{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"comet-api","slug":"comet-api","name":"Comet API","type":"api","url":"https://www.comet.com/docs/v2/api-and-sdk","page_url":"https://unfragile.ai/comet-api","categories":["model-training","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"comet-api__cap_0","uri":"capability://data.processing.analysis.experiment.parameter.and.metric.logging.with.automatic.versioning","name":"experiment parameter and metric logging with automatic versioning","description":"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.","intents":["Log hyperparameters and metrics from my training loop without manual checkpoint management","Compare metric trajectories across 50+ experiment runs to identify which hyperparameter changes improved performance","Track custom domain-specific metrics alongside standard loss/accuracy metrics","Automatically version and tag experiments without writing boilerplate logging code"],"best_for":["ML engineers running iterative hyperparameter tuning on teams","Researchers comparing model variants across multiple training runs","Data scientists needing audit trails of experiment configurations"],"limitations":["Metric submission is asynchronous — high-frequency logging (>1000 metrics/sec) may experience buffering delays","No built-in support for distributed training metric aggregation across multiple nodes without custom synchronization","Free tier has retention limits (~30 days) before metrics are archived"],"requires":["Python 3.7+ or JavaScript/Node.js 12+","Comet API key (free account available)","Network connectivity to Comet servers during training"],"input_types":["numeric scalars (float, int)","Python dicts/JSON objects for structured metrics","custom objects with __dict__ serialization"],"output_types":["time-series metric data","run metadata JSON","comparison matrices (CSV/JSON)"],"categories":["data-processing-analysis","ml-observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_1","uri":"capability://data.processing.analysis.code.snapshot.capture.and.diff.tracking","name":"code snapshot capture and diff tracking","description":"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.","intents":["Reproduce a model's exact behavior by retrieving the code version that generated a specific experiment result","Identify which code changes caused a metric regression by comparing diffs between two experiment runs","Audit which developer and code branch trained a production model","Link model performance to specific code commits without manual tagging"],"best_for":["Teams using Git-based workflows who need code-to-model traceability","ML engineers debugging performance regressions across code versions","Organizations requiring compliance audit trails for model development"],"limitations":["Requires Git repository initialization — standalone scripts without Git context will not capture commit metadata","Large codebases (>100MB) may experience slow snapshot uploads on first run","Binary files and large data files are excluded from snapshots to reduce storage; only source code is captured"],"requires":["Git repository initialized in project directory","Git command-line tools accessible from Python/Node.js environment","Comet SDK configured with API key"],"input_types":["local file system (source code files)","Git repository metadata"],"output_types":["code snapshot JSON with file contents","diff objects (unified diff format)","commit metadata (hash, author, timestamp)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_10","uri":"capability://automation.workflow.team.collaboration.with.workspace.sharing.and.permission.management","name":"team collaboration with workspace sharing and permission management","description":"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.","intents":["Share experiment results with team members and stakeholders without granting full account access","Assign different permission levels (viewer, editor, admin) to team members based on their roles","Invite new team members to workspace and automatically provision their access","Audit who accessed or modified experiments for compliance and governance"],"best_for":["ML teams collaborating on shared projects with multiple stakeholders","Organizations with compliance requirements for access control and audit trails","Enterprise deployments with centralized user management"],"limitations":["Permission changes are not retroactive; existing shared links remain accessible even after permissions are revoked","Audit logs show access events but not detailed actions (e.g., which metrics were viewed); requires external logging for detailed tracking","Workspace-level permissions apply to all experiments; fine-grained per-experiment permissions are not supported"],"requires":["Comet team account (paid tier)","Email addresses of team members to invite","Role definitions (viewer, editor, admin) configured in workspace settings"],"input_types":["team member email addresses","role assignments (viewer, editor, admin)"],"output_types":["workspace access tokens","audit logs (JSON)","permission matrices (CSV)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_11","uri":"capability://automation.workflow.automated.experiment.alerts.and.notifications","name":"automated experiment alerts and notifications","description":"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.","intents":["Receive email notification when a training run completes with metrics below acceptable thresholds","Get Slack alerts when model performance degrades unexpectedly during production monitoring","Trigger automated retraining pipelines via webhooks when drift is detected","Suppress duplicate alerts to avoid notification fatigue from repeated metric violations"],"best_for":["Teams running continuous training pipelines who need automated notifications","Organizations with SLAs requiring rapid response to model performance degradation","ML engineers integrating Comet with external alerting systems (PagerDuty, Opsgenie)"],"limitations":["Alert rules are static; no dynamic thresholds based on historical performance","Anomaly detection uses simple statistical methods (z-score, isolation forest); may produce false positives on naturally variable metrics","Webhook delivery is not guaranteed; failed deliveries are retried with exponential backoff but may be lost after max retries"],"requires":["Alert rules defined in Comet dashboard or API","Email address or Slack workspace for notifications","Webhook endpoint (optional, for custom integrations)"],"input_types":["metric thresholds (numeric comparisons)","anomaly detection parameters (z-score threshold, isolation forest contamination)","notification channels (email, Slack, webhook)"],"output_types":["alert notifications (email, Slack messages, webhook POST requests)","alert history logs (JSON)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_2","uri":"capability://data.processing.analysis.system.and.hardware.resource.monitoring","name":"system and hardware resource monitoring","description":"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.","intents":["Identify whether my model training is CPU-bound or GPU-bound by correlating resource usage with metric progression","Detect memory leaks in training loops by monitoring RAM growth over time","Optimize batch size by comparing GPU memory usage across runs with different batch configurations","Understand why training slowed down by reviewing system resource contention during specific time windows"],"best_for":["ML engineers optimizing training efficiency on resource-constrained hardware","Teams running distributed training who need per-node resource visibility","Data scientists debugging performance issues without manual profiling tools"],"limitations":["GPU monitoring requires NVIDIA CUDA toolkit and nvidia-ml-py library; AMD/Intel GPUs not supported","Polling-based approach adds ~1-2% CPU overhead; high-frequency polling (>10Hz) may impact training performance","System metrics are aggregated at process level — does not isolate resource usage of individual functions or layers"],"requires":["Python 3.7+ with psutil library, or Node.js with native OS module","Linux/macOS/Windows OS with standard system monitoring APIs","NVIDIA CUDA toolkit (optional, for GPU monitoring)"],"input_types":["system process IDs","GPU device indices"],"output_types":["time-series resource metrics (CPU %, memory MB, GPU memory MB)","resource correlation matrices","bottleneck reports (JSON)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_3","uri":"capability://data.processing.analysis.interactive.experiment.comparison.dashboard.with.filtering.and.visualization","name":"interactive experiment comparison dashboard with filtering and visualization","description":"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.","intents":["Visually compare metric trajectories across 20+ experiments to identify the best-performing configuration","Filter experiments by parameter ranges (e.g., learning_rate > 0.001) to isolate the impact of specific hyperparameters","Export comparison tables and charts for presentations or research papers","Share experiment results with team members via shareable dashboard links"],"best_for":["ML teams collaborating on hyperparameter tuning with non-technical stakeholders","Researchers presenting experiment results in papers or talks","Product managers reviewing model performance improvements across versions"],"limitations":["Dashboard rendering becomes slow with >500 experiments on a single view; requires filtering or pagination","Custom chart configurations are not persisted across sessions — must be reconfigured each visit","Export to PDF/PNG uses client-side rendering, which may produce inconsistent output across browsers"],"requires":["Web browser with JavaScript enabled (Chrome, Firefox, Safari, Edge)","Comet account with experiments logged","Network connectivity to Comet servers"],"input_types":["experiment metadata (parameters, metrics, timestamps)","filter criteria (parameter ranges, metric thresholds, date ranges)"],"output_types":["interactive HTML dashboard","PNG/PDF exports of charts","CSV exports of comparison tables"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_4","uri":"capability://automation.workflow.model.registry.with.versioning.and.metadata.tagging","name":"model registry with versioning and metadata tagging","description":"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.","intents":["Register a trained model and mark it as 'production-ready' after validation, with automatic rollback capability if issues arise","Track which training run produced a specific model version and retrieve its exact hyperparameters and metrics","Manage multiple model versions (v1.0, v1.1, v2.0) with different performance characteristics for A/B testing","Prevent accidental overwriting of production models by enforcing immutable versioning"],"best_for":["ML teams managing multiple model versions in production","Organizations requiring audit trails of model deployments","Teams practicing continuous model retraining with automated rollback"],"limitations":["Model artifacts are stored server-side; large models (>1GB) may experience slow upload/download times","No built-in model compression or quantization — full model weights are stored as-is","Stage transitions (e.g., staging → production) are not gated by approval workflows; requires external CI/CD integration for governance"],"requires":["Comet API key with model registry permissions","Model artifact in serializable format (PyTorch .pt, TensorFlow SavedModel, ONNX, pickle, etc.)","Network connectivity for artifact upload/download"],"input_types":["model artifacts (binary files, serialized objects)","metadata tags (strings, key-value pairs)","stage labels (staging, production, archived)"],"output_types":["model registry entries (JSON metadata)","versioned artifact URLs","deployment history logs"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_5","uri":"capability://data.processing.analysis.production.model.monitoring.with.prediction.logging.and.drift.detection","name":"production model monitoring with prediction logging and drift detection","description":"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.","intents":["Detect when production data distribution shifts away from training data, indicating model retraining may be needed","Monitor prediction confidence scores to identify when the model is making uncertain predictions on new data","Compare production model performance (accuracy, precision) against baseline metrics from training","Receive alerts when data drift or performance degradation is detected, triggering manual review or automated retraining"],"best_for":["ML teams deploying models to production who need continuous performance monitoring","Organizations with regulatory requirements for model audit trails (finance, healthcare)","Teams practicing continuous retraining with automated drift-triggered pipelines"],"limitations":["Drift detection requires baseline statistics from training data; models without logged training data cannot establish baselines","Statistical drift tests assume sufficient sample size (>100 predictions); sparse prediction logs may produce unreliable drift signals","Alerts are rule-based (threshold-driven) and do not account for business context; false positives require manual tuning of thresholds"],"requires":["Production model integrated with Comet SDK or REST API","Ground-truth labels available (either immediately or with delay) for performance calculation","Comet API key with monitoring permissions","Training data baseline logged during experiment phase"],"input_types":["prediction inputs (features, embeddings)","model predictions (class labels, probabilities, regression values)","ground-truth labels (optional, for performance metrics)","prediction metadata (timestamp, model version, confidence)"],"output_types":["prediction logs (JSON, time-series)","drift detection reports (statistical test results)","performance metrics (accuracy, precision, recall over time)","alert notifications (email, webhook, Slack)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_6","uri":"capability://tool.use.integration.rest.api.for.programmatic.experiment.access.and.automation","name":"rest api for programmatic experiment access and automation","description":"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.","intents":["Query experiment results from external dashboards or BI tools without accessing Comet UI","Programmatically retrieve the best-performing model version based on metric thresholds","Trigger automated model deployments when a new experiment meets performance criteria","Integrate Comet data into custom CI/CD pipelines for automated retraining workflows"],"best_for":["ML engineers building custom automation workflows around Comet","Teams integrating Comet with existing CI/CD and MLOps infrastructure","Organizations building internal tools that consume experiment data"],"limitations":["Rate limiting applies to API calls (typically 100 requests/minute on free tier); high-frequency polling requires pagination and caching","API responses are paginated; retrieving all metrics from 1000+ experiments requires multiple sequential requests","No GraphQL support — REST API requires multiple calls to fetch related data (e.g., experiment + metrics + code)"],"requires":["Comet API key (generated in account settings)","HTTP client library (curl, requests, axios, etc.)","Knowledge of REST API conventions and JSON parsing"],"input_types":["query parameters (experiment ID, metric name, date range)","JSON request bodies (for POST/PUT operations)"],"output_types":["JSON response bodies (experiment metadata, metrics, model registry entries)","HTTP status codes (200, 400, 404, 429, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_7","uri":"capability://tool.use.integration.python.and.javascript.sdk.with.framework.specific.integrations","name":"python and javascript sdk with framework-specific integrations","description":"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).","intents":["Log metrics and parameters from PyTorch/TensorFlow training loops with minimal code changes (single decorator or callback)","Automatically capture model architecture and layer-wise gradients for debugging training dynamics","Integrate Comet into existing scikit-learn pipelines without refactoring code","Use Comet in Jupyter notebooks with automatic cell-level experiment tracking"],"best_for":["ML engineers using PyTorch, TensorFlow, or scikit-learn who want minimal instrumentation overhead","Data scientists working in Jupyter notebooks who need experiment tracking without boilerplate","Teams building production training pipelines with automated logging"],"limitations":["Framework integrations are optimized for specific versions; older framework versions may have incomplete logging","Decorator-based logging adds ~5-10% overhead to training time due to metric serialization","JavaScript SDK has fewer framework integrations than Python; TensorFlow.js support is limited"],"requires":["Python 3.7+ or Node.js 12+","Framework library (PyTorch, TensorFlow, scikit-learn, etc.)","Comet SDK installed via pip or npm","Comet API key"],"input_types":["Python training loops, Keras callbacks, PyTorch Lightning modules","scikit-learn estimators and pipelines","Jupyter notebook cells"],"output_types":["logged metrics, parameters, and model metadata","experiment run objects with methods for manual logging"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_8","uri":"capability://planning.reasoning.hyperparameter.search.space.definition.and.optimization.tracking","name":"hyperparameter search space definition and optimization tracking","description":"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.","intents":["Define a hyperparameter search space and track optimization progress across 100+ trials without manual logging","Visualize parameter importance to identify which hyperparameters have the most impact on model performance","Resume interrupted hyperparameter searches by retrieving previous trial results and continuing from the last checkpoint","Compare optimization strategies (grid search vs Bayesian optimization) by analyzing trial trajectories"],"best_for":["ML engineers running large-scale hyperparameter optimization campaigns","Teams using Optuna, Ray Tune, or Hyperopt who want centralized tracking","Researchers analyzing hyperparameter sensitivity for publications"],"limitations":["Search space definition requires manual specification; no automatic space inference from code","Parameter importance analysis uses tree-based methods (SHAP) which may be slow for >1000 trials","Integration with optimization frameworks requires explicit callback setup; not automatic"],"requires":["Optuna, Ray Tune, or Hyperopt library installed","Comet SDK with optimization tracking support","Hyperparameter search space defined in framework-specific format"],"input_types":["hyperparameter search space definitions (ranges, choices, conditionals)","trial results (parameter values, metrics, status)"],"output_types":["optimization trajectory visualizations (parameter vs metric plots)","parameter importance rankings (SHAP values)","trial history logs (JSON)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__cap_9","uri":"capability://data.processing.analysis.custom.metric.and.artifact.logging.with.schema.validation","name":"custom metric and artifact logging with schema validation","description":"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.","intents":["Log domain-specific metrics (e.g., F1 score per class, business KPIs) alongside standard metrics","Log model predictions and ground-truth labels as structured tables for error analysis","Capture generated images, audio samples, or text outputs from generative models for qualitative evaluation","Enforce consistent metric schemas across team to enable reliable comparison and aggregation"],"best_for":["Teams with domain-specific metrics not covered by standard frameworks","Researchers logging qualitative outputs (generated images, text) alongside quantitative metrics","Organizations enforcing data governance with schema validation"],"limitations":["Schema validation adds ~10-20ms overhead per logged artifact; high-frequency logging may be impacted","Large artifacts (images, audio) are stored as binary blobs; querying and filtering on artifact contents is not supported","Custom metric schemas are not enforced retroactively; existing runs with inconsistent schemas cannot be automatically corrected"],"requires":["Comet SDK with custom logging support","Python objects serializable to JSON or binary formats","Optional: Pydantic models or JSON Schema for schema definition"],"input_types":["Python objects (dicts, lists, custom classes)","NumPy arrays, Pandas DataFrames","PIL Images, audio arrays","JSON-serializable objects"],"output_types":["logged artifacts stored in Comet backend","schema validation reports (pass/fail)","artifact metadata (type, size, timestamp)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"comet-api__headline","uri":"capability://data.processing.analysis.ml.experiment.tracking.and.model.monitoring.api","name":"ml experiment tracking and model monitoring api","description":"A comprehensive API for tracking machine learning experiments, logging metrics, parameters, and system info, while providing model monitoring and comparison dashboards.","intents":["best ML experiment tracking API","model monitoring API for machine learning","API for logging ML training metrics","comparison dashboards for ML experiments","model registry solutions for ML","production monitoring tools for ML models"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+ or JavaScript/Node.js 12+","Comet API key (free account available)","Network connectivity to Comet servers during training","Git repository initialized in project directory","Git command-line tools accessible from Python/Node.js environment","Comet SDK configured with API key","Comet team account (paid tier)","Email addresses of team members to invite","Role definitions (viewer, editor, admin) configured in workspace settings","Alert rules defined in Comet dashboard or API"],"failure_modes":["Metric submission is asynchronous — high-frequency logging (>1000 metrics/sec) may experience buffering delays","No built-in support for distributed training metric aggregation across multiple nodes without custom synchronization","Free tier has retention limits (~30 days) before metrics are archived","Requires Git repository initialization — standalone scripts without Git context will not capture commit metadata","Large codebases (>100MB) may experience slow snapshot uploads on first run","Binary files and large data files are excluded from snapshots to reduce storage; only source code is captured","Permission changes are not retroactive; existing shared links remain accessible even after permissions are revoked","Audit logs show access events but not detailed actions (e.g., which metrics were viewed); requires external logging for detailed tracking","Workspace-level permissions apply to all experiments; fine-grained per-experiment permissions are not supported","Alert rules are static; no dynamic thresholds based on historical performance","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=comet-api","compare_url":"https://unfragile.ai/compare?artifact=comet-api"}},"signature":"Kd0kwVwim4s1Oq5VZO8XbJ9h5kzbg9gXSEnjj5IxuELrSSVDMwyuEBx+WbCmSqFINkIRxDYbQ37M0CN9g5wsCQ==","signedAt":"2026-06-22T03:39:37.522Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/comet-api","artifact":"https://unfragile.ai/comet-api","verify":"https://unfragile.ai/api/v1/verify?slug=comet-api","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}