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Training Cache ​

Connect the PyTorch data loader to LanceDB Enterprise and training reads automatically benefit from the plan executor's data page cache.

Applies to: Embedded ❌, Enterprise ✅

The PyTorch data loader reads training data the same way any other query does -- there is no training-specific read path. Point StreamingDataset or a DataLoader at a table opened through an Enterprise connection instead of a local one, and every read it makes flows through the same plan executors that serve every other query, with the same data page cache in front of object storage. No training-specific configuration turns this on; it's a consequence of connecting to Enterprise at all.

mermaid
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flowchart LR
    DL["PyTorch DataLoader<br/>/ StreamingDataset"] --> T["Enterprise-connected table"]
    T --> PE["Plan executor"]
    PE --> C{"In data page cache?"}
    C -->|hit| Fast["Served from memory<br/>or local disk"]
    C -->|miss| O[("Object storage")]
    O --> Fill["Fills the cache,<br/>served"]
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    class Fast accent

This covers the ordinary tabular columns a query reads -- the data page cache's unit is a fixed-size page of table data. A Blob v2 column's payload is fetched through a separate range-request path keyed to its own descriptor, not through this cache, so a training table mixing plain feature columns with Blob v2 images or video gets this benefit only for the former. A custom collate function or transform that does its own file or network I/O -- decoding an image, say -- is also outside this cache; only reads that go through the table query path benefit.

Why this matters for training specifically ​

Training reads the same rows repeatedly: once per epoch, often for many epochs. The first epoch's reads behave like any cold cache -- a miss per page, each one filling the cache on the way. Every epoch after that reads the same pages again, so a dataset that fits the cache's budget serves almost entirely from memory or local disk after the first pass, instead of re-reading object storage every epoch. A local-file or Embedded connection has no equivalent remote cache to warm this way -- its locality comes from whatever the local filesystem or OS page cache already does.

Cache Prewarm extends this further: a plan executor can warm its cache at startup, before training even begins, instead of paying the first-epoch cold-cache cost during the run itself.

PyTorch Data LoaderConnecting a DataLoader to an Enterprise table.Data Page CacheHow the cache a training read benefits from actually works.Cache PrewarmWarming the cache before the first epoch instead of during it.