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Jack YeandClaude Sonnet 5
de95ed3f6d
A broad documentation pass across the areas LanceDB Enterprise and Embedded diverge on most: **Data Curation** -- GPU Function execution, Slurm scheduling (and what Slurm is, for readers new to it), blob late materialization as its own page, joins, aggregation/profiling, sampling, near-duplicate detection, chunking, distributed shuffle, and IVF-partition clustering. **Feature Engineering** -- computed columns and materialized views, restructured to lead with the Function-based path and show the SQL form side by side with Python throughout; a single Auto Refresh page instead of duplicating scheduling details on every refresh page; adaptive execution; checkpointing; GPU and Slurm placement for refresh workers; a Training Cache page for the PyTorch data loader's automatic cache benefit. **Security & Governance** -- an Audit Log reference (schema, access, grants), Secrets promoted to an ordinary catalog object with their own SQL DDL and a vending/rotation guide, and privilege-table fixes verified against the authorization source. **Deployment & Maintenance** -- Horizontal Scaling and OpenTelemetry moved into a real Deployment area; Automated Cleanup folded into Intelligent Auto-Maintenance (every other maintenance decision already lived there); RTE Compaction and Advanced Conflict Resolution documented in depth. **Landing page** -- the Enterprise feature comparison table reorganized (several redundant or stale rows removed, several new areas added), and the "why Enterprise" bullets replaced with ones tied to a concrete page (Performance, Scale, Fault tolerance, Multicloud deployment, Simplify operations) instead of vague promises. Plus a full pass addressing lancedb-gatekeeper's review: a real test-safety bug (a quickstart test could silently overwrite and delete a deployment's own `frames` table), a fabricated SQL statement with no backing implementation, an actual dedup-guarantee bug in `take_row_ids`, and dozens of documentation-accuracy fixes verified against source rather than taken on faith -- each either fixed, or left with an explicit note on why it needs a feature or deployment owner's sign-off instead of being guessed at. 🤖 Generated with [Claude Code](https://claude.com/claude-code) https://claude.ai/code/session_018PXMCbRH6i5w47TsjeEUMT --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
58 lines
2.6 KiB
Plaintext
58 lines
2.6 KiB
Plaintext
---
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title: "GPU Refresh Execution"
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sidebarTitle: "GPU Refresh Execution"
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description: "A computed column or materialized view filled by a GPU Function schedules its refresh workers the same way GPU Function execution does in Data Curation."
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icon: microchip
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keywords: ["gpu", "gpu class", "scheduling", "refresh", "computed column", "function"]
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availability:
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embedded: unavailable
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enterprise: available
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---
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A [computed column](/feature-engineering/add-computed-column) or
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[materialized view](/feature-engineering/materialized-views) filled by a
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registered [Function](/curation/create-function) with `gpu=True` refreshes
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through the exact same placement path as
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[GPU Function execution](/curation/gpu-function-execution) in Data
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Curation -- refreshing a column is not a separate scheduling code path
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from calling the Function directly, just a different caller.
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```mermaid
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flowchart LR
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F["Function registered with<br/>gpu=True, gpu_class"] --> A{"Who requests<br/>a worker?"}
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A -->|"You, directly"| D["Data Curation:<br/>GPU Function execution"]
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A -->|"A refresh job"| R["Feature Engineering:<br/>computed column / MV refresh"]
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D --> P["Same GPU-class placement,<br/>same capacity-wait behavior"]
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R --> P
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```
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The Function's `gpu_class` (or the deployment's `default`) resolves to the
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same node selector, tolerations and resource name either way, and a
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refresh that needs more GPU capacity than is currently free waits the
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same way a direct call would -- see
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[Waiting for capacity](/curation/gpu-function-execution#capacity) for that
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behavior in full. [Adaptive Execution](/enterprise/adaptive-execution)'s
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resource-consumption-based sizing applies here too: a refresh's GPU
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utilization is measured and fed back into sizing the *next* part the same
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way CPU and memory are.
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A single refresh can also override the Function's registered placement for
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that run -- see
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[Configure workers and resources](/feature-engineering/refresh-column#resources)
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for the `gpu_class`/`gpu_count` options on `refresh_column_async` and the
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equivalent SQL `WITH (...)` clause.
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## What to read next {#next}
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<CardGroup cols={2}>
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<Card title="GPU Function Execution" icon="microchip" href="/curation/gpu-function-execution">
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The full placement and capacity-wait mechanism this page reuses.
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</Card>
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<Card title="Adaptive Execution" icon="gauge-high" href="/enterprise/adaptive-execution">
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How a refresh's resource class, part size and worker count adjust themselves.
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</Card>
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<Card title="Configure workers and resources" icon="sliders" href="/feature-engineering/refresh-column#resources">
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Override GPU class and count for one refresh call.
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</Card>
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</CardGroup>
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