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To reproduce an AI workflow a month later, preserve more than its code: record the exact code revision, inputs and transformations, software environment, run settings, execution steps, outputs, and known sources of variation. Capture those details as you work, then use the record to rerun the workflow and compare results. The goal is a traceable, understandable rerun—not a promise of bit-for-bit identical output.
What to record for a reproducible AI workflow
Keep a run record alongside the code and connect every result to the inputs and settings that produced it. A README section, run record, or structured metadata system can work; the important thing is that a future reader can answer what went in, what ran, and what came out.
| Record item | What to capture |
|---|---|
| Purpose and owner | What the workflow is intended to produce, who ran it, and the date or run ID. |
| Code | Repository and commit or release identifier, plus meaningful changes since the previous run. |
| Inputs | Data source and exact version or release date, storage reference, access or license limits, and checksums where useful. Identify original data separately from derived data. |
| Transformations | The ordered preprocessing, quality-control, feature-generation, and filtering steps, including material manual decisions. |
| Environment | Operating system or container reference, package and framework versions, hardware requirements, and whether GPU support is needed. |
| Configuration | Model and analysis settings, parameters, preprocessing choices, seeds, data splits, and resource requirements. |
| Execution | Exact command or job script, expected working directory, relevant environment variables without secrets, applicable run or cluster ID, and required external services or access. |
| Outputs | Expected files or artifacts, storage locations, logs, evaluation results, and how each output relates to its inputs. |
| Known limits | Nondeterministic steps, unavailable data or services, known failure modes, and expected variation. |
| Rerun check | How to start in a clean environment and what to compare after execution. |
This record combines practical recordkeeping guidance from the University of Tübingen ML Cloud User Guide with Google’s guidance on experiment artifacts and versioned assets.
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1. Pin the code and explain changes
Record the repository and exact commit or release used for the run. Keep code, configuration, environment specifications, and documentation under version control where practical. Use meaningful commit messages and tags so a later reader can identify what changed and why. A branch name alone is not a stable identifier because its contents can change.
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2. Identify data and preserve its lineage
Write down where each input came from and which version or release was used. Explain how original data became derived data, what transformations occurred, where the files are stored, and what access or license conditions apply. Keep original data separate from derived data so a rerun does not mistake a processed file for the source. Checksums can help verify that a file has not changed when the data is available to the future reader.
3. Preserve the environment
List dependency and framework versions, the operating system or container definition, and any relevant hardware requirements, including GPU support. If you use a container, preserve its definition or image reference and version or date. A package list without versions may not be enough to reconstruct the environment.
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4. Keep run settings explicit
Where practical, store settings outside the main analysis code in a configuration file. Include model settings, preprocessing options, learning rate, batch size, seeds, data splits, resource needs, and output paths when they apply. This makes it easier to see which choices belong to a particular run rather than relying on remembered defaults.
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5. Save how the run was launched
Preserve the exact command or job script, expected working directory, relevant environment variables, and run or cluster identifier when applicable. Record how required external services or access are obtained, but never put passwords, access tokens, or other secrets in version control. Use the team’s approved secret-management process to provide access.
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6. Keep logs and outputs tied to the run
Store logs, evaluation results, and output locations with the corresponding code revision and configuration. Give each run a distinct identifier and avoid overwriting previous outputs; otherwise, it may be impossible to tell which result belongs to which settings. Automate repeatable capture and execution steps where practical.
Choose documentation that fits the workflow’s scale
For an individual or small project
A version-controlled repository with a clear README, configuration file, environment specification, run scripts, logs, and versioned data references may be sufficient. Keep the run record close to the code and use stable references for inputs and outputs.
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For many runs or a multi-step pipeline
Record each execution as a run with its parameters and input and output artifacts. Google Cloud ML Metadata describes tracking lineage across training, test, and evaluation data, hyperparameters, training code, model artifacts, and downstream predictions. The Google ML Metadata project models executions and artifacts with recorded relationships, allowing readers to trace how an artifact was created.
When choosing a documentation or metadata approach, check whether it captures the code, data, environment, settings, commands, outputs, and lineage; how much is recorded automatically; whether results trace back to a specific run; and whether future readers will retain access to the data, artifacts, and logs. A searchable metadata system can help when the number of runs makes a document difficult to navigate, but the sources do not establish that every project needs one.
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What a rerun can—and cannot—promise
A complete record makes a workflow understandable and rerunnable, but it does not guarantee identical results. Google identifies data shuffling, variable initialization, asynchronous parallelism, and small evaluation sets as potential sources of noisy results. Its guidance on interpreting metrics recommends repeating runs to check whether an observed difference is stable.
Google’s deployment-testing guidance recommends deterministic random seeding, a fixed initialization order, averaging several runs, and version control, while cautioning that other sources of nondeterminism may remain. The University of Tübingen guide puts the limitation plainly: “A fixed seed improves repeatability but may not guarantee identical results across different hardware or software versions.”
Record the seed and data split, state whether deterministic execution was enabled, and note any known nondeterministic steps. If results vary, repeat the experiment where appropriate and report the observed variability rather than treating one run as universal.
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- Start from the documented code revision and create the specified environment.
- Confirm that the referenced inputs are available, match the recorded versions, and can legally be accessed.
- Follow the saved command or job script from the stated working directory, supplying required access through the approved secret-management process.
- Write outputs to a new run-specific location and retain the logs.
- Compare the new outputs with the expected artifacts and evaluation results, taking documented variation into account.
- If the procedure fails, record the missing dependency, changed input, unavailable service, or undocumented manual step so the record can be corrected.
A failed clean rerun is useful evidence that the record is incomplete or that a dependency has changed; it is not a reason to silently replace the original run’s details.
Quick Recap
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