{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dvc","slug":"dvc","name":"DVC","type":"repo","url":"https://github.com/iterative/dvc","page_url":"https://unfragile.ai/dvc","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dvc__cap_0","uri":"capability://data.processing.analysis.content.addressable.data.versioning.with.git.tracked.metadata","name":"content-addressable data versioning with git-tracked metadata","description":"DVC versions large files and ML models by computing content hashes (checksums) and storing metadata (.dvc files) in Git while keeping actual data in local cache or remote storage. Uses a Repo class that coordinates cache management, remote synchronization, and Git integration to enable data versioning without bloating the Git repository. The Output class associates files with their checksums and manages retrieval from content-addressable storage, enabling efficient deduplication across experiments and team members.","intents":["I want to version control large datasets and models without storing them in Git","I need to track which version of a dataset was used to train a specific model","I want my team to share large files efficiently without duplicating storage"],"best_for":["ML teams managing datasets >100MB","Data scientists building reproducible pipelines","Organizations with limited Git storage budgets"],"limitations":["Requires separate remote storage configuration (S3, GCS, Azure) for team collaboration — local-only workflows don't enable sharing","Hash computation adds latency on first-time data addition (scales with file size)","No built-in encryption at rest — relies on remote storage provider security"],"requires":["Git repository initialized","Python 3.8+","Remote storage credentials (optional for local-only use)"],"input_types":["files","directories","binary data"],"output_types":[".dvc metadata files","checksums","cache entries"],"categories":["data-processing-analysis","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_1","uri":"capability://planning.reasoning.declarative.pipeline.dag.definition.with.stage.dependencies","name":"declarative pipeline dag definition with stage dependencies","description":"DVC pipelines are defined as directed acyclic graphs (DAGs) where each Stage represents a computational step with explicit dependencies (inputs) and outputs. The Stage class tracks command execution, input/output relationships, and reproduction status. The Repo class maintains a pipeline index that resolves dependency chains, enabling DVC to determine which stages need rerunning when inputs change. Pipeline definitions are stored in dvc.yaml files, making them version-controllable and shareable.","intents":["I want to define a multi-step data processing pipeline that only reruns affected stages","I need to visualize dependencies between data preparation, training, and evaluation steps","I want to ensure reproducibility by declaring all inputs and outputs explicitly"],"best_for":["ML engineers building multi-stage training pipelines","Data teams with complex ETL workflows","Projects requiring reproducible, auditable data lineage"],"limitations":["DAG must be acyclic — no support for iterative/looping constructs natively (requires external orchestration)","Stage execution is sequential by default; parallel execution requires manual queue configuration","No built-in error recovery or retry logic — failed stages require manual intervention"],"requires":["dvc.yaml file in repository root","Git repository","Python 3.8+"],"input_types":["YAML pipeline definitions","command strings","file paths"],"output_types":["DAG structure","execution logs","stage status"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_10","uri":"capability://tool.use.integration.git.scm.integration.for.metadata.tracking.and.history","name":"git scm integration for metadata tracking and history","description":"DVC integrates deeply with Git through an SCM (Source Control Management) abstraction that enables tracking .dvc metadata files, reading Git history, and managing experiment branches. The SCM class provides methods to commit files, create branches, read commit history, and resolve Git conflicts. This integration allows DVC to store pipeline definitions and metadata in Git while keeping large data files separate. The experiment system leverages Git branching to create isolated experiment variants without polluting the main branch.","intents":["I want to version control my pipeline definitions and experiment metadata in Git","I need to track which Git commit corresponds to which model version","I want to create experiment branches that don't interfere with my main development branch"],"best_for":["Teams using Git for code version control","ML projects requiring code-data-model traceability","Organizations with Git-based CI/CD pipelines"],"limitations":["Requires Git repository — doesn't work with other VCS (Mercurial, SVN)","Git history can become cluttered with experiment branches if not cleaned up regularly","Merge conflicts in dvc.yaml require manual resolution"],"requires":["Git repository initialized","Git 2.0+"],"input_types":["Git commits","branch names","file paths"],"output_types":["commit hashes","branch references","history logs"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_11","uri":"capability://tool.use.integration.configuration.management.with.hierarchical.dvc.config","name":"configuration management with hierarchical .dvc/config","description":"DVC stores configuration in .dvc/config files using INI format, supporting hierarchical configuration (system, global, local, project-level). The Configuration class parses these files and merges settings from multiple levels, with local settings overriding global settings. Configuration includes remote storage URLs, cache settings, authentication credentials, and pipeline parameters. This design enables teams to share project-level config (remotes, cache settings) via Git while keeping sensitive credentials in local .dvc/config.local files (which are .gitignored).","intents":["I want to configure S3 remote storage for my team without committing AWS credentials to Git","I need to override cache settings for a specific project without affecting other projects","I want to set up different remotes for dev and prod environments"],"best_for":["Teams managing multiple DVC projects with different configurations","Organizations with security requirements (credential isolation)","Projects requiring environment-specific settings (dev/staging/prod)"],"limitations":["INI format doesn't support complex nested structures — requires flattening for hierarchical config","No schema validation — invalid config values are silently ignored or cause runtime errors","Credentials in environment variables are not encrypted — requires secure environment setup"],"requires":[".dvc directory in Git repository","Python 3.8+"],"input_types":["INI config files","environment variables","command-line arguments"],"output_types":["parsed configuration","merged settings"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_12","uri":"capability://tool.use.integration.python.api.for.programmatic.dvc.operations","name":"python api for programmatic dvc operations","description":"DVC exposes a Python API through the Repo class that enables developers to programmatically perform DVC operations (add data, run pipelines, track experiments) without using the CLI. The API provides methods like repo.add(), repo.run(), repo.reproduce(), and repo.experiments.run() that mirror CLI commands. This enables integration with Jupyter notebooks, custom scripts, and external tools. The API is built on the same core components as the CLI (Repo, Stage, Output classes), ensuring consistency between programmatic and CLI usage.","intents":["I want to add data to DVC from a Jupyter notebook without running CLI commands","I need to programmatically trigger pipeline reproduction from a custom script","I want to integrate DVC operations into my Python-based ML framework"],"best_for":["Data scientists using Jupyter notebooks","ML engineers building custom training frameworks","Teams integrating DVC into Python-based tools"],"limitations":["API is less documented than CLI — some operations may require reading source code","Error handling is inconsistent between API and CLI — exceptions may differ","No async/await support — blocking operations can freeze Jupyter notebooks"],"requires":["Python 3.8+","dvc package installed","Git repository"],"input_types":["file paths","configuration dicts","parameter values"],"output_types":["operation results","status objects","experiment records"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_13","uri":"capability://data.processing.analysis.status.and.diff.reporting.for.data.parameters.and.metrics","name":"status and diff reporting for data, parameters, and metrics","description":"DVC provides status and diff commands that compare current workspace state against cached/committed state. The status command shows which files have changed, which stages need rerunning, and which experiments have uncommitted results. The diff command compares parameters and metrics across Git commits or experiments, showing which values changed and by how much. These commands use the checksum-based tracking system to detect changes efficiently without recomputing hashes.","intents":["I want to see which data files have changed since the last commit","I need to compare metrics between two experiments to see which performed better","I want to understand which parameters changed between two model versions"],"best_for":["ML teams reviewing experiment results","Data scientists tracking pipeline changes","Teams requiring change audits for compliance"],"limitations":["Diff is text-based — doesn't show semantic differences (e.g., schema changes in CSV files)","Status computation can be slow for large datasets (requires checksum recomputation)","No built-in filtering — comparing 100+ metrics produces verbose output"],"requires":["dvc.yaml with metrics and params sections","Git repository with committed dvc.yaml"],"input_types":["Git commits","experiment IDs","file paths"],"output_types":["status reports","diff tables","change summaries"],"categories":["data-processing-analysis","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_2","uri":"capability://automation.workflow.smart.pipeline.re.execution.with.dependency.aware.caching","name":"smart pipeline re-execution with dependency-aware caching","description":"DVC implements intelligent pipeline reproduction by computing checksums of stage inputs (code, data, parameters) and comparing against cached results. The Repo class maintains a cache index that tracks which outputs correspond to which input states. When a stage's dependencies change, DVC detects this via checksum mismatch and marks only affected downstream stages for rerunning. This avoids redundant computation while guaranteeing reproducibility because outputs are tied to specific input states.","intents":["I want to rerun only the pipeline stages affected by my code or data changes","I need to avoid retraining models when only evaluation code changed","I want to cache intermediate results so experiments run faster"],"best_for":["ML teams iterating on feature engineering or model training","Data scientists with long-running pipelines (hours/days)","Projects with expensive compute stages (GPU training, large-scale processing)"],"limitations":["Cache invalidation is based on file checksums only — doesn't detect semantic changes (e.g., parameter value changes in code without dvc.yaml updates)","Cache is local by default; distributed caching across team requires remote storage setup","No built-in garbage collection — cache can grow unbounded without manual cleanup"],"requires":["dvc.yaml with stage definitions","Git repository with committed dvc.yaml","Python 3.8+"],"input_types":["stage definitions","file checksums","parameter files"],"output_types":["execution status","cache hits/misses","rerun decisions"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_3","uri":"capability://data.processing.analysis.multi.backend.remote.storage.synchronization","name":"multi-backend remote storage synchronization","description":"DVC abstracts storage backends (S3, GCS, Azure Blob, HDFS, SSH, local paths) through a unified Remote Storage interface. The Repo class manages remote configuration and coordinates push/pull operations that synchronize data between local cache and remote storage. Remote storage is configured in .dvc/config files and supports authentication via environment variables or credential files. This enables teams to store large files in cloud buckets while keeping local workspaces clean, with automatic deduplication across users.","intents":["I want to push large datasets to S3 so my team can pull them without duplicating storage","I need to switch between multiple storage backends (dev S3 bucket, prod GCS)","I want to keep my local machine clean by storing data remotely and pulling only what I need"],"best_for":["Distributed ML teams using cloud storage","Organizations with multi-cloud strategies","Projects with datasets too large for local storage"],"limitations":["No built-in bandwidth throttling — large push/pull operations can saturate network","Authentication is credential-based; no native support for temporary STS tokens or OIDC","Sync operations are not atomic — interruption mid-transfer can leave partial files in remote storage"],"requires":["Remote storage credentials (AWS_ACCESS_KEY_ID, GOOGLE_APPLICATION_CREDENTIALS, etc.)","Network connectivity to remote storage",".dvc/config with remote URL configured"],"input_types":["local cache files","remote URLs","credentials"],"output_types":["synchronized files","transfer logs","status reports"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_4","uri":"capability://planning.reasoning.experiment.tracking.with.parameter.and.metrics.extraction","name":"experiment tracking with parameter and metrics extraction","description":"DVC's experiment system tracks ML experiments by capturing parameters (hyperparameters, configuration) and metrics (accuracy, loss, F1) from runs. Parameters are read from YAML/JSON files specified in dvc.yaml, while metrics are extracted from output files (JSON, CSV, YAML). The Experiment class queues and executes experiment variants, storing results in a local Git-based experiment registry. Experiments are compared via a diff system that shows parameter and metric changes across runs, enabling data-driven model selection.","intents":["I want to track which hyperparameters and metrics correspond to each model training run","I need to compare 10 different model configurations and see which performed best","I want to reproduce a specific experiment from 2 weeks ago by checking out its parameters and code"],"best_for":["ML researchers running hyperparameter sweeps","Teams comparing multiple model architectures","Projects requiring experiment audit trails for compliance"],"limitations":["Metrics must be explicitly written to files (JSON, CSV, YAML) — no automatic capture from training logs","Experiment queue is local only; no built-in distributed experiment execution across machines","Parameter changes require re-running the entire pipeline — no partial parameter updates"],"requires":["dvc.yaml with params and metrics sections","Parameter files (params.yaml, config.json, etc.)","Metrics output files written by training scripts","Git repository"],"input_types":["parameter files","metrics files","dvc.yaml definitions"],"output_types":["experiment records","comparison tables","diff reports"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_5","uri":"capability://automation.workflow.parameter.driven.pipeline.templating.and.sweeping","name":"parameter-driven pipeline templating and sweeping","description":"DVC enables parameterized pipelines where stage commands reference variables from params.yaml or other parameter files. Parameters are injected into stage commands at execution time, allowing the same pipeline definition to run with different configurations. The Experiment system extends this with parameter sweeping — automatically generating experiment variants by iterating over parameter ranges or grids. This is implemented through the Experiment queue, which creates multiple experiment branches with different parameter values.","intents":["I want to run the same training pipeline with learning rates [0.001, 0.01, 0.1] without editing dvc.yaml","I need to test different data preprocessing parameters and compare results","I want to define a parameter grid and automatically run all combinations"],"best_for":["ML engineers doing hyperparameter optimization","Data scientists testing multiple feature engineering approaches","Teams with standardized pipeline templates"],"limitations":["Parameter sweeping is not distributed — all variants run sequentially on a single machine","No built-in Bayesian optimization or smart sampling — only grid/random search via external tools","Parameter types are inferred from YAML; no schema validation for parameter ranges"],"requires":["params.yaml or equivalent parameter file","dvc.yaml with ${param_name} placeholders in stage commands","Git repository"],"input_types":["parameter files (YAML/JSON)","parameter ranges","dvc.yaml with variable references"],"output_types":["experiment variants","parameter combinations","execution results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_6","uri":"capability://planning.reasoning.dag.visualization.and.pipeline.dependency.analysis","name":"dag visualization and pipeline dependency analysis","description":"DVC generates visual representations of pipeline DAGs showing stage dependencies, inputs, and outputs. The visualization system parses dvc.yaml and builds a dependency graph, then renders it as a directed graph (typically in Mermaid or Graphviz format). This enables developers to understand data lineage, identify bottlenecks, and verify pipeline structure. The diff system also visualizes how pipeline structure changes across Git commits.","intents":["I want to see a diagram of how my data flows through preprocessing, training, and evaluation stages","I need to understand which stages depend on which data files","I want to identify stages that could run in parallel to speed up my pipeline"],"best_for":["ML teams onboarding new members to complex pipelines","Data engineers documenting data lineage","Projects requiring pipeline audits or compliance documentation"],"limitations":["Visualization is static — doesn't show runtime execution flow or actual data sizes","No interactive exploration of DAG (e.g., clicking to drill into stage details)","Large pipelines (100+ stages) produce cluttered visualizations"],"requires":["dvc.yaml with stage definitions","Graphviz or Mermaid renderer (for CLI output)"],"input_types":["dvc.yaml","stage definitions"],"output_types":["graph images (PNG, SVG)","Mermaid/Graphviz markup"],"categories":["planning-reasoning","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_7","uri":"capability://data.processing.analysis.metrics.and.plots.extraction.with.multi.format.support","name":"metrics and plots extraction with multi-format support","description":"DVC extracts metrics (scalar values like accuracy, loss) and plots (data for visualization like confusion matrices, ROC curves) from training outputs in multiple formats (JSON, CSV, YAML, TSV). The Metrics class parses these files and stores them in the experiment registry. Plots are rendered as interactive visualizations (line charts, scatter plots, confusion matrices) in the DVC UI or exported as static images. This enables teams to compare model performance across experiments without manually parsing output files.","intents":["I want to extract accuracy and loss from my training script's JSON output and compare across experiments","I need to visualize confusion matrices for different model variants","I want to plot training loss over epochs and compare learning curves across runs"],"best_for":["ML teams comparing model performance metrics","Data scientists analyzing training dynamics","Projects requiring automated performance dashboards"],"limitations":["Metrics must be written to files — no real-time streaming of metrics during training","Plot rendering is limited to built-in chart types (line, scatter, confusion matrix) — custom visualizations require external tools","No automatic outlier detection or anomaly alerts on metrics"],"requires":["Metrics output files (JSON, CSV, YAML, TSV)","dvc.yaml with metrics section specifying file paths","Python 3.8+"],"input_types":["JSON files","CSV files","YAML files","TSV files"],"output_types":["parsed metrics","plot data","visualizations"],"categories":["data-processing-analysis","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_8","uri":"capability://automation.workflow.git.integrated.experiment.branching.and.reproducibility","name":"git-integrated experiment branching and reproducibility","description":"DVC experiments are stored as Git commits with metadata (parameters, metrics) attached, enabling full reproducibility and version history. When an experiment is queued, DVC creates a new Git branch with modified parameter files and stage outputs. The experiment registry tracks which Git commits correspond to which experiments, enabling developers to checkout a specific experiment's code and data state. This design ensures experiments are reproducible because all inputs (code, data, parameters) are captured in Git history.","intents":["I want to reproduce an experiment from 3 weeks ago by checking out its exact code and data state","I need to share an experiment with a colleague by sending them a Git commit hash","I want to compare two experiments and see exactly what code and parameter changes caused the difference"],"best_for":["ML teams requiring experiment reproducibility and audit trails","Organizations with compliance requirements (healthcare, finance)","Projects where experiments must be shareable via Git"],"limitations":["Experiment branches can proliferate, cluttering Git history (requires manual cleanup)","Merging experiments back to main branch requires manual conflict resolution","No built-in experiment garbage collection — old experiments remain in Git history indefinitely"],"requires":["Git repository","dvc.yaml with params and metrics","Parameter files (params.yaml)"],"input_types":["experiment definitions","parameter files","Git commits"],"output_types":["experiment branches","Git commits","reproducibility metadata"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__cap_9","uri":"capability://tool.use.integration.file.system.abstraction.with.local.and.remote.path.handling","name":"file system abstraction with local and remote path handling","description":"DVC abstracts file system operations through a FileSystem interface that supports local paths, cloud storage (S3, GCS, Azure), and remote protocols (SSH, HDFS). This abstraction enables DVC to treat all storage backends uniformly — operations like read, write, exists, and list work identically whether the path is local or remote. The abstraction is implemented through provider-specific classes (LocalFileSystem, S3FileSystem, etc.) that inherit from a common base. This design enables DVC to support new storage backends by implementing the FileSystem interface without modifying core logic.","intents":["I want to read data from S3 using the same code path as local files","I need to support multiple storage backends without duplicating file handling logic","I want to add support for a new storage provider (e.g., MinIO) without modifying DVC core"],"best_for":["Teams using heterogeneous storage backends","Organizations building custom DVC extensions","Projects requiring storage-agnostic data handling"],"limitations":["Abstraction adds ~50-100ms latency per operation due to polymorphic dispatch","Some storage-specific features (e.g., S3 multipart upload) are not exposed through the abstraction","Error handling is generic — storage-specific errors are wrapped, losing provider context"],"requires":["Python 3.8+","Storage provider credentials (for remote backends)"],"input_types":["file paths","storage URLs"],"output_types":["file contents","metadata","status information"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc__headline","uri":"capability://data.processing.analysis.data.version.control.tool.for.machine.learning","name":"data version control tool for machine learning","description":"DVC is an open-source tool that extends Git's capabilities to manage data and ML models, enabling reproducible pipelines and experiment tracking for data science projects.","intents":["best data version control tool","data version control for machine learning","how to track ML experiments","version control for large datasets","reproducible data pipelines solutions"],"best_for":["data scientists","ML engineers"],"limitations":["requires Git knowledge"],"requires":["Git"],"input_types":["datasets","ML models"],"output_types":["versioned data","experiment results"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Git repository initialized","Python 3.8+","Remote storage credentials (optional for local-only use)","dvc.yaml file in repository root","Git repository","Git 2.0+",".dvc directory in Git repository","dvc package installed","dvc.yaml with metrics and params sections","Git repository with committed dvc.yaml"],"failure_modes":["Requires separate remote storage configuration (S3, GCS, Azure) for team collaboration — local-only workflows don't enable sharing","Hash computation adds latency on first-time data addition (scales with file size)","No built-in encryption at rest — relies on remote storage provider security","DAG must be acyclic — no support for iterative/looping constructs natively (requires external orchestration)","Stage execution is sequential by default; parallel execution requires manual queue configuration","No built-in error recovery or retry logic — failed stages require manual intervention","Requires Git repository — doesn't work with other VCS (Mercurial, SVN)","Git history can become cluttered with experiment branches if not cleaned up regularly","Merge conflicts in dvc.yaml require manual resolution","INI format doesn't support complex nested structures — requires flattening for hierarchical config","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","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=dvc","compare_url":"https://unfragile.ai/compare?artifact=dvc"}},"signature":"FdOVr2nW7riHO5XN2T4xP2ad7uXVGNwjppyxo4wUHOp5KirEI08/ekSkj/VOvKxvjXn87R4jb5+WhVkayyU+Bg==","signedAt":"2026-06-21T00:52:23.269Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dvc","artifact":"https://unfragile.ai/dvc","verify":"https://unfragile.ai/api/v1/verify?slug=dvc","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"}}