{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-lakefs-lakefs-dvc","slug":"dvc-by-lakefs","name":"DVC by lakeFS","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=lakeFS.lakefs-dvc","page_url":"https://unfragile.ai/dvc-by-lakefs","categories":["automation"],"tags":["__ext_dvc","data version control","dataset","dvc","experiment tracking","hyperparameters","lakefs","plots","snippet","yaml"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-lakefs-lakefs-dvc__cap_0","uri":"capability://automation.workflow.git.based.experiment.tracking.and.comparison","name":"git-based experiment tracking and comparison","description":"Records ML experiment metadata (parameters, metrics, hyperparameters) as Git commits, enabling version control of entire experiment lineage without external databases. The extension integrates with Git's native commit history to track experiments as first-class Git objects, allowing developers to navigate, filter, and compare experiments across commits using Git's existing infrastructure for reproducibility and collaboration.","intents":["Track multiple ML training runs with different hyperparameters and compare their metrics side-by-side","Recover and re-run a previous experiment by checking out its Git commit","Share experiment results with team members through Git history without external experiment tracking services","Understand which code changes led to metric improvements or regressions"],"best_for":["ML teams already using Git for code versioning who want lightweight experiment tracking","Solo data scientists building reproducible ML pipelines without infrastructure overhead","Teams migrating from external experiment tracking platforms to Git-native workflows"],"limitations":["Requires Git repository initialization — cannot track experiments in non-Git projects","Experiment metadata stored in Git commits increases repository size for large-scale hyperparameter sweeps","No built-in support for distributed experiment tracking across multiple machines without manual Git synchronization","Comparison UI limited to experiments within the same Git repository — cross-repo comparisons require manual export"],"requires":["Git 2.0+ installed and initialized in project directory","DVC (Data Version Control) installed separately as command-line tool","VS Code 1.50+ (inferred from extension marketplace requirements)","Python 3.7+ for DVC runtime"],"input_types":["YAML configuration files (dvc.yaml, params.yaml)","Git commit metadata","Metrics files (JSON, CSV, or custom formats)","Hyperparameter definitions"],"output_types":["Structured experiment comparison tables","Git commit hashes linked to experiments","Metrics snapshots per experiment","Parameter diffs between experiments"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_1","uri":"capability://image.visual.real.time.metrics.visualization.and.plotting","name":"real-time metrics visualization and plotting","description":"Renders customizable dashboards within VS Code that display training metrics, loss curves, and performance plots by parsing metrics files generated during ML training. The extension supports overlaying multiple experiments on a single plot for direct visual comparison, with live updates as new metrics are written to disk during active training runs, enabling developers to monitor model performance without switching to external visualization tools.","intents":["Monitor training progress in real-time by watching loss curves and accuracy metrics update live in VS Code","Compare performance curves across multiple experiments visually to identify which hyperparameters produce better results","Generate publication-ready plots of model performance for reports or papers","Quickly spot training anomalies (divergence, plateauing) without running separate visualization scripts"],"best_for":["ML researchers and practitioners who want integrated visualization without leaving their IDE","Teams building reproducible ML pipelines that need visual experiment comparison","Data scientists iterating rapidly on model architectures and wanting immediate visual feedback"],"limitations":["Metrics file format must be JSON, CSV, or DVC-compatible format — custom binary formats require conversion","Live updates depend on file system polling — may lag 1-5 seconds behind actual metric writes","Plot customization limited to DVC's built-in plot types (line, scatter, confusion matrix) — advanced statistical plots require external tools","Large metrics files (>100MB) may cause UI lag or memory pressure in VS Code","No built-in statistical significance testing or confidence interval visualization"],"requires":["Metrics files written to disk in JSON or CSV format during training","DVC plots configuration defined in dvc.yaml or .dvc/plots directory","VS Code 1.50+ with sufficient memory for rendering large datasets","Training scripts that output metrics in DVC-compatible format"],"input_types":["JSON metrics files","CSV metrics files","DVC plots configuration (YAML)","Real-time file system updates from training processes"],"output_types":["Interactive line plots (loss, accuracy over epochs)","Scatter plots (parameter vs metric relationships)","Confusion matrices (classification metrics)","Overlaid experiment comparison visualizations"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_10","uri":"capability://tool.use.integration.dvc.output.channel.for.debugging.and.logging","name":"dvc output channel for debugging and logging","description":"Streams all DVC command execution output, errors, and logs to a dedicated 'DVC' output channel in VS Code, providing visibility into DVC operations without opening a terminal. The channel captures stdout/stderr from DVC CLI invocations, displays execution status and timing, and enables developers to diagnose failures by reviewing detailed logs without context switching.","intents":["View DVC command output and error messages without opening a terminal","Debug failed DVC operations by reviewing detailed logs in the Output panel","Monitor long-running operations (dvc push, dvc pull) with progress and status updates","Understand DVC behavior and troubleshoot configuration issues through detailed logging"],"best_for":["Developers preferring integrated IDE logging over terminal windows","Teams standardizing on VS Code workflows without terminal access","Users debugging DVC configuration issues and needing detailed error messages"],"limitations":["Output channel displays only text — no structured logging or filtering","Large operations (dvc push with GB-scale data) may produce verbose logs that are difficult to parse","No log persistence — logs are cleared when VS Code restarts unless manually saved","Real-time streaming may lag for very fast operations — timing information may be inaccurate"],"requires":["VS Code 1.50+ with Output panel support","DVC installed and executable from VS Code's PATH"],"input_types":["DVC command execution output (stdout/stderr)","DVC operation status and timing information"],"output_types":["Text logs in 'DVC' output channel","Error messages and stack traces","Operation status and timing information"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_2","uri":"capability://data.processing.analysis.data.versioning.and.remote.storage.synchronization","name":"data versioning and remote storage synchronization","description":"Tracks large datasets, model files, and binary artifacts using DVC's content-addressable storage model, storing file hashes in Git while actual data is versioned separately on remote backends (S3, Azure Blob, GCS, NFS). The extension provides UI controls to push/pull data to/from remote storage, display synchronization status in the file tree, and manage data dependencies across experiments without bloating the Git repository with large files.","intents":["Version large datasets (GB-scale) alongside code without storing binary data in Git","Share training datasets with team members by pushing to cloud storage and pulling on their machines","Recover previous versions of datasets by checking out historical DVC metadata files","Track which dataset version was used for each experiment to ensure reproducibility"],"best_for":["ML teams working with large datasets (>100MB) that cannot be stored in Git","Organizations with cloud infrastructure (AWS S3, Azure, GCP) for centralized data storage","Teams requiring data lineage tracking and reproducible dataset versioning","Projects with multiple contributors sharing large training datasets"],"limitations":["Requires separate cloud storage account and credentials configuration — adds infrastructure complexity","Data synchronization is manual (push/pull commands) — no automatic sync on Git operations","Network bandwidth for large datasets can be prohibitive — no built-in compression or delta sync","Remote storage costs (S3, Azure, GCS) are user's responsibility — extension provides no cost estimation","No built-in data validation or integrity checking beyond hash verification","Concurrent modifications to data files on multiple machines can cause conflicts requiring manual resolution"],"requires":["DVC installed and initialized in project (.dvc directory)","Remote storage backend configured (S3, Azure Blob, GCS, or NFS)","Cloud credentials (AWS access keys, Azure SAS tokens, GCP service account) configured in DVC config","Network connectivity to remote storage","Sufficient local disk space for cached data during sync operations"],"input_types":["Large binary files (datasets, model checkpoints, images)","DVC metadata files (.dvc files containing hashes and remote paths)","Remote storage configuration (dvc remote add commands)","File system paths to data directories"],"output_types":["DVC metadata files (.dvc) stored in Git","Synchronization status indicators (synced, pending, missing)","Data file tree with version information","Remote storage paths and access logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_3","uri":"capability://automation.workflow.dvc.tracked.file.state.visualization.in.explorer","name":"dvc-tracked file state visualization in explorer","description":"Augments VS Code's file explorer with a dedicated 'DVC Tracked' panel that displays the status of all DVC-versioned files and directories, showing synchronization state (synced, modified, missing, not-downloaded) with visual indicators. The extension parses DVC metadata files (.dvc) and remote storage state to provide at-a-glance visibility into which data files are tracked, which versions are cached locally, and which require synchronization.","intents":["Quickly identify which files in the project are DVC-tracked vs regular Git files","See which datasets are missing locally and need to be pulled from remote storage","Understand data synchronization status without running CLI commands","Navigate to specific DVC-tracked files for inspection or modification"],"best_for":["ML teams new to DVC who need visual guidance on data versioning status","Developers managing multiple datasets and wanting quick status overview","Teams with distributed data across local and remote storage needing visibility"],"limitations":["Status indicators update on file system events — may lag if external processes modify DVC files","Large projects with thousands of DVC-tracked files may cause UI lag or tree rendering slowness","No filtering or search within the DVC Tracked panel — requires manual tree navigation","Status only reflects local cache state — does not show remote storage consistency without explicit sync check"],"requires":["DVC initialized in project (.dvc directory present)","DVC metadata files (.dvc) present in project","VS Code file watcher enabled (default behavior)"],"input_types":["DVC metadata files (.dvc containing file hashes and remote paths)","Local file system state (cached files, missing files)","Remote storage metadata (if remote is configured)"],"output_types":["Tree view of DVC-tracked files with status badges","Visual indicators (icons, colors) for sync status","File paths and version information"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_4","uri":"capability://automation.workflow.reproducible.ml.pipeline.definition.and.execution","name":"reproducible ml pipeline definition and execution","description":"Enables developers to define ML pipelines as code using dvc.yaml configuration files that specify data inputs, training scripts, hyperparameters, and expected outputs. The extension integrates with DVC's pipeline execution engine to run reproducible workflows where each stage is re-executed only if its inputs (code, data, parameters) have changed, with full dependency tracking and artifact versioning to ensure experiments are repeatable across machines and time.","intents":["Define multi-stage ML workflows (data preprocessing, training, evaluation) as declarative YAML pipelines","Re-run only the pipeline stages affected by code or data changes, avoiding redundant computation","Ensure experiments are reproducible by capturing exact versions of code, data, and parameters used","Share complete ML workflows with team members who can reproduce results by pulling data and running the pipeline"],"best_for":["ML teams building complex multi-stage pipelines requiring reproducibility","Organizations standardizing on infrastructure-as-code practices for ML workflows","Projects with expensive training stages (GPU-intensive) where incremental re-execution saves costs"],"limitations":["Pipeline definition requires YAML syntax knowledge — no visual pipeline builder in extension","Dependency detection based on file hashes — circular dependencies or dynamic dependencies not automatically detected","No built-in support for distributed pipeline execution across multiple machines — requires external orchestration","Pipeline execution is sequential by default — parallel stage execution requires manual configuration","Debugging failed pipeline stages requires manual inspection of logs — no integrated debugger"],"requires":["dvc.yaml file in project root defining pipeline stages","params.yaml file defining hyperparameters (optional but recommended)","DVC installed with pipeline execution support","Training scripts referenced in dvc.yaml must be executable and accept parameter files as input"],"input_types":["dvc.yaml pipeline definition (YAML)","params.yaml hyperparameter definitions (YAML)","Training scripts (Python, shell, or other executables)","Input data files referenced in pipeline stages"],"output_types":["Pipeline execution logs and stage outputs","Metrics and artifacts produced by each stage","Dependency graph visualization","Execution status and timing information"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_5","uri":"capability://automation.workflow.source.control.panel.integration.for.dvc.status","name":"source control panel integration for dvc status","description":"Adds a 'DVC' panel to VS Code's Source Control view that displays workspace-level DVC status alongside Git status, showing pending data synchronization operations, modified DVC metadata files, and overall project health. The panel provides quick-access buttons to trigger common DVC operations (push, pull, repro) without opening the command palette, integrating data versioning status into the same UI surface developers use for Git operations.","intents":["See DVC synchronization status in the same Source Control panel where Git status is displayed","Quickly push/pull data to/from remote storage with single-click buttons","Understand project readiness for commit by seeing both Git and DVC status together","Trigger pipeline re-execution from the Source Control panel without CLI commands"],"best_for":["Developers familiar with Git workflows who want DVC operations in the same UI paradigm","Teams wanting unified version control status (code + data) in a single panel","Users preferring GUI interactions over CLI commands for common DVC operations"],"limitations":["Panel displays only high-level status — detailed operation logs require opening Output panel","No progress indication for long-running operations (large data push/pull) — operations appear to hang","Button availability depends on project state — some operations disabled if prerequisites not met (e.g., no remote configured)","No undo capability for destructive operations (dvc remove) — accidental deletion requires manual recovery"],"requires":["VS Code 1.50+ with Source Control API support","DVC initialized in project","Git repository initialized"],"input_types":["DVC project state (metadata files, remote configuration)","Git repository state","User button clicks in Source Control panel"],"output_types":["Status text and icons in Source Control panel","Operation execution (push, pull, repro commands)","Execution logs in Output panel"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_6","uri":"capability://tool.use.integration.command.palette.integration.for.dvc.operations","name":"command palette integration for dvc operations","description":"Registers DVC-prefixed commands in VS Code's Command Palette (accessible via Ctrl+Shift+P), enabling developers to invoke DVC operations (dvc push, dvc pull, dvc repro, dvc dag) using fuzzy search without memorizing CLI syntax. Commands are discoverable through the palette's search and include contextual help, with execution output streamed to the dedicated 'DVC' output channel for debugging.","intents":["Execute DVC operations without opening a terminal or memorizing CLI syntax","Discover available DVC commands through fuzzy search in the Command Palette","View command execution output and logs in the integrated Output panel","Bind custom keyboard shortcuts to frequently-used DVC operations"],"best_for":["Developers preferring GUI interactions over CLI for common operations","Teams standardizing on VS Code workflows without terminal access","Users wanting keyboard shortcuts for DVC operations without CLI knowledge"],"limitations":["Command Palette search requires knowing command names — discoverability limited to fuzzy matching","No command history or favorites — frequently-used commands require repeated search","Long-running operations (dvc push with large datasets) block the Command Palette UI until completion","Error messages from DVC CLI may be cryptic — requires knowledge of DVC error codes for troubleshooting"],"requires":["VS Code 1.50+ with Command Palette support","DVC installed and accessible from VS Code's PATH","Project initialized with DVC"],"input_types":["Command name and arguments from Command Palette search","User confirmation for destructive operations"],"output_types":["Command execution output streamed to 'DVC' output channel","Status messages in VS Code status bar","File system changes from DVC operations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_7","uri":"capability://data.processing.analysis.experiment.comparison.and.filtering","name":"experiment comparison and filtering","description":"Provides UI for navigating and filtering experiments tracked in Git, enabling developers to compare metrics, parameters, and outputs across multiple training runs. The extension displays experiments as a sortable table where rows represent experiments (Git commits) and columns represent metrics/parameters, with highlighting to show which experiments achieved best performance and filtering to focus on specific parameter ranges or metric thresholds.","intents":["Compare metrics across 10+ experiments to identify which hyperparameters produce best results","Filter experiments by parameter range (e.g., learning_rate > 0.001) to focus on promising configurations","Sort experiments by metric (accuracy, loss) to quickly find best-performing models","Identify which code changes (Git commits) led to metric improvements or regressions"],"best_for":["ML researchers running hyperparameter sweeps and needing to analyze results","Teams comparing multiple model architectures and training approaches","Data scientists iterating rapidly on experiments and needing quick comparison"],"limitations":["Comparison UI limited to experiments within single Git repository — cross-repo comparisons require manual export","Filtering based on simple parameter ranges — no support for complex boolean queries or statistical filtering","Large experiment counts (>1000) may cause UI lag when rendering comparison table","No built-in statistical significance testing — requires manual analysis to determine if metric differences are meaningful","Experiment metadata must be stored in Git commits — external experiment tracking systems cannot be integrated"],"requires":["Multiple experiments tracked as Git commits with DVC metrics","Metrics files in JSON or CSV format","dvc.yaml or params.yaml defining experiment parameters"],"input_types":["Git commit history with experiment metadata","Metrics files (JSON, CSV)","Parameter definitions (params.yaml)","User filter and sort selections"],"output_types":["Experiment comparison table (experiments × metrics/parameters)","Filtered experiment subsets","Sorted experiment rankings by metric","Highlighted best-performing experiments"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_8","uri":"capability://automation.workflow.setup.and.configuration.wizard","name":"setup and configuration wizard","description":"Provides a guided setup interface accessible via 'DVC: Show Setup' command that walks developers through initializing DVC in a project, configuring remote storage backends, and validating prerequisites. The wizard checks for DVC installation, Git repository initialization, and cloud credentials, providing clear error messages and remediation steps if configuration is incomplete.","intents":["Initialize DVC in a new project without CLI knowledge","Configure remote storage (S3, Azure, GCS) through a guided UI","Validate that all prerequisites are met before starting data versioning","Troubleshoot configuration issues with clear error messages and remediation steps"],"best_for":["Teams new to DVC wanting guided onboarding","Developers unfamiliar with CLI tools preferring UI-driven setup","Organizations standardizing on DVC across projects and needing consistent configuration"],"limitations":["Setup wizard covers only basic configuration — advanced DVC settings require manual dvc config commands","Cloud credential input in wizard may be insecure if VS Code extensions lack secure credential storage","Wizard cannot validate cloud credentials without making test API calls — may fail silently if credentials are invalid","No rollback capability if setup is partially completed — requires manual cleanup of .dvc directory"],"requires":["VS Code 1.50+","Git repository initialized","DVC installable (Python 3.7+ available)"],"input_types":["User selections in wizard UI (remote storage type, credentials)","Project configuration (Git repository path)"],"output_types":["Initialized .dvc directory","dvc remote configuration","Validation status and error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-lakefs-lakefs-dvc__cap_9","uri":"capability://automation.workflow.offline.first.data.versioning.without.external.services","name":"offline-first data versioning without external services","description":"Operates entirely within the local VS Code environment and Git repository without requiring external databases, SaaS platforms, or cloud services for core functionality. All experiment metadata, metrics, and data versioning information is stored in Git commits and local DVC metadata files, with optional remote storage for data synchronization but no mandatory cloud dependency or subscription requirement.","intents":["Version ML experiments and datasets without vendor lock-in or SaaS subscriptions","Work offline on ML projects with full experiment tracking and reproducibility","Keep all experiment data under organizational control without external platforms","Avoid costs and privacy concerns of external experiment tracking services"],"best_for":["Organizations with data privacy requirements preventing cloud-based experiment tracking","Teams wanting to avoid SaaS subscription costs for experiment management","Solo developers and small teams without infrastructure for external platforms","Projects requiring offline development capability"],"limitations":["No built-in collaboration features (shared dashboards, notifications) — requires manual Git synchronization for team sharing","Scaling to thousands of experiments may cause Git repository bloat — no built-in archival or pruning","No cloud-native features (auto-scaling, distributed execution) — all computation must run locally","Data sharing across teams requires manual Git/remote storage setup — no managed collaboration platform"],"requires":["Git repository for experiment storage","Local disk space for experiment metadata and cached data","Optional: remote storage backend (S3, Azure, GCS) for data sharing"],"input_types":["Local project files and Git repository","Optional: remote storage credentials"],"output_types":["Git commits with experiment metadata","Local DVC metadata files","Optional: data pushed to remote storage"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"high","permissions":["Git 2.0+ installed and initialized in project directory","DVC (Data Version Control) installed separately as command-line tool","VS Code 1.50+ (inferred from extension marketplace requirements)","Python 3.7+ for DVC runtime","Metrics files written to disk in JSON or CSV format during training","DVC plots configuration defined in dvc.yaml or .dvc/plots directory","VS Code 1.50+ with sufficient memory for rendering large datasets","Training scripts that output metrics in DVC-compatible format","VS Code 1.50+ with Output panel support","DVC installed and executable from VS Code's PATH"],"failure_modes":["Requires Git repository initialization — cannot track experiments in non-Git projects","Experiment metadata stored in Git commits increases repository size for large-scale hyperparameter sweeps","No built-in support for distributed experiment tracking across multiple machines without manual Git synchronization","Comparison UI limited to experiments within the same Git repository — cross-repo comparisons require manual export","Metrics file format must be JSON, CSV, or DVC-compatible format — custom binary formats require conversion","Live updates depend on file system polling — may lag 1-5 seconds behind actual metric writes","Plot customization limited to DVC's built-in plot types (line, scatter, confusion matrix) — advanced statistical plots require external tools","Large metrics files (>100MB) may cause UI lag or memory pressure in VS Code","No built-in statistical significance testing or confidence interval visualization","Output channel displays only text — no structured logging or filtering","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.33,"quality":0.32,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.803Z","last_scraped_at":"2026-05-03T15:20:36.253Z","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=dvc-by-lakefs","compare_url":"https://unfragile.ai/compare?artifact=dvc-by-lakefs"}},"signature":"oZLowa/COfZvExC7OdGtx8h73G9whfp6N0N+X4IUtmKeZFww40p6w06LhQSRHaTTC0Ns19EOG6hv2oGZ7CE4Ag==","signedAt":"2026-06-21T20:13:24.340Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dvc-by-lakefs","artifact":"https://unfragile.ai/dvc-by-lakefs","verify":"https://unfragile.ai/api/v1/verify?slug=dvc-by-lakefs","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"}}