{"id":24169682,"url":"https://github.com/clearml/clearml","last_synced_at":"2026-04-02T01:35:27.817Z","repository":{"id":37419535,"uuid":"191126383","full_name":"clearml/clearml","owner":"clearml","description":"ClearML - Auto-Magical CI/CD to streamline your AI workload. 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MLOps / LLMOps \u0026 Production"],"sub_categories":["General-Purpose Machine Learning"],"readme":"\u003cdiv align=\"center\" style=\"text-align: center\"\u003e\n\n\u003cp style=\"text-align: center\"\u003e\n  \u003cimg align=\"center\" src=\"docs/clearml-logo.svg#gh-light-mode-only\" alt=\"Clear|ML\"\u003e\u003cimg align=\"center\" src=\"docs/clearml-logo-dark.svg#gh-dark-mode-only\" alt=\"Clear|ML\"\u003e\n\u003c/p\u003e\n\n**[ClearML](https://clear.ml) - Auto-Magical Suite of tools to streamline your AI workflow\n\u003c/br\u003eExperiment Manager, MLOps/LLMOps and Data-Management**\n\n[![GitHub license](https://img.shields.io/github/license/clearml/clearml.svg)](https://img.shields.io/github/license/clearml/clearml.svg) [![PyPI pyversions](https://img.shields.io/pypi/pyversions/clearml.svg)](https://img.shields.io/pypi/pyversions/clearml.svg) [![PyPI version shields.io](https://img.shields.io/pypi/v/clearml.svg)](https://pypi.org/project/clearml/) [![Conda version shields.io](https://img.shields.io/conda/v/clearml/clearml)](https://anaconda.org/clearml/clearml) [![Optuna](https://img.shields.io/badge/Optuna-integrated-blue)](https://optuna.org)\u003cbr\u003e\n[![PyPI Downloads](https://static.pepy.tech/badge/clearml/month)](https://pypi.org/project/clearml/) [![Artifact Hub](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/clearml)](https://artifacthub.io/packages/search?repo=clearml) [![Youtube](https://img.shields.io/badge/ClearML-DD0000?logo=youtube\u0026logoColor=white)](https://www.youtube.com/c/clearml) [![Slack Channel](https://img.shields.io/badge/slack-%23clearml--community-blueviolet?logo=slack)](https://joinslack.clear.ml) [![Signup](https://img.shields.io/badge/Clear%7CML-Signup-brightgreen)](https://app.clear.ml)\n\n\n`🌟 ClearML is open-source - Leave a star to support the project! 🌟`\n\n\u003c/div\u003e\n\n---\n### ClearML\n\nClearML is a ML/DL development and production suite. It contains FIVE main modules:\n\n- [Experiment Manager](#clearml-experiment-manager) - Automagical experiment tracking, environments and results\n- [MLOps / LLMOps](https://github.com/clearml/clearml-agent) - Orchestration, Automation \u0026 Pipelines solution for ML/DL/GenAI jobs (Kubernetes / Cloud / bare-metal)  \n- [Data-Management](https://github.com/clearml/clearml/blob/master/docs/datasets.md) - Fully differentiable data management \u0026 version control solution on top of object-storage \n  (S3 / GS / Azure / NAS)  \n- [Model-Serving](https://github.com/clearml/clearml-serving) - *cloud-ready* Scalable model serving solution! \n  - **Deploy new model endpoints in under 5 minutes** \n  - Includes optimized GPU serving support backed by Nvidia-Triton \n  - **with out-of-the-box  Model Monitoring** \n- [Reports](https://clear.ml/docs/latest/docs/webapp/webapp_reports) - Create and share rich MarkDown documents supporting embeddable online content \n- :fire: [Orchestration Dashboard](https://clear.ml/docs/latest/docs/webapp/webapp_orchestration_dash/) - Live rich dashboard for your entire compute cluster (Cloud / Kubernetes / On-Prem)\n- 🔥 💥 [Fractional GPUs](https://github.com/clearml/clearml-fractional-gpu) - Container based, driver level GPU memory limitation 🙀 !!!\n  \n\nInstrumenting these components is the **ClearML-server**, see [Self-Hosting](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server) \u0026 [Free tier Hosting](https://app.clear.ml)  \n\n\n---\n\u003cdiv align=\"center\"\u003e\n\n**[Sign up](https://app.clear.ml)  \u0026  [Start using](https://clear.ml/docs/) in under 2 minutes**\n\n---\n**Friendly tutorials to get you started**\n\n\u003ctable\u003e\n\u003ctbody\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003ca href=\"https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb\"\u003e\u003cb\u003eStep 1\u003c/b\u003e\u003c/a\u003e - Experiment Management\u003c/td\u003e\n    \u003ctd\u003e\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_1_Experiment_Management.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003ca href=\"https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb\"\u003e\u003cb\u003eStep 2\u003c/b\u003e\u003c/a\u003e - Remote Execution Agent Setup\u003c/td\u003e\n    \u003ctd\u003e\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_2_Setting_Up_Agent.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003ca href=\"https://github.com/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb\"\u003e\u003cb\u003eStep 3\u003c/b\u003e\u003c/a\u003e - Remotely Execute Tasks\u003c/td\u003e\n    \u003ctd\u003e\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/clearml/clearml/blob/master/docs/tutorials/Getting_Started_3_Remote_Execution.ipynb\"\u003e\n  \u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\n\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003c/div\u003e\n\n---\n\n\u003ctable\u003e\n\u003ctbody\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eExperiment Management\u003c/td\u003e\n    \u003ctd\u003eDatasets\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003ca href=\"https://app.clear.ml\"\u003e\u003cimg src=\"https://github.com/clearml/clearml/blob/master/docs/experiment_manager.gif?raw=true\" width=\"100%\"\u003e\u003c/a\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003ca href=\"https://app.clear.ml/datasets\"\u003e\u003cimg src=\"https://github.com/clearml/clearml/blob/master/docs/datasets.gif?raw=true\" width=\"100%\"\u003e\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd colspan=\"2\" height=\"24px\"\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eOrchestration\u003c/td\u003e\n    \u003ctd\u003ePipelines\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003e\u003ca href=\"https://app.clear.ml/workers-and-queues/autoscalers\"\u003e\u003cimg src=\"https://github.com/clearml/clearml/blob/master/docs/orchestration.gif?raw=true\" width=\"100%\"\u003e\u003c/a\u003e\u003c/td\u003e\n    \u003ctd\u003e\u003ca href=\"https://app.clear.ml/pipelines\"\u003e\u003cimg src=\"https://github.com/clearml/clearml/blob/master/docs/pipelines.gif?raw=true\" width=\"100%\"\u003e\u003c/a\u003e\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\n## ClearML Experiment Manager\n\n**Adding only 2 lines to your code gets you the following**\n\n* Complete experiment setup log\n    * Full source control info, including non-committed local changes\n    * Execution environment (including specific packages \u0026 versions)\n    * Hyper-parameters\n        * [`argparse`](https://docs.python.org/3/library/argparse.html)/[Click](https://github.com/pallets/click/)/[PythonFire](https://github.com/google/python-fire) for command line parameters with currently used values\n        * Explicit parameters dictionary\n        * Tensorflow Defines (absl-py)\n        * [Hydra](https://github.com/facebookresearch/hydra) configuration and overrides\n    * Initial model weights file\n* Full experiment output automatic capture\n    * stdout and stderr\n    * Resource Monitoring (CPU/GPU utilization, temperature, IO, network, etc.)\n    * Model snapshots (With optional automatic upload to central storage: Shared folder, S3, GS, Azure, Http)\n    * Artifacts log \u0026 store (Shared folder, S3, GS, Azure, Http)\n    * Tensorboard/[TensorboardX](https://github.com/clearml/clearml/tree/master/examples/frameworks/tensorboardx) scalars, metrics, histograms, **images, audio and video samples**\n    * [Matplotlib \u0026 Seaborn](https://github.com/clearml/clearml/tree/master/examples/frameworks/matplotlib)\n    * [ClearML Logger](https://clear.ml/docs/latest/docs/fundamentals/logger) interface for complete flexibility.\n* Extensive platform support and integrations\n    * Supported ML/DL frameworks: [PyTorch](https://github.com/clearml/clearml/tree/master/examples/frameworks/pytorch) (incl' [ignite](https://github.com/clearml/clearml/tree/master/examples/frameworks/ignite) / [lightning](https://github.com/clearml/clearml/tree/master/examples/frameworks/pytorch-lightning)), [Tensorflow](https://github.com/clearml/clearml/tree/master/examples/frameworks/tensorflow), [Keras](https://github.com/clearml/clearml/tree/master/examples/frameworks/keras), [AutoKeras](https://github.com/clearml/clearml/tree/master/examples/frameworks/autokeras), [FastAI](https://github.com/clearml/clearml/tree/master/examples/frameworks/fastai), [XGBoost](https://github.com/clearml/clearml/tree/master/examples/frameworks/xgboost), [LightGBM](https://github.com/clearml/clearml/tree/master/examples/frameworks/lightgbm), [MegEngine](https://github.com/clearml/clearml/tree/master/examples/frameworks/megengine) and [Scikit-Learn](https://github.com/clearml/clearml/tree/master/examples/frameworks/scikit-learn)\n    * Seamless integration (including version control) with [**Jupyter Notebook**](https://jupyter.org/)\n    and [*PyCharm* remote debugging](https://github.com/clearml/trains-pycharm-plugin)\n      \n#### [Start using ClearML](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps) \n\n\n1. Sign up for free to the [ClearML Hosted Service](https://app.clear.ml) (alternatively, you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server)).\n\n    \u003e **_ClearML Demo Server:_** ClearML no longer uses the demo server by default. To enable the demo server, set the `CLEARML_NO_DEFAULT_SERVER=0`\n    \u003e environment variable. Credentials aren't needed, but experiments launched to the demo server are public, so make sure not \n    \u003e to launch sensitive experiments if using the demo server.\n\n1. Install the `clearml` python package:\n\n    ```bash\n    pip install clearml\n    ```\n\n1. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration), then execute the command\nbelow and follow the instructions: \n\n    ```bash\n    clearml-init\n    ```\n\n1. Add two lines to your code:\n    ```python\n    from clearml import Task\n    task = Task.init(project_name='examples', task_name='hello world')\n    ```\n\nAnd you are done! Everything your process outputs is now automagically logged into ClearML.\n\nNext step, automation! **Learn more about ClearML's two-click automation [here](https://clear.ml/docs/latest/docs/getting_started/mlops/mlops_first_steps)**. \n\n## ClearML Architecture\n\nThe ClearML run-time components:\n\n* 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.\n* 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.\n* The ClearML Agent - for MLOps orchestration, experiment and workflow reproducibility, and scalability.\n\n\u003cimg src=\"https://raw.githubusercontent.com/clearml/clearml-docs/main/docs/img/clearml_architecture.png\" width=\"100%\" alt=\"clearml-architecture\"\u003e\n\n## Additional Modules \n\n- [clearml-session](https://github.com/clearml/clearml-session) - **Launch remote JupyterLab / VSCode-server inside any docker, on Cloud/On-Prem machines**\n- [clearml-task](https://github.com/clearml/clearml/blob/master/docs/clearml-task.md) - Run any codebase on remote machines with full remote logging of Tensorboard, Matplotlib \u0026 Console outputs \n- [clearml-data](https://github.com/clearml/clearml/blob/master/docs/datasets.md) - **CLI for managing and versioning your datasets, including creating / uploading / downloading of data from S3/GS/Azure/NAS** \n- [AWS Auto-Scaler](https://clear.ml/docs/latest/docs/guides/services/aws_autoscaler) - Automatically spin EC2 instances based on your workloads with preconfigured budget! No need for AKE!\n- [Hyper-Parameter Optimization](https://clear.ml/docs/latest/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt) - Optimize any code with black-box approach and state-of-the-art Bayesian optimization algorithms\n- [Automation Pipeline](https://clear.ml/docs/latest/docs/guides/pipeline/pipeline_controller) - Build pipelines based on existing experiments / jobs, supports building pipelines of pipelines!  \n- [Slack Integration](https://clear.ml/docs/latest/docs/guides/services/slack_alerts) - Report experiments progress / failure directly to Slack (fully customizable!)  \n\n## Why ClearML?\n\nClearML is our solution to a problem we share with countless other researchers and developers in the machine\nlearning/deep learning universe: Training production-grade deep learning models is a glorious but messy process.\nClearML tracks and controls the process by associating code version control, research projects,\nperformance metrics, and model provenance.\n\nWe designed ClearML specifically to require effortless integration so that teams can preserve their existing methods\nand practices. \n\n  - Use it on a daily basis to boost collaboration and visibility in your team \n  - Create a remote job from any experiment with a click of a button\n  - Automate processes and create pipelines to collect your experimentation logs, outputs, and data\n  - Store all your data on any object-storage solution, with the most straightforward interface possible\n  - Make your data transparent by cataloging it all on the ClearML platform\n\nWe believe ClearML is ground-breaking. We wish to establish new standards of true seamless integration between\nexperiment management, MLOps, and data management.\n\n## Who We Are\n\nClearML is supported by you and the [clear.ml](https://clear.ml) team, which helps enterprise companies build scalable MLOps. \n\nWe built ClearML to track and control the glorious but messy process of training production-grade deep learning models.\nWe are committed to vigorously supporting and expanding the capabilities of ClearML.\n\nWe promise to always be backwardly compatible, making sure all your logs, data, and pipelines will always upgrade with you.\n\n## License\n\nApache License, Version 2.0 (see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0.html) for more information)\n\nIf ClearML is part of your development process / project / publication, please cite us :heart: : \n```\n@misc{clearml,\ntitle = {ClearML - Your entire MLOps stack in one open-source tool},\nyear = {2024},\nnote = {Software available from http://github.com/clearml/clearml},\nurl={https://clear.ml/},\nauthor = {ClearML},\n}\n```\n\n## Documentation, Community \u0026 Support\n\nFor more information, see the [official documentation](https://clear.ml/docs) and [on YouTube](https://www.youtube.com/c/ClearML).\n\nFor examples and use cases, check the [examples folder](https://github.com/clearml/clearml/tree/master/examples) and [corresponding documentation](https://clear.ml/docs/latest/docs/guides).\n\nIf you have any questions: post on our [Slack Channel](https://joinslack.clear.ml), or tag your questions on [stackoverflow](https://stackoverflow.com/questions/tagged/clearml) with '**[clearml](https://stackoverflow.com/questions/tagged/clearml)**' tag (*previously [trains](https://stackoverflow.com/questions/tagged/trains) tag*).\n\nFor feature requests or bug reports, please use [GitHub issues](https://github.com/clearml/clearml/issues).\n\nAdditionally, you can always find us at *info@clear.ml*\n\n## Contributing\n\n**PRs are always welcome** :heart: See more details in the ClearML [Guidelines for Contributing](https://github.com/clearml/clearml/blob/master/CONTRIBUTING.md).\n\n\n_May the force (and the goddess of learning rates) be with you!_\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fclearml%2Fclearml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fclearml%2Fclearml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fclearml%2Fclearml/lists"}