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https://issues.apache.org/jira/browse/SPARK-26412?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-26412:
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Assignee: Apache Spark (was: Weichen Xu)
> Allow Pandas UDF to take an iterator of pd.DataFrames
> -----------------------------------------------------
>
> Key: SPARK-26412
> URL: https://issues.apache.org/jira/browse/SPARK-26412
> Project: Spark
> Issue Type: New Feature
> Components: PySpark
> Affects Versions: 3.0.0
> Reporter: Xiangrui Meng
> Assignee: Apache Spark
> Priority: Major
>
> Pandas UDF is the ideal connection between PySpark and DL model inference
> workload. However, user needs to load the model file first to make
> predictions. It is common to see models of size ~100MB or bigger. If the
> Pandas UDF execution is limited to each batch, user needs to repeatedly load
> the same model for every batch in the same python worker process, which is
> inefficient.
> We can provide users the iterator of batches in pd.DataFrame and let user
> code handle it:
> {code}
> @pandas_udf(DoubleType(), PandasUDFType.SCALAR_ITERATOR)
> def predict(batch_iter):
> model = ... # load model
> for batch in batch_iter:
> yield model.predict(batch)
> {code}
> We might add a contract that each yield must match the corresponding batch
> size.
> Another benefit is with iterator interface and asyncio from Python, it is
> flexible for users to implement data pipelining.
> cc: [~icexelloss] [~bryanc] [~holdenk] [~hyukjin.kwon] [~ueshin] [~smilegator]
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