metrik-core

The vocabulary every other Metrik package agrees on: the artifact envelope, canonical serialization, and content addressing.

This package defines vocabulary, not behaviour (plan.md §4). It has no ML dependencies — no torch, no transformers — which is enforced in CI by import-linter, not by convention. That constraint is what lets the artifact layer be installed and reasoned about without a multi-gigabyte ML stack.

Install it directly only if you are reading artifacts without running Metrik — pip install metrik-ai gets you this plus the CLI.

What is here

Module Purpose
metrik.core.artifacts The envelope every artifact carries
metrik.core.hashing Canonical JSON, Digest, content_hash, derivation_hash
metrik.core.store The content-addressed artifact and blob store
metrik.core.lineage The lineage DAG, and the walker that recovers an input closure
metrik.core.runs The run registry — including runs that failed
metrik.core.ir ModelGraph, the shared representation producers write
metrik.core.profile ProfileRecord and the capability gate
metrik.core.bench BenchResult and the sandbox attestation it requires
metrik.core.analysis ModelFingerprint, GraphSummary, MemoryEstimate
metrik.core.viz VizSpec — a declarative render, no renderer
metrik.core.plugins Entry-point discovery that never imports plugin code
metrik.core.errors The error taxonomy plugins raise from

The two hashes

The subtlest part of the design, and getting it wrong makes both caching and reproducibility silently unreliable. There are two questions, so there are two hashes:

Question Answered by
Is this the same result as that? content_hash — over the payload only
Have I already computed this, so can I skip the work? derivation_hash — over how it was asked for

A single hash cannot do both. Caching needs a key computable before running, so it must cover inputs and parameters rather than results. Dedup and citation need a key covering only the value, so two runs producing identical numbers are recognised as identical even if asked for differently.

Because both are recorded, the store can check a producer's determinism claim rather than believe it: same derivation_hash, different content_hash, and a declared class of deterministic means the declaration is false.

Specification

The full contract is docs/planning/artifacts.md. This package implements §2 (envelope), §3 (hashing and canonicalization), and §4 (blobs).

Canonicalization rules are normative and tested as properties, not examples — see tests/test_canonical_properties.py.

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Newer release available (0.3.1)

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Credits

Author: The Metrik Authors

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License expression

Apache-2.0
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Requires

Python >=3.11

Tags

machine-learning provenance quantization reproducibility

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