ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.
Open source
24 alternativesClearML
ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.
About ClearML
ClearML is an open-source suite for machine learning development and production. It includes modules for experiment tracking, dataset versioning on object storage, orchestration of jobs and pipelines on Kubernetes, cloud or bare metal, model serving with monitoring, and Markdown reports.
Integration is through a Python package that logs source control state, environment packages, hyperparameters, stdout and stderr, resource use, and model snapshots and artifacts, with optional upload to shared folders, S3, Google Storage, Azure or HTTP. It can use a hosted server or a self-hosted server; experiments sent to the demo server are public.
Key features
- Log experiments, hyperparameters, source control state and environment packages
- Version and manage datasets on S3, GS, Azure or NAS storage
- Orchestrate ML jobs and pipelines on Kubernetes, cloud or bare metal
- Serve models with GPU support via Nvidia Triton and monitoring
- Capture stdout, resource use and model snapshots with optional upload
- Integrate with PyTorch, TensorFlow, Jupyter Notebook and PyCharm
Best for: Python machine learning teams that need experiment tracking, data versioning, orchestration and serving in one workflow.
Good for
- Open-source codebase with option to run a self-hosted server
- Records source control state, packages, parameters and outputs from existing scripts
- Supports versioned datasets on S3, Google Storage, Azure and NAS
Watch out for
- Experiments sent to the demo server are public and not suited for sensitive data
ClearML at a glance
- Website
- github.com/allegroai/clearml
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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ClearML FAQ
How do I start logging an experiment?
Install with pip install clearml, create credentials, run clearml-init, then add from clearml import Task and Task.init with project and task names.
Can I host ClearML myself?
Yes, you can use the hosted service or set up your own server. The demo server is not used by default.
Is the demo server private?
No. Credentials are not needed for the demo server, but experiments there are public, so do not use it for sensitive experiments.
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