> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# How to do batch prediction

There are three ways to do inference with KumoRFM.

| **Method**       | **Scale**           | **Best Use Case**            |
| :--------------- | :------------------ | :--------------------------- |
| Single entity    | 1                   | Testing / debugging / Agents |
| Multi-entity     | ≤ 1,000 per request | Small batches                |
| Batch prediction | Unlimited           | Full scoring pipelines       |

Let’s take a look at how this works by using an e-commerce dataset. First, import the data and setup the graph.

```python import data & setup graph theme={null}
import kumoai.rfm as rfm
import pandas as pd

root = "s3://kumo-sdk-public/rfm-datasets/online-shopping"

df_users = pd.read_parquet(f"{root}/users.parquet")
df_items = pd.read_parquet(f"{root}/items.parquet")
df_orders = pd.read_parquet(f"{root}/orders.parquet")

graph = rfm.Graph.from_data({
    'users': df_users,
    'items': df_items,
    'orders': df_orders,
})

model = rfm.KumoRFM(graph)
```

**Predict how many orders a user will place in the next 30 days.**

Predictive Query expression:

```python theme={null}
PREDICT COUNT(orders.*, 0, 30, days)
```

## Single-Entity Inference

Run inference for a single entity:

```python single-entity theme={null}
query = "PREDICT COUNT(orders.*, 0, 30, days) FOR users.user_id = 0"
result = model.predict(query)
```

## Multi-Entity Inference (≤ 1,000 Entities)

Score multiple specific entities in a single call:

```python multi-entity theme={null}
query = (
    "PREDICT COUNT(orders.*, 0, 30, days) "
    "FOR users.user_id IN (0, 1, 2, 3)"
)

result = model.predict(query)
```

## Batch Prediction (Full Dataset or Large Batches)

Batch prediction allows scoring all entities. The SDK automatically handles batching and retries.

```python batch-prediction theme={null}
# collect all user IDs
indices = df_users["user_id"].tolist()

with model.batch_mode(batch_size="max", num_retries=1):
    result = model.predict(
        "PREDICT COUNT(orders.*, 0, 30, days) FOR EACH users.user_id",
        indices=indices
    )
```

**Parameters**

* batch\_size="max": automatically uses the largest valid batch size
* num\_retries: retry count for transient failures