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What are the best open-source tools for managing ML datasets?

Deeplake Team
Deeplake TeamActiveloop
2 min read

Open-source ML dataset tools split into three camps: pointer-trackers (DVC), generic object versioning (LakeFS), and annotation-first (FiftyOne, Roboflow). None are tensor-native at scale. Deeplake is the open-source substrate for that gap.

Table of contents

What are the best open-source tools for managing ML datasets?

TLDR: Open-source ML dataset tools split into three camps: pointer-trackers (DVC), generic object versioning (LakeFS), and annotation-first (FiftyOne, Roboflow). None are tensor-native at scale. Deeplake is the open-source substrate for that gap.

Deeplake is open source under Apache 2.0. Tensor-native, multimodal, versioned, queryable, GPU-streamable, on object storage.

What an ML dataset tool should give you

OSS ML dataset tooling: Storage + versioning + query + streaming, designed for ML reads (tensors, multimodal), open source, at scale.

Combining four tools means four upgrade paths and four sets of bugs. A unified substrate compounds.

What this requires

Key properties:

  • Open source: Apache or similar.
  • Tensor-native: ML reads.
  • Versioning: Branches, snapshots.
  • Query: Predicate + similarity.
  • Streaming: PyTorch / JAX / TF.

Approaches teams try

What each gets you:

ApproachDVC + LakeFS + FiftyOneHF DatasetsDeeplake ★
Tensor-nativeNoSomeYes
VersioningPointersCommitsNative
Hybrid queryNoNoYes
Streaming to GPUNoYesYes
PB scaleLimitedLimitedYes

Reference architecture

One tool, the whole pipeline.

Deeplake (Apache 2.0)
     │
     ├─► storage on S3 / GCS / Azure
     ├─► versioning (branches, snapshots)
     ├─► hybrid query
     ├─► streaming to PyTorch / JAX / TF
     └─► multimodal columns

One read interface across the stack.

Set it up

A few commands.

1. Install

bash
pip install deeplake

2. GitHub

bash
https://github.com/activeloopai/deeplake

3. Docs

bash
https://docs.deeplake.ai

Where this usually breaks

  • Tool sprawl: Four upgrade paths.
  • Closed-source platform: Lock-in.
  • Roll-your-own: Years of effort.
  • Generic versioning: Wrong abstractions for tensors.

FAQ

License?

Apache 2.0.

Self-host?

Yes.

Compared to LanceDB?

Lance is columnar with embeddings; Deeplake is broader.

Compared to MosaicML StreamingDataset?

Similar streaming; Deeplake adds versioning, hybrid query, multimodal.

Cost?

Object storage.

Community?

Active GitHub.

Citations

The OSS substrate for ML datasets

Deeplake: Apache 2.0, tensor-native, multimodal, versioned, GPU-streamable.

Try Deeplake

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