All 51 Dependencies
Every package ClearML depends on, ranked by repo health score.
ClearML is an open-source MLOps and LLMOps platform that automates the full AI development lifecycle — from experiment tracking and data versioning to pipeline orchestration and model serving. By adding just two lines of code to any Python training script, data scientists and ML engineers gain automatic logging of hyperparameters, source code, environment dependencies, model weights, metrics, and rich media outputs like images, audio, and video samples.
Built around a three-tier architecture of Python SDK, self-hostable ClearML Server, and distributed ClearML Agent workers, the platform supports hybrid deployments across cloud, Kubernetes, and bare-metal infrastructure. Deep integrations with PyTorch, TensorFlow, Keras, XGBoost, LightGBM, FastAI, MegEngine, CatBoost, and scikit-learn mean most existing training scripts instrument themselves automatically through Python import hooks — no refactoring required.
Beyond experiment tracking, ClearML provides version-controlled dataset management over S3, Google Cloud Storage, Azure Blob, and NAS; a pipeline orchestration engine with DAG execution and cross-step artifact passing; GPU-optimized model serving via NVIDIA Triton with sub-5-minute deployment; and container-level fractional GPU memory partitioning for maximizing hardware utilization across concurrent experiments.
The platform has been under active development since 2019 and is used by teams ranging from individual researchers to enterprise MLOps organizations building reproducible, scalable AI workflows.
