Clear.ml

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.

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.

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.

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.

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.

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.

Automatically generates and executes multiple training runs with different hyperparameter combinations across available compute resources. Manages the sweep configuration, task creation, and result aggregation.

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.

Provides tools to compare metrics, hyperparameters, and results across multiple experiments in a unified interface. Enables visualization and statistical analysis of experiment differences.

Enables on-premise deployment of the entire ClearML platform with full control over infrastructure, data storage, and security. Provides flexibility to customize and audit all components without vendor lock-in.

Captures and logs custom metrics, plots, and data from any ML framework or training process through a flexible SDK. Supports diverse metric types and visualization formats without framework-specific constraints.

Facilitates packaging and deploying trained models to production environments with support for multiple serving frameworks and inference engines. Manages model serving infrastructure and enables A/B testing of model versions.

Monitors compute resource usage (CPU, GPU, memory) across training tasks and provides visibility into resource allocation and efficiency. Enables optimization of resource utilization across the cluster.

Defines and executes multi-stage ML pipelines with dependencies between tasks, enabling complex workflows that combine data processing, training, and evaluation stages. Manages task execution order and data flow between pipeline stages.