Comet API

Captures training hyperparameters, loss curves, accuracy metrics, and custom KPIs in real-time during model training runs, storing them with automatic run versioning and timestamping. Uses a client-side SDK that batches metric submissions to reduce network overhead, with server-side deduplication and time-series indexing for efficient retrieval and comparison across runs.

Automatically captures the source code, Git commit hash, and file diffs associated with each experiment run, enabling reproducibility and debugging of model behavior changes. Uses Git integration to extract commit metadata and file state at run time, storing code snapshots server-side with efficient delta compression for storage optimization.

Enables multiple team members to view, compare, and manage experiments within shared workspaces with role-based access control (viewer, editor, admin). Uses workspace-level permissions to control who can create experiments, modify runs, and access sensitive model artifacts. Supports team invitations via email and API-based user provisioning for enterprise deployments.

Triggers alerts based on metric thresholds, anomaly detection, or custom conditions, with notifications sent via email, Slack, or webhooks. Uses rule-based alert definitions (e.g., 'alert if accuracy < 0.85') and statistical anomaly detection (isolation forests, z-score) to identify unexpected metric behavior. Supports alert deduplication to prevent notification spam from repeated violations.

Automatically collects CPU usage, GPU memory, RAM consumption, disk I/O, and network bandwidth during training runs without explicit instrumentation. Uses OS-level system calls (psutil on Python, process APIs on Node.js) to poll resource metrics at configurable intervals, correlating them with experiment timeline for bottleneck identification.

Provides a web-based dashboard that displays multiple experiments side-by-side with metric curves, parameter tables, and system resource graphs. Uses client-side filtering (by metric range, parameter value, date range) and server-side aggregation to render comparisons across hundreds of runs without loading all data into memory. Supports custom chart configurations (line plots, scatter plots, heatmaps) with drag-and-drop metric selection.

Stores trained model artifacts (weights, checkpoints, serialized objects) with semantic versioning, stage transitions (staging → production), and custom metadata tags. Uses a hierarchical storage structure where each model version is immutable and tagged with training run ID, metrics snapshot, and deployment stage. Supports rollback to previous versions via API calls without manual artifact management.

Logs predictions, inputs, and ground-truth labels from production models in real-time, enabling detection of data drift, prediction drift, and performance degradation. Uses statistical methods (Kolmogorov-Smirnov test, Jensen-Shannon divergence) to compare production data distributions against training data baselines, triggering alerts when drift exceeds configurable thresholds. Stores prediction logs with low-latency writes using batched API calls.

Exposes experiment data, metrics, and model registry via RESTful endpoints, enabling external systems to query runs, retrieve metrics, and trigger model deployments. Uses standard HTTP verbs (GET for retrieval, POST for creation, PUT for updates) with JSON request/response bodies and pagination for large result sets. Supports API key authentication and role-based access control for team environments.

Provides native SDKs for Python and JavaScript/Node.js with built-in integrations for PyTorch, TensorFlow, scikit-learn, XGBoost, and Hugging Face Transformers. Uses decorator patterns and context managers to automatically log metrics, gradients, and model architecture without explicit instrumentation. Framework integrations hook into training loops via callbacks (PyTorch Lightning, Keras) or monkey-patching (scikit-learn).

Enables definition of hyperparameter search spaces (continuous ranges, discrete choices, conditional parameters) and tracks optimization progress across multiple runs. Integrates with Optuna, Ray Tune, and Hyperopt to log search configurations and intermediate trial results. Provides visualization of parameter importance and optimization trajectory to identify which hyperparameters have the most impact on model performance.

Allows logging of arbitrary custom metrics, images, audio, and structured artifacts (DataFrames, JSON objects) with optional schema validation. Uses a flexible logging API that accepts Python objects and serializes them to JSON or binary formats for storage. Schema validation (via JSON Schema or Pydantic models) ensures data consistency across runs and enables type-safe querying.