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https://issues.apache.org/jira/browse/SPARK-26412?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Xiangrui Meng updated SPARK-26412:
----------------------------------
Description:
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_ITER)
def predict(batch_iter):
model = ... # load model
for batch in batch_iter:
yield model.predict(batch)
{code}
The type of each batch is:
* a pd.Series if UDF is called with a single non-struct-type column
* a tuple of pd.Series if predict is called with more than one Spark DF columns
* a pd.DataFrame if predict is called with a single StructType column
{code}
@pandas_udf(...)
def evaluate(batch_iter):
model = ... # load model
for features, label in batch_iter:
pred = model.predict(features)
yield (pred - label).abs()
df.select(evaluate(col("features"), col("label")).alias("err"))
{code}
{code}
@pandas_udf(...)
def evaluate(pdf_iter):
model = ... # load model
for pdf in pdf_iter:
pred = model.predict(pdf['x'])
yield (pred - pdf['y']).abs()
df.select(evaluate(struct(col("features"), col("label"))).alias("err"))
{code}
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]
was:
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_ITER)
def predict(batch_iter):
model = ... # load model
for batch in batch_iter:
yield model.predict(batch)
{code}
The type of each batch is:
* pd.Series if UDF is called with a single non-struct-type column
* a tuple of pd.Series if predict is called with more than one Spark DF columns
* a pd.DataFrame if predict is called with a single StructType column
{code}
@pandas_udf(...)
def evaluate(batch_iter):
model = ... # load model
for features, label in batch_iter:
pred = model.predict(features)
yield (pred - label).abs()
df.select(evaluate(col("features"), col("label")).alias("err"))
{code}
{code}
@pandas_udf(...)
def evaluate(pdf_iter):
model = ... # load model
for pdf in pdf_iter:
pred = model.predict(pdf['x'])
yield (pred - pdf['y']).abs()
df.select(evaluate(struct(col("features"), col("label"))).alias("err"))
{code}
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]
> 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: Weichen Xu
> 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_ITER)
> def predict(batch_iter):
> model = ... # load model
> for batch in batch_iter:
> yield model.predict(batch)
> {code}
> The type of each batch is:
> * a pd.Series if UDF is called with a single non-struct-type column
> * a tuple of pd.Series if predict is called with more than one Spark DF
> columns
> * a pd.DataFrame if predict is called with a single StructType column
> {code}
> @pandas_udf(...)
> def evaluate(batch_iter):
> model = ... # load model
> for features, label in batch_iter:
> pred = model.predict(features)
> yield (pred - label).abs()
> df.select(evaluate(col("features"), col("label")).alias("err"))
> {code}
> {code}
> @pandas_udf(...)
> def evaluate(pdf_iter):
> model = ... # load model
> for pdf in pdf_iter:
> pred = model.predict(pdf['x'])
> yield (pred - pdf['y']).abs()
> df.select(evaluate(struct(col("features"), col("label"))).alias("err"))
> {code}
> 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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