WeichenXu123 commented on code in PR #40607:
URL: https://github.com/apache/spark/pull/40607#discussion_r1153066202
##########
python/pyspark/ml/torch/distributor.py:
##########
@@ -581,11 +593,11 @@ def _run_distributed_training(
f"Started distributed training with {self.num_processes} executor
proceses"
)
try:
+ assert self.spark is not None
result = (
- self.sc.parallelize(range(self.num_tasks), self.num_tasks)
- .barrier()
- .mapPartitions(spark_task_function)
- .collect()[0]
+ self.spark.range(start=0, end=self.num_tasks, step=1,
numPartitions=self.num_tasks)
+ .mapInPandas(func=spark_task_function, schema="output binary",
barrier=True)
+ .first()["output"]
Review Comment:
I think barrier mode mapping result dataframe does not support `.first()`
operation if I remember it correctly
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