Mental Model

Mental model for a Quilt package

Quilt represents datasets as packages. A package is an immutable collection of related files with a handle of the form AUTHOR/DESCRIPTION, a cryptographic top-hash (or hash of hashes) that uniquely identifies package contents, and a backing manifest.

The manifest is serialized as file that contains entries. Manifest entries are tuples of the following form:

(LOGICAL_KEY, PHYSICAL_KEYS, HASH, METADATA)

Logical keys are user-facing friendly names, like "README.md". Physical keys are fully qualified paths to bytes on disk, or bytes in S3. A hash is a digest of the physical key's contents, usually SHA-256. Metadata are a dictionary that may contain user-defined keys for metadata like bounding boxes, labels, or provenance information (e.g. {"algorithm_version": "4.4.1"} to indicate how a given file was created).

Package manifests are stored in registries. Quilt supports both local disk and Amazon S3 buckets as registry. A registry may store manifests as well as the primary data. S3 was chosen for its widespread adoption, first-class versioning support, and cost/performance profile. The Quilt roadmap includes plans to support more storage formats in the future (e.g. GCP, Azure, NAS, etc.).

By way of illustration first entry of a package manifest for the COCO machine learning dataset are shown below.

{
    "logical_key": "annotations/captions_train2017.json",
    "physical_keys":
    ["s3://quilt-ml-data/data/raw/annotations/captions_train2017.json?versionId=UtzkAN8FP4irtroeN9bfYP1yKzX7ko3G"],
    "size": 91865115,
    "hash": {
    "type": "SHA256",
    "value":
    "4b62086319480e0739ef390d04084515defb9c213ff13605a036061e33314317"},
    "meta": {}
}

Buckets are branches

In Quilt, S3 buckets are analogous to branches in git. Each bucket is a self-contained registry for one or more packages. As package data and schemas are refined, you can promote a package to a new bucket to signify its increased data quality.

We generally recommend a minimum of three buckets for the data lifecycle:

• Raw

• Stage

• Production

See Quilt workflows for more on how you can control data quality with schemas.