[](https://pypi.org/project/clearml/) [](https://artifacthub.io/packages/search?repo=allegroai) [](https://www.youtube.com/c/clearml) [](https://joinslack.clear.ml) [](https://app.clear.ml)
## Additional Modules
- [clearml-session](https://github.com/allegroai/clearml-session) - **Launch remote JupyterLab / VSCode-server inside any docker, on Cloud/On-Prem machines**
- [clearml-task](https://github.com/allegroai/clearml/blob/master/docs/clearml-task.md) - Run any codebase on remote machines with full remote logging of Tensorboard, Matplotlib & Console outputs
- [clearml-data](https://github.com/allegroai/clearml/blob/master/docs/datasets.md) - **CLI for managing and versioning your datasets, including creating / uploading / downloading of data from S3/GS/Azure/NAS**
- [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!
- [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
- [Automation Pipeline](https://clear.ml/docs/latest/docs/guides/pipeline/pipeline_controller) - Build pipelines based on existing experiments / jobs, supports building pipelines of pipelines!
- [Slack Integration](https://clear.ml/docs/latest/docs/guides/services/slack_alerts) - 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 team
- Create a remote job from any experiment with a click of a button
- Automate processes and create pipelines to collect your experimentation logs, outputs, and data
- Store all your data on any object-storage solution, with the most straightforward interface possible
- Make your data transparent by cataloging it all on the ClearML platform
We believe ClearML is ground-breaking. We wish to establish new standards of true seamless integration between
experiment management, MLOps, and data management.
## Who We Are
ClearML is supported by you and the [clear.ml](https://clear.ml) team, 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.
## License
Apache License, Version 2.0 (see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0.html) for 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 & Support
For more information, see the [official documentation](https://clear.ml/docs) and [on YouTube](https://www.youtube.com/c/ClearML).
For examples and use cases, check the [examples folder](https://github.com/allegroai/clearml/tree/master/examples) and [corresponding documentation](https://clear.ml/docs/latest/docs/guides).
If 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*).
For feature requests or bug reports, please use [GitHub issues](https://github.com/allegroai/clearml/issues).
Additionally, you can always find us at *info@clear.ml*
## Contributing
**PRs are always welcome** :heart: See more details in the ClearML [Guidelines for Contributing](https://github.com/allegroai/clearml/blob/master/docs/contributing.md).
_May the force (and the goddess of learning rates) be with you!_