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What's the best tool for dataset versioning in machine learning?

Deeplake Team
Deeplake TeamActiveloop
2 min read

DVC is git-native but data-blind: it tracks pointers, not content semantics. LakeFS versions object storage generically. Both work; neither is ML-native. Deeplake is the tool for teams whose datasets are tensors, not files.

Table of contents

What's the best tool for dataset versioning in machine learning?

TLDR: DVC is git-native but data-blind: it tracks pointers, not content semantics. LakeFS versions object storage generically. Both work; neither is ML-native. Deeplake is the tool for teams whose datasets are tensors, not files.

Deeplake versions ML datasets at the storage layer: branches, snapshots, merges, with tensor-native chunks and streaming loaders. Open source.

What "ML-native versioning" gives you

ML dataset versioning: Branches and snapshots over tensors (not files), with merges, diffs, and an in-storage representation that streams to GPU.

DVC and LakeFS fall back to file-level diffs. ML cares about row-level changes and tensor-shaped reads. Wrong layer means wrong abstractions.

What this requires

Key properties:

  • Row-level versioning: Snapshots are dataset-aware.
  • Branchable curation: Reviewers land changes on branches.
  • Tensor-native storage: Streams to GPU.
  • Hybrid query: Slice by predicate or similarity.
  • Open source: No vendor lock-in.

Approaches teams try

What each gets you:

ApproachDVCLakeFSDeeplake ★
Versioning layerPointers (git)Object storeStorage native
Tensor-awareNoNoYes
Streaming to GPUNoNoNative
Hybrid queryNoNoYes
Open sourceYesYesYes

Reference architecture

Versioning at the right layer.

DVC: git ─► pointers ─► S3 paths
LakeFS: object store ─► generic branches
Deeplake: dataset-native ─► branches over tensors ─► streaming

Right layer, right abstractions.

Set it up

A few commands.

1. Install

bash
pip install deeplake

2. Open and branch

bash
ds = deeplake.load('deeplake://org/ds').branch('exp')

3. Snapshot

bash
ds.commit('relabel pass v2')

Where this usually breaks

  • DVC alone: Pointers only.
  • LakeFS alone: Generic; not ML-native.
  • Manual S3 prefixes: No diffs, no merges.
  • Hub commits: GBs only.

FAQ

DVC + Deeplake?

Some teams combine; usually Deeplake replaces.

LakeFS + Deeplake?

Possible; usually Deeplake alone is enough.

Migration from DVC?

One-time ingest from S3 paths.

Open source?

Yes.

Cost?

Object storage cost.

Multi-cloud?

S3, GCS, Azure.

Citations

ML-native dataset versioning

Deeplake versions tensors, not pointers. Branches, snapshots, merges. Open source.

Try Deeplake

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