Versionsverwaltung für ML-Daten und Artefakte
For Versionskontrollsystem für ML-Daten, the strongest matches are treeverse/dvc (DVC is a Git-integrated data versioning tool and pipeline), treeverse/lakefs (lakeFS is a data lake versioning system that provides) and iterative/dvc (DVC is the leading open-source data version control tool). attic-labs/noms and wandb/client round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.
Tools zur Nachverfolgung, Versionierung und Verwaltung großer Machine-Learning-Datensätze und Modell-Artefakte.
Versionsverwaltung für ML-Daten und Artefakte
Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
- treeverse/dvc
treeverse/dvc
15,679Auf GitHub ansehenDVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models using external storage and metadata pointers. It integrates with Git by utilizing placeholders to keep heavy artifacts out of the repository while maintaining a versioned link between code and data. The system manages remote data caches through a synchronization layer that connects local environments to cloud storage or network filesystems. It also functions as an experiment tracker, recording hyperparameters and metrics to compare the performance of different model iterations.
DVC is a Git-integrated data versioning tool and pipeline orchestrator that tracks datasets and ML models via external storage, provides experiment tracking with metrics comparison, and supports cloud storage sync — directly matching the request for Git-like version control of data and ML artifacts.
PythonGit-Integrated Data Versioning - treeverse/lakefs
treeverse/lakeFS
5,406Auf GitHub ansehenlakeFS is a data lake versioning system that provides Git-like branching and commits for large datasets stored in object storage. It functions as a version control layer, enabling the creation of immutable snapshots, atomic commits, and zero-copy branching to create isolated environments for data experimentation without duplicating physical files. The system serves as an S3-compatible storage gateway and an Iceberg REST catalog, allowing standard cloud storage protocols and compatible clients to manage versioned tables. It acts as a data quality gatekeeper by using an event-driven hook system
lakeFS is a data lake versioning system that provides Git-like branching and commits for large datasets stored in object storage, making it a direct fit for version controlling datasets with Git workflows; while it focuses on data lakes rather than ML-specific artifacts, it covers dataset versioning, snapshotting, and cloud integration.
GoDifferential Dataset ComparisonsData Versioning SystemsS3-Compatible Storage Adapters - iterative/dvc
iterative/dvc
15,680Auf GitHub ansehenDVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models. It functions as a system for managing large data artifacts by storing lightweight metadata in version control while keeping the actual binaries in a separate cache. The project serves as an experiment tracker and remote storage synchronizer, enabling the execution and comparison of machine learning iterations based on hyperparameters and performance metrics. It provides a bridge for pushing and pulling these large data artifacts between local environments and cloud or on-premi
DVC is the leading open-source data version control tool that tracks datasets and ML artifacts with Git-like workflows, supporting large file storage, pipeline orchestration, model registry, dataset snapshotting, cloud storage sync, and diff/compare—covering everything needed for this search.
PythonDataset Versioning SystemsPointer-Based TrackingContent-Addressable Storage - attic-labs/noms
attic-labs/noms
7,422Auf GitHub ansehenNoms is a distributed version control database and content-addressable data store. It identifies data by cryptographic hashes to ensure integrity and deduplication, while tracking dataset state changes through a sequence of immutable commits to enable branching, forking, and historical recovery. The system functions as a peer-to-peer data synchronizer, reconciling state between disconnected database instances to ensure all nodes converge on the same data. It distinguishes itself as a schema-flexible document store that supports self-describing types, allowing schemas to evolve and widen as ne
Noms is a distributed version-control database that uses commits and branching to version datasets, directly matching the Git-like workflow you need for data versioning, though it does not include ML-specific pipeline tracking or a model registry.
GoVersioned Dataset Snapshots - wandb/client
wandb/client
11,128Auf GitHub ansehenThis project is a collection of utilities designed for machine learning experiment tracking, data versioning, and the observability of large language model applications. It provides a client for recording hyperparameters and metrics during training to visualize performance trends and compare different model versions. The tool includes a model evaluation framework that uses custom scorers and automated judges to assess the quality of generated text outputs. It also provides observability tools to monitor and debug the execution flow and runtime behavior of language model applications. The sys
This repository is the Python client for Weights & Biases, a platform that provides Git-like versioning for datasets and ML artifacts, experiment tracking, model registry, and dataset snapshotting — exactly the kind of tool this search is after, though you'll need the wandb service to store and retrieve versions.
PythonExperiment TrackingArtifact VersioningData Lineage
