I start with a simple code to generate dask array and then save it to hdf5. Load('parquet_test', 'parquet_testII', False) Merged.to_parquet('merged', engine="fastparquet")Ĭreate_parquet('parquet_test', False, None)Ĭreate_parquet('parquet_testII', False, None) Partition_col = np.random.randint(101, size=n)ĭdf.to_parquet(args, engine="fastparquet") Am I doing something fundamnetally wrong here? (I can run the test Job very easily with a LocalCluster with 4 workers). My theory is that the SpecCluster is simply not scaling up workers quickly enough to handle the demand and then just crashes out. So far this is not yielding promosing results, I am getting lots of : Stream is closed and : in I am currently trying a proof of concept on my mac using the SpecCluster running in adapt mode.I have identified 3 issues with this that I would like some help/clarification with. The target architecture basically then looks like this We are hoping to make use of adaptive clusters, probably a KubeCluster in production, such that when no users are using the app the cluster will scale down to zero workers, and will then scale up adaptively as more and more Jobs get kicked off. I am working on upgrading our DASK infrastructure where currently we create a LocalCluster for each Job (a Job is 1 of ~10 computationally intensive tasks kicked off when one of your users clicks a button in our web app).#How to delete the upload from issuu mac#
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