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This page covers the runtime configuration options for KumoRFM, including run modes, batch prediction, and retry handling.

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Run Modes

The run_mode parameter controls the trade-off between prediction quality and speed by adjusting how much context data is sampled.
Run ModeContext SizeNeighbor SamplingUse Case
DEBUG100[16, 16, 4, 4, 1, 1]Quick iteration, testing queries
FAST1,000[32, 32, 8, 8, 4, 4]Default. Good balance of speed and quality
NORMAL5,000[64, 64, 8, 8, 4, 4]Higher quality predictions
BEST10,000[64, 64, 8, 8, 4, 4]Maximum quality
# Use the fastest mode for quick testing
result = model.predict(query, run_mode="DEBUG")

# Use the highest quality mode for production
result = model.predict(query, run_mode="BEST")
You can also fine-tune the neighbor sampling directly: 
result = model.predict(
    query,
    num_neighbors=[64, 64, 8, 8, 4, 4],
    num_hops=3,
)

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Batch Mode

For predictions over many entities, use KumoRFM.batch_mode() to automatically split the workload into batches: 
with model.batch_mode(batch_size='max', num_retries=1):
    result = model.predict(
        "PREDICT COUNT(orders.*, 0, 30, days) > 0 FOR users.user_id=1",
        indices=list(range(1, 1001)),
    )
Parameters:
  • batch_size: The number of entities per batch. Set to "max" (default) to use the maximum applicable batch size for the task type.
  • num_retries: Number of retries for failed batches due to server issues.
The maximum prediction sizes per task type are:
Task TypeMax Prediction SizeMax Test Size
Classification / Regression / Forecasting1,0002,000

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Retry

Use KumoRFM.retry() to automatically retry failed queries due to transient server issues: 
with model.retry(num_retries=2):
    result = model.predict(query, indices=[1, 2, 3])
This is useful for long-running batch predictions where occasional failures are expected.

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Size Limits

KumoRFM enforces a 30 MB context size limit per prediction. If exceeded, you will see an error message suggesting:
  • Reducing the number of tables in the graph
  • Reducing the number of columns (e.g., large text columns)
  • Adjusting the neighborhood configuration
  • Using a lower run mode
The optimize parameter in KumoRFM can help with database backends by creating indices for faster sampling: 
model = rfm.KumoRFM(graph, optimize=True)