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Supports filtering by metric ranges, parameter values, and tags, with results returned as structured DataFrames for analysis and visualization.","intents":["Query historical runs to find best-performing models by metric thresholds","Compare hyperparameter sensitivity across multiple runs to identify optimal configurations","Export run data to pandas DataFrames for custom analysis and visualization"],"best_for":["Data scientists analyzing experiment results and identifying patterns","ML engineers automating hyperparameter search and model selection","Teams building custom dashboards or analysis pipelines on top of Neptune data"],"limitations":["Query performance degrades with large numbers of runs (>10k) — no built-in indexing optimization","Complex nested queries require manual DataFrame manipulation — no query optimizer","Pagination required for large result sets — no streaming interface","Time-series queries limited to pre-aggregated metrics — no raw sample-level access"],"requires":["Python 3.7+","Neptune API key with read permissions","pandas library for DataFrame operations","Network connectivity to Neptune backend"],"input_types":["query filters (metric ranges, parameter values, tags)","column selection (which metrics/parameters to retrieve)","sorting and aggregation specifications"],"output_types":["pandas DataFrame with runs as rows and metrics/parameters as columns","filtered run objects with metadata","aggregated statistics (mean, std, min, max per metric)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-neptune__cap_4","uri":"capability://data.processing.analysis.artifact.upload.and.download.with.deduplication","name":"artifact-upload-and-download-with-deduplication","description":"Handles file and directory uploads to Neptune backend with content-addressable deduplication (same file content = same storage), automatic compression, and resumable transfers for large files. 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Supports nested directory structures and preserves file metadata (timestamps, permissions).","intents":["Upload large model checkpoints, datasets, and logs without duplicating storage for identical files","Download artifacts for local analysis or deployment without re-uploading from source","Resume interrupted uploads/downloads without starting from scratch"],"best_for":["Teams managing large model artifacts (>100MB) across many experiments","Organizations with limited storage budgets who benefit from deduplication","Distributed teams downloading artifacts over unreliable networks"],"limitations":["Deduplication requires computing content hashes upfront — adds latency for initial upload","Resumable transfers require server-side support for range requests — may not work with all proxies","Directory uploads require flattening or explicit path mapping — no automatic tree traversal","No built-in bandwidth throttling — can saturate network if uploading many large files in parallel"],"requires":["Python 3.7+","Neptune API key","Sufficient local disk space for temporary buffers during upload/download","Network connectivity with support for HTTP range requests (for resumable transfers)"],"input_types":["file paths (local filesystem)","directory paths (recursive upload)","file-like objects (BytesIO, file handles)","bytes data"],"output_types":["artifact URLs in Neptune backend","local file paths (after download)","file metadata (size, hash, upload timestamp)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-neptune__cap_5","uri":"capability://memory.knowledge.custom.namespace.and.hierarchical.organization","name":"custom-namespace-and-hierarchical-organization","description":"Allows users to define custom namespaces within runs using a dot-notation path system (e.g., 'training.metrics.loss', 'model.weights.layer1') that creates a hierarchical tree structure in the Neptune UI. Namespaces are arbitrary and user-defined, enabling flexible organization of related metrics and artifacts without schema enforcement.","intents":["Organize metrics hierarchically by training phase, model component, or data split","Group related artifacts and metadata under logical namespaces for easier navigation","Create custom views of experiment data without modifying the underlying storage"],"best_for":["Teams with complex experiments involving multiple model components or training phases","Researchers organizing metrics by data split (train/val/test) or cross-validation fold","Projects requiring flexible organization without predefined schema"],"limitations":["No schema validation — users can create inconsistent namespace structures across runs","Deep nesting (>5 levels) can make UI navigation slow","No automatic namespace discovery — requires manual documentation or convention","Renaming namespaces requires re-logging data — no built-in migration tools"],"requires":["Python 3.7+","Neptune API key","Understanding of dot-notation path syntax"],"input_types":["namespace path strings (dot-separated)","metric values (any serializable type)","artifact references"],"output_types":["hierarchical namespace tree in Neptune UI","queryable namespace paths for filtering and retrieval"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-neptune__cap_6","uri":"capability://data.processing.analysis.real.time.metric.streaming.and.live.monitoring","name":"real-time-metric-streaming-and-live-monitoring","description":"Streams metrics to Neptune backend in real-time as they're logged, enabling live dashboard updates and alerts without waiting for experiment completion. Uses WebSocket connections for low-latency updates and supports server-side aggregation for high-frequency metrics (e.g., per-batch loss). Includes configurable buffering to balance latency vs. network overhead.","intents":["Monitor training progress in real-time via Neptune dashboard without polling","Trigger alerts or early stopping based on live metric thresholds","Stream metrics from long-running experiments for immediate visibility"],"best_for":["Teams running long-duration training jobs who need real-time visibility","Experiments requiring early stopping based on live metric thresholds","Distributed training setups where aggregated metrics need immediate visibility"],"limitations":["WebSocket connections require persistent network connectivity — disconnections cause buffering","High-frequency metric logging (>100Hz) may overwhelm the backend or network","Real-time updates only visible in Neptune UI — no local real-time callbacks","Buffering adds latency between metric generation and UI visibility (configurable, typically 1-5s)"],"requires":["Python 3.7+","Neptune API key","Network connectivity with WebSocket support","Neptune backend with real-time streaming enabled (cloud or self-hosted)"],"input_types":["scalar metrics (float, int)","metric timestamps (auto-generated or user-provided)","batch aggregation hints (for high-frequency metrics)"],"output_types":["real-time metric updates in Neptune UI","server-side aggregated metrics (e.g., per-epoch averages)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-neptune__cap_7","uri":"capability://safety.moderation.collaborative.experiment.sharing.and.access.control","name":"collaborative-experiment-sharing-and-access-control","description":"Provides role-based access control (RBAC) for experiments and projects, allowing users to share runs with team members at granular permission levels (view-only, edit, admin). 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Uses SQLite or file-based storage for the local queue with configurable retention policies. Supports selective sync (e.g., only sync metrics, skip large artifacts) to manage bandwidth.","intents":["Continue logging experiments even when Neptune backend is temporarily unavailable","Sync metrics and artifacts to Neptune once connectivity is restored without data loss","Run training on disconnected systems (e.g., air-gapped networks) with deferred sync"],"best_for":["Teams running experiments in environments with intermittent connectivity","Air-gapped or on-premises deployments that sync to Neptune periodically","Mobile or edge ML scenarios where connectivity is unreliable"],"limitations":["Local queue requires disk space proportional to experiment size — can fill up on long-running experiments","Sync is one-way (local → remote) — no conflict resolution if run is modified remotely during offline period","Queue persistence depends on filesystem reliability — data loss possible if disk fails","Selective sync requires manual configuration — no automatic bandwidth-aware sync"],"requires":["Python 3.7+","Local disk space for queue storage (size depends on experiment size and offline duration)","SQLite or file-based storage backend","Network connectivity for sync (can be periodic, not real-time)"],"input_types":["metrics and artifacts (same as online mode)","sync configuration (selective sync rules, bandwidth limits)"],"output_types":["local queue files (SQLite or JSON)","sync status and logs","remote Neptune data after sync completes"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-neptune__cap_9","uri":"capability://search.retrieval.custom.dashboard.and.visualization.builder","name":"custom-dashboard-and-visualization-builder","description":"Provides a UI builder for creating custom dashboards that combine metrics, artifacts, and metadata from multiple runs into configurable visualizations (line charts, scatter plots, heatmaps, tables). 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