ClearML: An Open-Source MLOps Platform for Experiment Tracking and Reproducibility
ClearML is an open-source MLOps platform designed to streamline the development lifecycle of machine learning and deep learning projects. Since its inception in 2019, ClearML has evolved into a comprehensive suite of tools that address key challenges in AI workflows, including experiment tracking, data management, orchestration, and model deployment. The platform operates under the Apache 2.0 license, ensuring accessibility and flexibility for users across industries.
The core philosophy behind ClearML is to eliminate the complexity and messiness inherent in training production-grade AI models. By integrating experiment tracking, data versioning, and orchestration into a unified platform, ClearML enables teams to maintain reproducibility, collaboration, and scalability throughout the AI development process. Its modular architecture allows for seamless integration with existing workflows, making it a versatile tool for both research and production environments.
ClearML’s design emphasizes effortless adoption, ensuring that teams can preserve their existing methods and practices while benefiting from enhanced visibility and automation. The platform is built to support collaboration, automation, and transparency, establishing new standards for integrating experiment management, MLOps, and data management.
How ClearML Works
ClearML operates through a modular architecture consisting of five primary components: Experiment Manager, MLOps/LLMOps, Data-Management, Model-Serving, and Reports. Each module is designed to address specific aspects of the AI development lifecycle, from initial experimentation to deployment and monitoring.
The Experiment Manager automates the tracking of experiments by capturing comprehensive metadata, including source code versions, hyperparameters, execution environments, and performance metrics. This module ensures that every experiment is logged with full context, enabling reproducibility and comparison across runs. Users can integrate ClearML into their existing scripts with minimal effort—typically by adding just two lines of code—while retaining full control over their workflows.
The MLOps/LLMOps module provides orchestration and automation capabilities for managing machine learning and large language model workflows. Features include hyperparameter optimization, pipeline automation, and remote job execution, all of which are accessible through a user-friendly dashboard.
For deployment, the Model-Serving module enables users to deploy models as scalable endpoints with support for GPU acceleration via NVIDIA Triton.
The Reports module allows users to create and share rich Markdown documents that embed visualizations, metrics, and artifacts from experiments. Reports can be published, shared, or exported as PDFs, providing a collaborative way to document findings and share insights. Embedded visualizations update dynamically as experiments progress, ensuring that reports remain accurate and up-to-date.
ClearML’s architecture is built around three runtime components: the ClearML Python Package, the ClearML Server, and the ClearML Agent. The Python package integrates seamlessly with existing scripts, enabling experiment tracking with minimal setup. The server stores experiment data, supports the web UI, and facilitates MLOps automation, while the agent handles orchestration, reproducibility, and scalability across distributed environments.
Additional modules extend ClearML’s functionality, including clearml-session for launching remote JupyterLab or VSCode environments, clearml-task for executing code on remote machines, and clearml-data for managing datasets via CLI. Advanced features such as AWS Auto-Scaler, Hyper-Parameter Optimization, and Slack Integration further enhance the platform’s utility for teams working at scale.
Use Cases
Its modular design makes it suitable for a variety of use cases, from academic research to enterprise-grade AI development.
For researchers and data scientists, ClearML simplifies experiment tracking by automatically logging hyperparameters, code versions, and performance metrics. The integration with frameworks like PyTorch, TensorFlow, and Keras ensures compatibility with popular deep learning libraries, while support for Jupyter Notebooks and PyCharm enhances the development experience.
In enterprise environments, ClearML enables teams to standardize their AI workflows by providing a centralized platform for experiment management, data versioning, and model deployment. Features like hyperparameter optimization and automated pipelines streamline the transition from experimentation to production, reducing time-to-market for AI models. The platform’s support for Kubernetes and cloud deployments ensures scalability, while role-based access control (RBAC) and networking security address enterprise-grade requirements.
The GenAI App Engine module provides tools for deploying LLMs into GPU clusters, managing Retrieval-Augmented Generation (RAG) tasks, and ensuring secure, authenticated access to deployed services. This makes ClearML a robust solution for organizations developing and deploying advanced AI models.
For collaborative teams, ClearML fosters transparency and reproducibility by centralizing experiment logs, data versions, and model artifacts. The Reports module allows teams to document findings, share insights, and embed dynamic visualizations, ensuring that knowledge is preserved and accessible. Integration with Slack and other collaboration tools further enhances communication within teams.
Key Features and Capabilities
ClearML’s feature set is designed to address the end-to-end needs of AI development, from experimentation to deployment.
- Automated Experiment Tracking: ClearML captures every aspect of an experiment, including code versions, hyperparameters, environment details, and performance metrics. This ensures that experiments are fully reproducible and comparable. Supported frameworks include PyTorch, TensorFlow, Keras, FastAI, XGBoost, LightGBM, and Scikit-Learn.
- Data Versioning and Management: The Data-Management module provides a unified interface for managing datasets across object storage systems. Users can version datasets, track changes, and ensure data integrity throughout the AI lifecycle.
- Orchestration and Pipelines: ClearML’s MLOps/LLMOps module automates workflows through pipelines, enabling teams to formalize their processes. Pipelines can be built from existing experiments or jobs, supporting nested pipelines for complex workflows. Integration with Kubernetes and cloud providers ensures scalability and flexibility.
- Hyperparameter Optimization (HPO): ClearML offers state-of-the-art Bayesian optimization algorithms for hyperparameter tuning.
- Model Serving and Deployment: The Model-Serving module allows users to deploy models as scalable endpoints with GPU acceleration.
- Dynamic Reporting and Visualization: ClearML’s Reports module enables users to create rich Markdown documents that embed live visualizations from experiments. Reports can include plots, metrics, and artifacts, with dynamic updates as experiments progress. Embedded visualizations support complex queries, allowing users to filter by tags, projects, or performance metrics.
- Remote Execution and Debugging: ClearML supports remote execution of tasks, enabling users to run code on distributed machines while logging outputs back to the central platform. This is particularly useful for large-scale experiments requiring significant compute resources. Integration with Jupyter Notebooks and PyCharm enhances the debugging experience.
- Security and Access Control: ClearML includes role-based access control (RBAC) for managing permissions across teams and projects. The platform also supports secure authentication and networking, ensuring that deployed models and experiments are protected. Features like fractional GPU allocation and cost optimization further enhance security and resource management.
- Integration with Cloud and On-Premises Infrastructure: ClearML’s Infrastructure Control Plane automates GPU resource provisioning, workload autoscaling, and cost optimization. This allows teams to manage compute resources efficiently, whether in the cloud or on-premises. Features like GPUaaS (GPU-as-a-Service) consolidate resources for seamless deployment.
- Regular updates and bug fixes, as documented in the release notes, reflect the project’s dedication to stability and improvement.
Recent releases, such as v2.1.12 (2026-08-19), have introduced enhancements like data entry deletion methods, hyperdataset metadata management, and improved iterator support. These updates address user feedback and expand the platform’s capabilities, particularly in data management and pipeline automation.
Implementation and Setup
Getting started with ClearML is straightforward, with options for both self-hosted and cloud-based deployments. Users can sign up for the hosted service or deploy their own ClearML server, providing flexibility based on organizational needs.
- Sign up for the ClearML Hosted Service or deploy a self-hosted server. The hosted service is accessible without credentials for public experiments, though sensitive data should not be shared on the demo server.
- Install the ClearML Python package using pip install clearml.
- Connect the SDK to the server by running clearml-init and following the provided instructions.
- Integrate ClearML into existing scripts by adding two lines of code: from clearml import Task and task = Task.init(projectname='examples', taskname='hello world'). This automatically logs experiment details, outputs, and metrics to the ClearML platform.
ClearML’s architecture is designed for scalability, with support for distributed execution across Kubernetes, cloud, and on-premises environments. The platform’s modular design allows teams to adopt only the components they need, whether for experiment tracking, data management, or model deployment.
Integration and Compatibility
ClearML is designed to integrate seamlessly with existing tools and workflows, ensuring minimal disruption to established processes.
Supported frameworks include PyTorch (with Ignite and Lightning), TensorFlow, Keras, AutoKeras, FastAI, XGBoost, LightGBM, MegEngine, and Scikit-Learn. Integration with Jupyter Notebooks and PyCharm enables remote debugging and execution, while support for TensorBoard and TensorBoardX ensures compatibility with existing visualization tools.
The platform’s CLI tools further simplify data versioning and management, making it accessible for both technical and non-technical users.
For deployment, ClearML supports Kubernetes, cloud providers, and on-premises infrastructure, ensuring that models can be served in environments that match organizational requirements.
The platform’s active community and support channels, including Slack, Stack Overflow, and GitHub, provide additional resources for troubleshooting and feature requests.
Community and Support
ClearML is supported by a growing community of researchers, developers, and enterprises. The project is maintained by the clear.ml team, which collaborates with users to expand its capabilities and address emerging needs in AI development.
- GitHub: The primary repository hosts the source code, documentation, and issue tracker.
- Slack: The ClearML Slack channel provides a space for real-time discussions, questions, and collaboration. Users can join the channel to connect with the development team and other community members.
- Stack Overflow: Questions tagged with clearml are monitored by the development team, ensuring that users receive timely assistance.
- Documentation: Comprehensive guides, tutorials, and API references are available on the ClearML website, covering everything from basic setup to advanced use cases.
- Email Support: Users can reach out to the ClearML team via support@clearml.ai for direct assistance with technical or operational questions.
ClearML’s commitment to open-source principles is reflected in its permissive Apache 2.0 license, which encourages adoption and customization.
Technical Details and Recent Developments
ClearML’s continuous evolution is evident in its regular release cycle, with updates focused on bug fixes, performance improvements, and new features.
- Methods for deleting data entries in hyperdatasets, improving data management flexibility.
- Support for setting labels on dataviews and managing hyperdataset metadata, enhancing organizational capabilities.
These updates reflect ClearML’s ongoing efforts to address user feedback and expand its feature set.
ClearML’s architecture is built on three essential layers: the Infrastructure Control Plane, the AI Development Center, and the GenAI App Engine. Each layer serves distinct purposes, from resource management to model deployment, ensuring a cohesive and scalable AI workflow.
- The Infrastructure Control Plane automates GPU resource allocation, workload scaling, and cost optimization, providing a seamless GPUaaS experience across cloud and on-premises environments.
- The AI Development Center offers tools for model training, data management, experiment tracking, and workflow automation, supporting both cloud and on-premises deployments.
- The GenAI App Engine is tailored for deploying large language models and managing RAG workloads, with built-in networking, authentication, and RBAC for secure deployments.
The Platform Management Center provides administrators with a dashboard to monitor tenant activity, usage, and costs, ensuring centralized control over ClearML deployments.
Conclusion
ClearML stands out as a comprehensive open-source MLOps platform that addresses the challenges of AI development, from experiment tracking to model deployment. Its modular architecture, seamless integrations, and commitment to reproducibility make it a valuable tool for researchers, data scientists, and enterprises alike.
By automating experiment tracking, managing data versions, and enabling scalable orchestration, ClearML enhances collaboration, transparency, and efficiency in AI workflows. Its support for a wide range of frameworks, cloud providers, and deployment scenarios ensures flexibility, while continuous updates and community engagement reflect its dedication to innovation.
For teams seeking to streamline their AI development processes, ClearML offers a robust, open-source solution that aligns with industry standards and best practices. Whether used for research, production, or collaborative projects, ClearML provides the tools and infrastructure needed to build, manage, and deploy AI models effectively.