Neptune

Captures training metrics, hyperparameters, and artifacts across any ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost, etc.) via a unified Python SDK that intercepts logging calls and serializes structured metadata to Neptune's backend. Uses a client-side buffering layer to batch writes and reduce network overhead, with automatic schema inference for custom metrics and support for nested parameter hierarchies.

Provides a web-based UI and API for querying and comparing experiments across multiple dimensions (metrics, hyperparameters, artifacts, execution time, hardware) using a columnar data model that indexes all logged metadata. Supports SQL-like filtering, sorting, and grouping operations to identify patterns across hundreds or thousands of runs. Implements client-side caching and lazy-loading of comparison tables to handle large experiment histories.

Tracks dataset versions used in experiments with automatic profiling (row counts, column statistics, data types, missing values) and lineage tracking back to data sources. Stores dataset metadata (schema, statistics, sample rows) and enables comparison of datasets across experiments to identify data drift or distribution changes. Integrates with data versioning tools (DVC, Pachyderm) to track external dataset versions.

Exposes a REST API and Python SDK for programmatic access to all Neptune data (experiments, metrics, artifacts, models) enabling integration with external tools and custom workflows. Supports complex queries (filtering, sorting, aggregation) on experiment metadata and metrics, and enables batch operations (tagging, archiving, deleting) across multiple experiments. API responses are JSON-formatted and support pagination for large result sets.

Provides a centralized registry for storing trained models with automatic versioning, metadata tagging, and lineage tracking back to source experiments and datasets. Models are stored as artifacts with associated metadata (framework, input/output schemas, performance metrics) and can be promoted through stages (staging, production, archived) with audit logs. Integrates with experiment runs to automatically link models to their training configurations.

Provides a web-based dashboard that displays live-updating metrics, system resource usage, and training progress for active experiments with real-time WebSocket connections to Neptune backend. Supports custom dashboard layouts with draggable widgets, metric visualization (line charts, histograms, scatter plots), and alerts for metric anomalies or training failures. Multiple team members can view the same experiment simultaneously with shared annotations and comments.

Stores experiment artifacts (models, datasets, plots, checkpoints) using content-addressable storage (SHA-256 hashing) to automatically deduplicate identical files across experiments and reduce storage overhead. Maintains version history for each artifact with metadata (upload time, size, associated experiment) and provides download URLs with optional expiration. Supports incremental uploads for large files and resumable downloads.

Provides a declarative API for defining hyperparameter search spaces (grid, random, Bayesian optimization) and automatically logs each trial as a separate experiment run with consistent tagging and grouping. Supports integration with popular HPO libraries (Optuna, Ray Tune, Hyperopt) via adapters that automatically capture trial metadata, search space definitions, and optimization progress. Enables post-hoc analysis of search trajectories and convergence patterns.

Implements role-based access control (RBAC) with configurable permissions (viewer, contributor, admin) at the project and experiment level, enabling teams to share experiment data with appropriate access restrictions. Maintains audit logs of all modifications (metric updates, model promotions, artifact uploads) with timestamps and user attribution. Supports team invitations, workspace management, and integration with SSO providers (SAML, OAuth) for enterprise deployments.

Provides a rich charting library supporting multiple visualization types (line charts, scatter plots, histograms, heatmaps, 3D plots, custom HTML) for logged metrics and artifacts. Enables interactive exploration with zoom, pan, and hover tooltips, and supports overlaying multiple experiments on the same chart for direct comparison. Charts are rendered client-side using a JavaScript visualization engine and can be embedded in external dashboards via iframe or API.

Provides native integrations and adapters for popular ML frameworks (PyTorch Lightning, Keras, Hugging Face Transformers, XGBoost) and orchestration tools (Airflow, Kubeflow, Ray) that automatically capture training metadata without requiring explicit logging code. Integrations use framework-specific hooks (callbacks, loggers) to intercept training events and serialize them to Neptune. Supports custom integrations via a plugin API for non-standard frameworks.

Automatically captures code snapshots (Git commit hash, uncommitted changes) and environment metadata (Python version, package versions, system info) for each experiment run, enabling reproducibility and debugging. Stores code diffs and environment specs alongside experiment metadata, and provides tools to restore the exact environment used for a past experiment. Integrates with Git to track source code lineage.