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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>
77 lines
3.2 KiB
Plaintext
77 lines
3.2 KiB
Plaintext
---
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title: "GPU Function Execution"
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sidebarTitle: "GPU Function Execution"
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description: "How a GPU Function's worker gets scheduled onto a GPU-capable node, and what happens when none is free."
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icon: microchip
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keywords: ["gpu", "gpu class", "scheduling", "function", "udf", "kubernetes", "node selector"]
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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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[Registering a GPU Function](/curation/create-function#gpu) sets `gpu=True`
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and an optional `gpu_class`; this page covers what happens after that, when
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a job actually needs a worker. An operator configures one or more named
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**GPU classes** for the deployment -- each one a Kubernetes node selector,
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toleration set, and resource name (such as `nvidia.com/gpu`) pointing at a
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specific pool of GPU nodes, plus a hardware description (GPU model, GPU
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memory, how many GPUs one worker may request). A Function with no
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`gpu_class` uses the deployment's `default` class.
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## Placement {#placement}
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When a job needs to run a GPU Function, the agent resolves its `gpu_class`
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(or `default`) to that class's node selector, tolerations, and resource
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name, and requests a worker pod shaped accordingly -- this is a separate
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placement path from the ordinary CPU worker ladder used by non-GPU
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Functions and other background jobs (see
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the [Job Framework](/basics/jobs) for that general job
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machinery). The resulting pod can only schedule onto a node matching the
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class's selector and tolerations, so a class is really naming a pool of
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nodes, not just a resource count.
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## Waiting for capacity {#capacity}
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A GPU class describes the deployment's hardware, not live availability --
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listing classes never tells you whether a GPU is free right now. If no
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node in the class has room, the worker pod stays pending rather than
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failing the job outright or falling back to a CPU worker: the job reports
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that it is waiting on that GPU class's capacity, and starts running as soon
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as a matching node has room. A job stuck waiting indefinitely usually means
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the class's node pool needs more capacity, not that anything is
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misconfigured.
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{/*
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Draft for the list_gpu_classes release. Keep this section unpublished
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until that API ships -- v0.41.0 (the latest release) does not have it,
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and there is no SQL equivalent; discovery is a REST call
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(`GET /v1/gpu/classes`), not a SQL statement.
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## Inspecting GPU classes {#inspecting}
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<CodeGroup>
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```python Python icon="python"
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for gpu in db.list_gpu_classes():
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print(gpu.name, gpu.gpu_model, gpu.gpu_memory_bytes, gpu.max_gpu_count_per_worker)
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```
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</CodeGroup>
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Returns the operator-configured classes: name, resource name, GPU model
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and memory, and the largest per-worker GPU count the class permits.
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Listing requires the System Usage privilege.
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*/}
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## What to read next {#next}
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<CardGroup cols={2}>
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<Card title="Create a Function" icon="code" href="/curation/create-function#gpu">
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Register a GPU Function and pin it to a specific class.
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</Card>
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<Card title="Job Framework" icon="list-check" href="/basics/jobs">
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The job types a deployment runs on its own schedule.
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</Card>
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<Card title="GPU Refresh Execution" icon="microchip" href="/enterprise/gpu-refresh-execution">
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The same placement path, for a computed column or MV refresh instead of a direct call.
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</Card>
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</CardGroup>
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