Reproducibility by Default

Most teams treat reproducibility as an afterthought — a fragile checklist of manual logs and naming conventions that break the moment someone forgets to follow them.

Valohai flips this model. Every execution becomes an immutable, reproducible unit by default. No conventions needed, no extra code required.

What Gets Tracked (Without You Lifting a Finger)

Every execution automatically captures:

• Git commit hash — the exact code that ran

• Docker image digest — the precise environment

• Input file hashes — data versions locked in

• Parameter values — all configuration frozen

• Execution command — the complete invocation

This isn't metadata you log. It's infrastructure-level tracking that happens whether you remember or not.

Why Infrastructure Beats Convention

Traditional approaches fail because they rely on human discipline:

• "Remember to log your hyperparameters"

• "Follow our naming convention for experiments"

• "Don't forget to track your data versions"

Valohai's approach succeeds because it removes human error from the equation. Your ML engineer having a bad Monday? Doesn't matter. The execution is still fully reproducible.

The Payoff: Reproduce Any Run, Anytime

Six months later, when your model starts drifting in production, you can:

1. Find the original execution in your history

2. Click "Create execution from this"

3. Get identical results — same code, same data, same environment

No detective work. No "wait, which dataset version was this?" No begging your colleague for their random seed.

💡 This reproducibility extends to your entire pipeline. If you chain executions together, every step maintains its audit trail.

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Your experiments are reproducible by default. You don't build it. We guarantee it.