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Experiment tracking, model registry & production monitoring for ML teams
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Automagical suite to streamline AI experiment, orchestration, and serving
ClearML provides experiment tracking, data versioning, pipeline orchestration, and scalable model serving with just two lines of code, supporting major ML frameworks and cloud/on‑prem deployments.
ClearML is a unified platform that turns messy machine‑learning workflows into reproducible, observable pipelines. By adding only two lines of Python, data scientists instantly capture code versions, hyper‑parameters, environment details, and output artifacts, while engineers gain a web UI to monitor resources and compare runs.
The suite covers five core modules: Experiment Manager, Data‑Management, MLOps/LLMOps orchestration, Model‑Serving, and Reporting. It integrates with PyTorch, TensorFlow, Scikit‑Learn and others, and works on cloud, Kubernetes, or bare‑metal clusters. Model serving leverages Nvidia‑Triton for GPU‑accelerated inference and includes built‑in monitoring. Users can self‑host the ClearML Server for full data control or use the hosted free tier. Together, these tools enable teams to move from prototype to production in minutes while maintaining full provenance and scalability.
When teams consider ClearML, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Rapid prototyping to production
Track experiments, version datasets, and deploy a model endpoint in under 5 minutes, ensuring reproducibility.
Hybrid cloud training pipelines
Orchestrate GPU jobs across on‑prem clusters and cloud providers, auto‑scaling resources via ClearML Agent.
Dataset governance for regulated industries
Version and audit data stored in S3 or Azure with full lineage linked to experiments.
Continuous model monitoring
Serve models with Nvidia‑Triton and receive real‑time performance metrics and drift alerts via built‑in monitoring.
Install the `clearml` Python package, run `clearml-init` to configure credentials, and add two lines of code to initialize a Task.
Yes, the ClearML Server is open source and can be deployed on‑prem or in any cloud environment.
PyTorch, TensorFlow, Keras, FastAI, XGBoost, LightGBM, MegEngine, Scikit‑Learn and more.
ClearML integrates with Nvidia‑Triton, providing optimized GPU inference out of the box.
Object storage services such as Amazon S3, Google Cloud Storage, Azure Blob, as well as NAS file systems.
Project at a glance
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