{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-dvc","slug":"pypi-dvc","name":"dvc","type":"cli","url":"https://pypi.org/project/dvc/","page_url":"https://unfragile.ai/pypi-dvc","categories":["data-pipelines"],"tags":["ai","collaboration","data-science","data-version-control","developer-tools","git","machine-learning","reproducibility"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-dvc__cap_0","uri":"capability://data.processing.analysis.git.integrated.data.versioning.with.content.addressed.storage","name":"git-integrated data versioning with content-addressed storage","description":"DVC tracks large files and datasets by storing metadata (.dvc files) in Git while maintaining actual data in a content-addressed object database (cache layer). Uses SHA256 hashing to deduplicate data across versions and projects, enabling efficient storage without bloating Git repositories. The Repo class coordinates between Git's SCM layer and DVC's FileSystem abstraction to transparently manage data lifecycle.","intents":["Track large datasets (>100MB) without storing them in Git","Maintain multiple versions of data files with minimal storage overhead","Share data versions across team members via Git commits","Reproduce experiments with exact data versions used in past runs"],"best_for":["ML teams managing datasets >1GB","Data scientists collaborating on shared repositories","Organizations needing audit trails for data provenance"],"limitations":["Requires separate remote storage configuration (S3, GCS, Azure) — local cache alone doesn't enable team sharing","Hash computation adds latency on first add (~1-5s per GB depending on disk I/O)","No built-in encryption at rest — relies on remote storage provider's security"],"requires":["Git repository initialized","Python 3.8+","Write access to .dvc directory in repo root"],"input_types":["files","directories","structured data (CSV, Parquet, etc.)"],"output_types":[".dvc metadata files","cache entries in .dvc/cache","remote storage references"],"categories":["data-processing-analysis","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_1","uri":"capability://automation.workflow.declarative.pipeline.definition.with.dag.based.execution","name":"declarative pipeline definition with dag-based execution","description":"DVC pipelines are defined in dvc.yaml using a declarative YAML format where each stage specifies dependencies (inputs), commands (execution), and outputs (results). The Index and Graph System builds a directed acyclic graph (DAG) from stage definitions, enabling DVC to compute execution order, detect changes, and run only affected stages. The Stage class encapsulates command execution with dependency tracking, while the Output system manages stage artifacts.","intents":["Define multi-step ML workflows (data prep → training → evaluation) in version-controlled YAML","Automatically detect which pipeline stages need re-execution when inputs change","Parallelize independent pipeline stages for faster execution","Reproduce exact pipeline runs from past experiments by re-executing the DAG"],"best_for":["ML engineers building reproducible training pipelines","Data teams with multi-stage ETL workflows","Projects requiring audit trails of computational steps"],"limitations":["No built-in support for conditional branching or loops — complex control flow requires external orchestration","Stage execution is local-only by default; distributed execution requires custom executors or external tools","DAG computation adds ~100-500ms overhead per pipeline run for graph traversal and dependency resolution"],"requires":["dvc.yaml file in repository root","Python 3.8+","Commands must be shell-executable (bash, Python, etc.)"],"input_types":["YAML pipeline definitions","file paths (dependencies)","command strings"],"output_types":["execution logs","stage outputs (files/directories)","DAG visualization (JSON/graph format)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_10","uri":"capability://data.processing.analysis.cache.and.object.database.with.deduplication.and.garbage.collection","name":"cache and object database with deduplication and garbage collection","description":"DVC's Cache and Object Database system stores data using content-addressed storage (SHA256 hashes as keys), enabling automatic deduplication across versions and projects. The CacheManager handles cache operations (add, retrieve, verify), while the object database maintains the actual cached files organized by hash. Garbage collection removes unreferenced cache entries, and cache integrity is verified through hash validation.","intents":["Store data efficiently with automatic deduplication across versions","Retrieve cached data by content hash without re-downloading from remote","Verify cache integrity through hash validation","Clean up unused cache entries to free disk space"],"best_for":["Teams with large datasets requiring efficient storage","Projects with many data versions where deduplication saves significant space","Workflows requiring cache integrity verification"],"limitations":["Cache is local-only — doesn't automatically sync across team members","Garbage collection requires explicit invocation (dvc gc) — no automatic cleanup","Hash collision detection is probabilistic (SHA256 collisions are extremely rare but theoretically possible)","Cache corruption can occur if files are modified outside DVC — requires manual recovery"],"requires":["Write access to .dvc/cache directory","Python 3.8+","Sufficient disk space for cached data"],"input_types":["file paths","content hashes (SHA256)"],"output_types":["cached file entries","cache statistics (size, entry count)","garbage collection reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_11","uri":"capability://planning.reasoning.index.and.dependency.graph.construction.with.change.detection","name":"index and dependency graph construction with change detection","description":"DVC's Index and Graph System builds a directed acyclic graph (DAG) from stage definitions, tracking dependencies between stages and detecting which stages need re-execution when inputs change. The Index class maintains the graph structure and provides methods for traversal and change detection. This enables efficient incremental execution by identifying affected stages without re-running the entire pipeline.","intents":["Build dependency graphs from pipeline definitions for visualization and analysis","Detect which stages are affected by input changes","Compute optimal execution order for pipeline stages","Identify circular dependencies and other graph anomalies"],"best_for":["Complex pipelines with many interdependent stages","Teams requiring pipeline visualization and analysis","Projects needing efficient incremental execution"],"limitations":["Graph construction adds ~100-500ms overhead per pipeline run","No built-in support for dynamic graphs (stages created at runtime)","Circular dependency detection requires full graph traversal","Large graphs (>1000 stages) can consume significant memory"],"requires":["dvc.yaml pipeline definition","Python 3.8+"],"input_types":["stage definitions (YAML)","dependency specifications","output declarations"],"output_types":["DAG structure (graph representation)","execution order (topologically sorted stages)","change detection results"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_12","uri":"capability://tool.use.integration.command.line.interface.with.subcommand.based.operations","name":"command-line interface with subcommand-based operations","description":"DVC provides a comprehensive CLI through the dvc.cli module with subcommands for all major operations (add, run, push, pull, repro, etc.). The CLI uses argparse for argument parsing and provides consistent help/error messages across commands. Each subcommand is implemented as a separate module with a run() method, enabling modular command implementation and testing.","intents":["Initialize and manage DVC repositories from command line","Add and track data files without writing Python code","Define and execute pipelines through CLI commands","Push/pull data to/from remote storage","Run experiments and compare results"],"best_for":["Data scientists preferring CLI over Python API","Teams using shell scripts for automation","Developers integrating DVC into CI/CD pipelines"],"limitations":["CLI is less discoverable than graphical interfaces — requires documentation reading","Complex operations may require chaining multiple commands","Error messages can be verbose or unclear for edge cases","No built-in shell completion for all commands (requires manual setup)"],"requires":["Python 3.8+","DVC installed (pip install dvc)","Shell with standard I/O redirection"],"input_types":["command names","command arguments and flags","file paths"],"output_types":["command output (stdout)","error messages (stderr)","exit codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_13","uri":"capability://tool.use.integration.python.api.for.programmatic.repository.access","name":"python api for programmatic repository access","description":"DVC exposes a Python API through the dvc.api module and Repo class, enabling programmatic access to all DVC operations without CLI invocation. The API provides methods for data operations (add, push, pull), pipeline management (run, repro), and experiment tracking. This enables integration with Jupyter notebooks, custom scripts, and external tools.","intents":["Access DVC operations from Python code without CLI invocation","Integrate DVC into Jupyter notebooks for interactive data science","Build custom tools and workflows on top of DVC","Programmatically manage experiments and pipelines"],"best_for":["Data scientists using Jupyter notebooks","Developers building custom DVC integrations","Teams automating DVC operations in Python scripts"],"limitations":["API documentation is less comprehensive than CLI documentation","Some advanced features may only be available through CLI","API changes between versions can break custom scripts","Error handling requires understanding of DVC exception hierarchy"],"requires":["Python 3.8+","DVC installed (pip install dvc)","Understanding of DVC architecture and concepts"],"input_types":["file paths","repository paths","configuration objects"],"output_types":["Python objects (Repo, Stage, Output, etc.)","operation results","exception objects"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_2","uri":"capability://data.processing.analysis.multi.remote.storage.backend.abstraction.with.cloud.provider.support","name":"multi-remote storage backend abstraction with cloud provider support","description":"DVC abstracts storage operations through a FileSystem abstraction layer that supports S3, GCS, Azure Blob Storage, HDFS, and local paths. The Remote Storage Operations subsystem handles push/pull operations with configurable remote endpoints defined in .dvc/config. Data is transferred using the CacheManager, which manages local cache coherency and remote synchronization, enabling teams to share data without direct file system access.","intents":["Push versioned datasets to cloud storage (S3, GCS, Azure) for team access","Pull specific data versions from remote storage on-demand","Configure multiple remotes with fallback/priority ordering","Sync local cache with remote storage to maintain consistency across team members"],"best_for":["Teams using AWS S3, Google Cloud Storage, or Azure for data lakes","Organizations requiring centralized data storage with Git-tracked metadata","Projects with large datasets requiring efficient bandwidth management"],"limitations":["Requires explicit remote configuration — no auto-discovery of storage backends","Network latency on push/pull operations can be significant for large datasets (100GB+ transfers may take hours)","No built-in bandwidth throttling or resumable transfers — interrupted uploads require restart","Credentials must be managed separately (environment variables, AWS IAM roles, etc.)"],"requires":["Cloud provider account with credentials (AWS_ACCESS_KEY_ID, GOOGLE_APPLICATION_CREDENTIALS, etc.)","Network connectivity to remote storage","Python 3.8+","Provider-specific SDK (boto3 for S3, google-cloud-storage for GCS, etc.)"],"input_types":["local file paths","remote URLs (s3://bucket/path, gs://bucket/path, etc.)","configuration strings"],"output_types":["remote storage objects","sync status reports","cache coherency logs"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_3","uri":"capability://planning.reasoning.experiment.tracking.with.queue.based.execution.and.comparison","name":"experiment tracking with queue-based execution and comparison","description":"DVC's Experiment Management subsystem enables running multiple ML experiments with different parameters/code versions, tracked in a queue system with configurable executors. The Experiment Lifecycle manages experiment creation, execution, and storage, while the Collection system organizes results for comparison. Experiments are stored as Git branches or commits, enabling version control of entire experiment runs including code, parameters, and outputs.","intents":["Queue multiple experiment runs with different hyperparameters for batch execution","Compare metrics and plots across experiments to identify best-performing configurations","Track which code version, parameters, and data were used in each experiment","Reproduce past experiments by checking out experiment commits and re-running pipelines"],"best_for":["ML researchers running hyperparameter sweeps","Teams comparing multiple model architectures or training approaches","Projects requiring experiment reproducibility and audit trails"],"limitations":["Queue system is local-only by default — distributed execution requires custom executors or external orchestration","Experiment storage as Git branches can create large numbers of refs, potentially impacting Git performance","No built-in support for early stopping or adaptive sampling — all queued experiments run to completion","Comparison UI is CLI-only; visualization requires external tools or DVC Studio"],"requires":["dvc.yaml pipeline definition","params.yaml or equivalent parameter file","Git repository with commit history","Python 3.8+"],"input_types":["parameter overrides (YAML or CLI flags)","code changes (Git commits)","pipeline definitions"],"output_types":["experiment metadata (Git commits/branches)","metrics files (JSON/YAML)","plots (CSV, JSON)","comparison reports"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_4","uri":"capability://data.processing.analysis.metrics.and.parameters.tracking.with.visualization","name":"metrics and parameters tracking with visualization","description":"DVC tracks model metrics (accuracy, loss, etc.) and pipeline parameters (learning rate, batch size, etc.) from files (JSON, YAML, CSV) specified in dvc.yaml. The Metrics and Parameters subsystem parses these files and enables comparison across experiments and pipeline runs. The Plots System generates visualizations from metrics data, supporting multiple plot types (line, scatter, confusion matrix) with automatic rendering in compatible tools.","intents":["Track model performance metrics (accuracy, F1, loss) across training runs","Compare parameter values and their corresponding metrics to identify optimal configurations","Generate plots (training curves, confusion matrices) for model evaluation","Visualize metric trends over time or across experiments"],"best_for":["ML practitioners monitoring model training progress","Teams comparing multiple model configurations","Projects requiring performance documentation and reporting"],"limitations":["Metrics must be explicitly written to files — no built-in integration with training frameworks (TensorFlow, PyTorch)","Plot generation is static (PNG/SVG) — no interactive dashboards without external tools","Large metrics files (>100MB) can slow down comparison operations","No built-in alerting or anomaly detection for metrics"],"requires":["Metrics written to JSON, YAML, or CSV files","dvc.yaml with metrics/plots sections defined","Python 3.8+"],"input_types":["JSON/YAML/CSV files containing metrics","parameter files (params.yaml)","plot definitions (dvc.yaml)"],"output_types":["metrics comparison tables","plot images (PNG, SVG)","JSON metric summaries"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_5","uri":"capability://data.processing.analysis.data.import.and.external.repository.integration","name":"data import and external repository integration","description":"DVC enables importing data from external repositories using the External Repository Integration subsystem, which clones remote DVC repos and extracts specific data files/versions. The import operation creates a dependency on the external repo, automatically pulling updates when the external repo changes. This is implemented through the dependency/repo.py module, which handles external repo resolution and data fetching.","intents":["Import datasets from shared DVC repositories without duplicating storage","Create dependencies on external data sources that auto-update when upstream changes","Build data pipelines that reference public or private DVC repos","Reuse preprocessed datasets across multiple projects"],"best_for":["Teams sharing common datasets across multiple projects","Organizations with centralized data repositories","Projects requiring automatic updates from upstream data sources"],"limitations":["External repo must be a valid DVC repository — no support for arbitrary data sources","Import creates a hard dependency on external repo availability — broken links cause pipeline failures","No built-in conflict resolution if external data changes incompatibly","Cloning external repos adds latency to pipeline initialization (network I/O dependent)"],"requires":["External DVC repository URL (local path, Git URL, or HTTP)","Git installed (for cloning external repos)","Network access to external repository","Python 3.8+"],"input_types":["external repository URLs","file paths within external repos","version/branch specifications"],"output_types":["imported data files","dependency metadata (.dvc files)","external repo references"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_6","uri":"capability://data.processing.analysis.state.tracking.and.cache.coherency.management","name":"state tracking and cache coherency management","description":"DVC's State Tracking subsystem maintains a local state database (.dvc/tmp/dvc-state) that records file modification times, sizes, and hashes to detect when data has changed without re-hashing. The CacheManager uses this state information to determine if cached files are still valid or need re-computation. This enables efficient incremental pipeline execution by skipping stages whose inputs haven't changed.","intents":["Detect which files have changed since last pipeline run without re-hashing all data","Skip pipeline stages when inputs are unchanged, enabling fast re-runs","Maintain cache coherency across local and remote storage","Optimize pipeline execution by avoiding redundant computations"],"best_for":["Teams with large pipelines where re-hashing is expensive","Projects with frequent incremental changes to data","Workflows requiring fast iteration cycles"],"limitations":["State database can become stale if files are modified outside DVC (requires manual cache invalidation)","File system time resolution issues on some systems (e.g., HFS+ on macOS) can cause false cache hits","State database is local-only — doesn't sync across team members (each developer has independent state)","Large state databases (>1GB) can slow down state lookups"],"requires":["Write access to .dvc/tmp directory","Python 3.8+","File system supporting modification time tracking"],"input_types":["file paths","modification times","file hashes"],"output_types":["state database entries","cache validity status","change detection results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_7","uri":"capability://tool.use.integration.configuration.management.with.layered.precedence","name":"configuration management with layered precedence","description":"DVC's Configuration System manages settings through multiple layers: system-wide (/etc/dvc/config), user-level (~/.config/dvc/config), and repository-level (.dvc/config) with clear precedence rules. The Config class parses YAML/INI configuration files and provides unified access to settings like remote storage endpoints, cache location, and execution parameters. Configuration can be modified via CLI commands (dvc config) or direct file editing.","intents":["Configure remote storage endpoints (S3, GCS, Azure) for data sharing","Set cache location and size limits for local storage management","Configure authentication credentials for remote storage access","Customize DVC behavior (parallelism, timeouts, etc.) at system/user/repo level"],"best_for":["Teams with centralized DVC configuration (system-level settings)","Organizations requiring per-project remote storage configuration","Developers needing environment-specific settings (dev vs. production)"],"limitations":["Configuration is stored in plain text — credentials should use environment variables or external secret managers","No built-in validation of configuration values — invalid settings may cause runtime errors","Precedence rules can be confusing when settings are defined at multiple levels","No built-in configuration versioning — changes aren't tracked in Git unless .dvc/config is committed"],"requires":["Write access to configuration files (.dvc/config or ~/.config/dvc/config)","Python 3.8+"],"input_types":["YAML/INI configuration files","CLI arguments (dvc config key value)","environment variables"],"output_types":["parsed configuration objects","configuration files","CLI output (dvc config --list)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_8","uri":"capability://automation.workflow.repository.initialization.and.lifecycle.management","name":"repository initialization and lifecycle management","description":"DVC's Repository Management subsystem handles repo initialization (dvc init), which creates the .dvc directory structure with config, cache, and metadata files. The Repo class serves as the central coordinator for all operations, managing initialization state, configuration loading, and lifecycle events. Repository initialization integrates with Git, creating .dvc/.gitignore to exclude cache from version control.","intents":["Initialize a new DVC repository in an existing Git repo","Set up directory structure and configuration for data versioning","Integrate DVC with existing Git workflows","Manage repository state and lifecycle (initialization, cleanup, migration)"],"best_for":["Teams starting new ML projects with Git repositories","Organizations migrating existing projects to DVC","Developers setting up reproducible data science workflows"],"limitations":["Requires existing Git repository — DVC cannot initialize without Git","Initialization creates multiple files (.dvc/config, .dvc/.gitignore, etc.) that must be committed","No built-in migration tools for projects already using other versioning systems (DVC, MLflow, etc.)","Initialization is one-way — no easy way to remove DVC from a repository without manual cleanup"],"requires":["Git repository initialized (git init or git clone)","Write access to repository root","Python 3.8+","Git 2.0+"],"input_types":["repository path","initialization flags (--no-scm, --subdir, etc.)"],"output_types":[".dvc directory structure",".dvc/config file",".dvc/.gitignore file","initialization logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-dvc__cap_9","uri":"capability://tool.use.integration.filesystem.abstraction.with.protocol.agnostic.data.access","name":"filesystem abstraction with protocol-agnostic data access","description":"DVC's Filesystem Abstraction layer provides a unified interface for accessing data across different storage backends (local, S3, GCS, Azure, HDFS) through a common API. The abstraction handles protocol-specific details (authentication, path normalization, error handling) transparently, allowing higher-level components to work with any storage backend without modification. This is implemented through pluggable filesystem classes that inherit from a common base.","intents":["Access data from multiple storage backends (local, S3, GCS, Azure) with unified API","Switch storage backends without changing pipeline code","Handle protocol-specific details (authentication, path normalization) transparently","Support custom storage backends through plugin architecture"],"best_for":["Organizations using multiple cloud providers","Teams requiring flexibility to change storage backends","Projects needing custom storage implementations"],"limitations":["Abstraction adds ~50-100ms latency per operation due to indirection","Not all storage backends support all operations (e.g., some don't support atomic renames)","Error handling is backend-specific — some errors may not be caught by abstraction","Custom filesystem implementations require understanding of base class interface"],"requires":["Python 3.8+","Provider-specific SDK (boto3, google-cloud-storage, etc.) for cloud backends"],"input_types":["file paths (local or remote URLs)","filesystem operations (read, write, list, delete)"],"output_types":["file contents","directory listings","operation results (success/failure)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Git repository initialized","Python 3.8+","Write access to .dvc directory in repo root","dvc.yaml file in repository root","Commands must be shell-executable (bash, Python, etc.)","Write access to .dvc/cache directory","Sufficient disk space for cached data","dvc.yaml pipeline definition","DVC installed (pip install dvc)","Shell with standard I/O redirection"],"failure_modes":["Requires separate remote storage configuration (S3, GCS, Azure) — local cache alone doesn't enable team sharing","Hash computation adds latency on first add (~1-5s per GB depending on disk I/O)","No built-in encryption at rest — relies on remote storage provider's security","No built-in support for conditional branching or loops — complex control flow requires external orchestration","Stage execution is local-only by default; distributed execution requires custom executors or external tools","DAG computation adds ~100-500ms overhead per pipeline run for graph traversal and dependency resolution","Cache is local-only — doesn't automatically sync across team members","Garbage collection requires explicit invocation (dvc gc) — no automatic cleanup","Hash collision detection is probabilistic (SHA256 collisions are extremely rare but theoretically possible)","Cache corruption can occur if files are modified outside DVC — requires manual recovery","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"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":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:23.204Z","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=pypi-dvc","compare_url":"https://unfragile.ai/compare?artifact=pypi-dvc"}},"signature":"tqfTrqDzZ7kL6b+Cg7r0UhGXEa7/n9noG9cnVdu6Ol9mAPOfDzOkaIaa8olLe2LdIKWIelzGKpeScr/WOEibDQ==","signedAt":"2026-06-21T06:26:53.233Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-dvc","artifact":"https://unfragile.ai/pypi-dvc","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}