Reuse transformations under a versioned semantic key

A useful cache identity includes the dataset snapshot, source record identity, transform code or version, tokenizer or vocabulary artifact, relevant configuration, and output representation. Include normalization rules, truncation length, special-token policy, and dtype when they affect the result. A pathname or experiment label alone is not enough. If source bytes can change behind an identity, either make that identity immutable or bind the actual approved content. Record schema and version information so a reader can reject an incompatible cached entry rather than reinterpret yesterday's bytes under today's assumptions.

Validate writes and publication as well as keys. Concurrent workers must not expose an incomplete entry as a complete result, and a retry should not silently overwrite correct content with a partial result. Verify the stored representation and recover from corrupt entries through an explicit miss or error path. The storage mechanism may provide atomic publication for one object, but the application still defines what that object means. Shared caches need a clear boundary for who can write results consumed by other jobs. Performance reuse is justified by content and transformation identity, not merely by the existence of a file.

Caching deterministic decoding or normalization before random cropping can preserve new crops on later visits while avoiding repeated stable work. Caching after a random transform stores that particular realization, so later visits may reuse it instead of drawing a new augmentation. That can be intentional, but it must be declared. Another design keys each augmentation by a stable occurrence seed or index; it trades more entries for explicit reproducibility. Decide whether epoch, view index, and random state belong in the result identity rather than assume one source sample always has one correct transformed output.

TensorFlow 2.16.1 Dataset.cache documents persistence when a filename is reused: changing upstream operations does not automatically refresh an existing file cache. It also requires completing the input iteration to finalize the cache and returns the cached element sequence on subsequent iterations. Shuffle after a stable cache if new order is desired; caching a shuffled or augmented stream can preserve more than intended. Treat these as version-scoped operator semantics, not a guarantee that every framework or custom cache behaves identically. Explicit cache naming, invalidation, and completion checks belong in the experiment workflow.

Tokenized and decoded forms can be much larger than compressed sources, so estimate retained bytes and eviction behavior before caching everything. Compare fill cost, subsequent reuse, serialization overhead, and whether the saved work was actually limiting training. A cache with frequent version churn may have little useful reuse even if its raw capacity is large. Separate cold-start measurements from warm-hit measurements and retain the exact keys and source identities used. A high reported hit rate cannot show that randomization remained correct or that a new tokenizer was applied.

The cache lab demonstrates capacity relative to an explicit working set. For each separate run, Reset, set both capacity C and stride s=4, predict, then inspect the initial sixteen requests. Capacity four retains the four-address cycle after four misses, producing twelve hits; capacity two produces no hits. This deterministic LRU model has no tokenizer, source version, random augmentation, or integrity check. It shows why enough capacity can enable reuse, while its missing semantic key fields expose what a real preprocessing cache must additionally verify before returning a result.