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name: batched-performance description: "Guides TabPFN batched multi-dataset inference, cache placement, and performance tradeoffs." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

TabPFN batched performance

Use this sub-skill when the user wants to score many datasets in one call or needs to understand how TabPFN trades memory for speed.

Start here

• Read references/batched-workflows.md for predict_proba_batched and predict_batched semantics.
• Read references/memory-and-performance.md for fit_mode, cache behavior, and chunking guidance.
• Read references/troubleshooting.md for ragged batches, float64, and unsupported option failures.
• Run scripts/batched_prediction_smoke.py --help when you have a local checkpoint and want a source-free batch smoke check.

Use this sub-skill when

• The task mentions cross-validation folds, repeated train/test splits, or a list of datasets.
• The user asks how to reduce repeated preprocessing or repeated inference cost.
• The user wants to understand fit_mode, fit_with_cache, keep_cache_on_device, or kv_cache_precision.
• The user is comparing memory use vs latency for one checkpoint on many datasets.

Route elsewhere

• One dataset, logits, quantiles, embeddings, or estimator basics: ../tabular-prediction/SKILL.md.
• Input cleaning, categorical detection, NaNs, infinities, or config fields: ../preprocessing-config/SKILL.md.
• eval_metric, tuning, differentiable input, or fine-tuning: ../tuning-and-advanced/SKILL.md.
• Model download, auth, persistence, or checkpoint conversion: ../model-management/SKILL.md.

What this route owns

• predict_proba_batched for classifiers.
• predict_batched for regressors.
• The shape and class-set constraints that come from fused batched scoring.
• Memory and cache tradeoffs for repeated fits and repeated prediction calls.

What to remember

• Batched inference is only worth it when multiple datasets share the same shape family.
• Ragged batches are rejected instead of padded.
• Some classifier post-processing options are not supported in batched mode because their fitted state is dataset-specific.
• float64 is rejected by the fused batched path even though ordinary per-dataset prediction may still work.