[Docs] Add minimal demo of Ray Data API usage (#21080)

Signed-off-by: Ricardo Decal <rdecal@anyscale.com>
This commit is contained in:
Ricardo Decal
2025-07-17 20:09:19 -07:00
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parent 8dfb45ca33
commit c4e3b12524

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@ -30,8 +30,31 @@ This API adds several batteries-included capabilities that simplify large-scale,
- Automatic sharding, load balancing, and autoscaling distribute work across a Ray cluster with built-in fault tolerance.
- Continuous batching keeps vLLM replicas saturated and maximizes GPU utilization.
- Transparent support for tensor and pipeline parallelism enables efficient multi-GPU inference.
- Reading and writing to most popular file formats and cloud object storage.
- Scaling up the workload without code changes.
The following example shows how to run batched inference with Ray Data and vLLM:
<gh-file:examples/offline_inference/batch_llm_inference.py>
??? code
```python
import ray # Requires ray>=2.44.1
from ray.data.llm import vLLMEngineProcessorConfig, build_llm_processor
config = vLLMEngineProcessorConfig(model_source="unsloth/Llama-3.2-1B-Instruct")
processor = build_llm_processor(
config,
preprocess=lambda row: {
"messages": [
{"role": "system", "content": "You are a bot that completes unfinished haikus."},
{"role": "user", "content": row["item"]},
],
"sampling_params": {"temperature": 0.3, "max_tokens": 250},
},
postprocess=lambda row: {"answer": row["generated_text"]},
)
ds = ray.data.from_items(["An old silent pond..."])
ds = processor(ds)
ds.write_parquet("local:///tmp/data/")
```
For more information about the Ray Data LLM API, see the [Ray Data LLM documentation](https://docs.ray.io/en/latest/data/working-with-llms.html).