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ConfigsApr 1, 2026·1 min de lectura

ClearML — End-to-End MLOps Platform

ClearML provides experiment tracking, pipeline orchestration, data management, and model serving in one platform. 6.6K+ stars. 2-line integration. Apache 2.0.

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Introducción

ClearML is an MLOps/LLMOps platform providing experiment management, data handling, pipeline orchestration, and model serving in one integrated solution. With 6,600+ GitHub stars and Apache 2.0 license, it requires just 2 lines of code to start tracking experiments. Automatic capture of code, environments, hyperparameters, results, CPU/GPU metrics, artifacts, and TensorBoard logs. Supports PyTorch, TensorFlow, Keras, XGBoost, and more. Deploy on Kubernetes, cloud, or on-premises.

Best for: ML teams wanting zero-friction experiment tracking with full MLOps capabilities Works with: Claude Code, OpenAI Codex, Cursor, Gemini CLI, Windsurf

Key Features

  • 2-line integration for any ML script
  • Auto-tracking of code, params, metrics, artifacts
  • Pipeline orchestration and scheduling
  • Dataset versioning (S3, GCS, Azure, NAS)
  • Model serving with GPU optimization
  • Kubernetes and cloud deployment

FAQ

Q: What is ClearML? A: End-to-end MLOps platform with 6.6K+ stars. 2-line experiment tracking, pipelines, data management, model serving. Apache 2.0.

Q: How do I install ClearML? A: pip install clearml. clearml-init to configure. Task.init() in your script.

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Fuente y agradecimientos

clearml/clearml — 6,600+ GitHub stars

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