ClearML run-time components:The ClearML Python Package - for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments and other workflows with ClearML's powerful and versatile set of classes and methods.The ClearML Server - for storing experiment, model, and workflow data; supporting the Web UI experiment manager and MLOps automation for reproducibility and tuning. It is available as a hosted service and open source for you to deploy your own ClearML Server.The ClearML Agent - for MLOps orchestration, experiment and workflow reproducibility, and scalability.Additional Modulesclearml-session-Launch remote JupyterLab / VSCode-server inside any docker, on Cloud/On-Prem machinesclearml-task- Run any codebase on remote machines with full remote logging of Tensorboard, Matplotlib & Console outputsclearml-data-CLI for managing and versioning your datasets, including creating / uploading / downloading of data from S3/GS/Azure/NASAWS Auto-Scaler- Automatically spin EC2 instances based on your workloads with preconfigured budget! No need for AKE!Hyper-Parameter Optimization- Optimize any code with black-box approach and state-of-the-art Bayesian optimization algorithmsAutomation Pipeline- Build pipelines based on existing experiments / jobs, supports building pipelines of pipelines!Slack Integration- Report experiments progress / failure directly to Slack (fully customizable!)Why ClearML?ClearML is our solution to a problem we share with countless other researchers and developers in the machine
learning/deep learning universe: Training production-grade deep learning models is a glorious but messy process.
ClearML tracks and controls the process by associating code version control, research projects,
performance metrics, and model provenance.We designed ClearML specifically to require effortless integration so that teams can preserve their existing methods
and practices.Use it on a daily basis to boost collaboration and visibility in your teamCreate a remote job from any experiment with a click of a buttonAutomate processes and create pipelines to collect your experimentation logs, outputs, and dataStore all your data on any object-storage solution, with the most straightforward interface possibleMake your data transparent by cataloging it all on the ClearML platformWe believe ClearML is ground-breaking. We wish to establish new standards of true seamless integration between
experiment management, MLOps, and data management.Who We AreClearML is supported by you and theclear.mlteam, which helps enterprise companies build scalable MLOps.We built ClearML to track and control the glorious but messy process of training production-grade deep learning models.
We are committed to vigorously supporting and expanding the capabilities of ClearML.We promise to always be backwardly compatible, making sure all your logs, data, and pipelines will always upgrade with you.LicenseApache License, Version 2.0 (see theLICENSEfor more information)If ClearML is part of your development process / project / publication, please cite us :heart: :@misc{clearml,
title = {ClearML - Your entire MLOps stack in one open-source tool},
year = {2023},
note = {Software available from http://github.com/allegroai/clearml},
url={https://clear.ml/},
author = {ClearML},
}Documentation, Community & SupportFor more information, see theofficial documentationandon YouTube.For examples and use cases, check theexamples folderandcorresponding documentation.If you have any questions: post on ourSlack Channel, or tag your questions onstackoverflowwith 'clearml' tag (previouslytrainstag).For feature requests or bug reports, please useGitHub issues.Additionally, you can always find us atinfo@clear.mlContributingPRs are always welcome:heart: See more details in the ClearMLGuidelines for Contributing.May the force (and the goddess of learning rates) be with you!