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Added Dask kwargs to Ray backend #1380

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Oct 13, 2021
Merged

Added Dask kwargs to Ray backend #1380

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7 changes: 5 additions & 2 deletions ludwig/backend/ray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -249,9 +249,10 @@ def __call__(self, df: pd.DataFrame) -> pd.DataFrame:


class RayBackend(RemoteTrainingMixin, Backend):
def __init__(self, horovod_kwargs=None, cache_format=PARQUET, engine=None, **kwargs):
def __init__(self, dask_kwargs=None, horovod_kwargs=None, cache_format=PARQUET, engine=None, **kwargs):
super().__init__(cache_format=cache_format, **kwargs)
self._df_engine = _get_df_engine(engine)
self._dask_kwargs = dask_kwargs or {}
self._horovod_kwargs = horovod_kwargs or {}
self._tensorflow_kwargs = {}
if cache_format not in [PARQUET, TFRECORD]:
Expand All @@ -268,8 +269,10 @@ def initialize(self):
logger.info('Initializing new Ray cluster...')
ray.init(ignore_reinit_error=True)

dask_kwargs = {**get_dask_kwargs(), **self._dask_kwargs}
logger.info(f"Dask params: {dask_kwargs}")
dask.config.set(scheduler=ray_dask_get)
self._df_engine.set_parallelism(**get_dask_kwargs())
self._df_engine.set_parallelism(**dask_kwargs)

def initialize_tensorflow(self, **kwargs):
# Make sure we don't claim any GPU resources on the head node
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