automatic-experiment-tracking
Automatically captures and logs experiment metadata including hyperparameters, metrics, and artifacts with minimal code instrumentation. Integrates directly with popular ML frameworks to record training runs without requiring extensive manual logging.
distributed-task-orchestration
Schedules and manages distributed ML tasks across multiple machines and GPUs without requiring external orchestration tools. Handles resource allocation, task queuing, and execution coordination for parallel workloads.
web-ui-experiment-dashboard
Provides a web-based interface for viewing, filtering, and managing experiments with dashboards for metrics visualization and experiment comparison. Enables team collaboration and experiment discovery through centralized UI.
team-collaboration-and-access-control
Manages user access, permissions, and team collaboration features within the ClearML platform. Enables sharing of experiments, models, and resources across team members with granular access control.
integration-with-popular-ml-frameworks
Provides native integrations and auto-logging capabilities with popular ML frameworks like PyTorch, TensorFlow, scikit-learn, and others. Automatically captures framework-specific metadata without requiring manual instrumentation.
data-versioning-and-lineage-tracking
Tracks data versions and maintains lineage information showing which datasets were used in which experiments. Enables reproducibility by documenting the complete data pipeline from source to model training.
hyperparameter-sweep-execution
Automatically generates and executes multiple training runs with different hyperparameter combinations across available compute resources. Manages the sweep configuration, task creation, and result aggregation.
model-versioning-and-artifact-management
Stores, versions, and manages trained models and associated artifacts with automatic tracking of model lineage and metadata. Enables retrieval and comparison of different model versions across experiments.
+6 more capabilities