dask: difference between client.persist and client.compute
Jan 23, 2017 · So if you persist a dask dataframe with 100 partitions you get back a dask dataframe with 100 partitions, with each partition pointing to a future currently running on the …
python - Why does Dask perform so slower while multiprocessing …
Sep 6, 2019 · 36 dask delayed 10.288054704666138s my cpu has 6 physical cores Question Why does Dask perform so slower while multiprocessing perform so much faster? Am I using …
Dask does not use all workers and behaves differently with …
Apr 21, 2023 · Workers: 15 Threads: 15 Memory: 22.02 GiB Dask Version: 2023.2.0 Dask.Distributed Version: 2023.2.0 10 nodes If I use 10 nodes the calculations interrupted …
python - Difference between dask.distributed LocalCluster with …
Sep 2, 2019 · What is the difference between the following LocalCluster configurations for dask.distributed? Client(n_workers=4, processes=False, threads_per_worker=1) versus …
python - dask: What does memory_limit control? - Stack Overflow
Oct 4, 2021 · The link you posted says explicitly that it's a per worker limit $ dask-worker tcp://scheduler:port --memory-limit="4 GiB" # four gigabytes per worker process. And you get …
Converting an DataFrame from pandas to dask - Stack Overflow
Oct 22, 2020 · I followed this documentation dask.dataframe.from_pandas and there are optional arguments called npartitions and chunksize. So I try write something like this: import …
python - Why does dask take long time to compute regardless of …
Mar 24, 2022 · The reason dask dataframe is taking more time to compute (shape or any operation) is because when a compute op is called, dask tries to perform operations from the …
python - Using Matplotlib with Dask - Stack Overflow
Jul 15, 2022 · One motivation to use dask instead of pandas is the size of the data. As such, swapping pandas DataFrame with dask DataFrame might not be feasible. Imagine a scatter …
numpy - How to handle large xarray/dask datasets to minimize ...
Aug 3, 2024 · How to handle large xarray/dask datasets to minimize computation time or running out of memory to store as yearly files (ERA5 dataset)
Strategy for partitioning dask dataframes efficiently
Jun 20, 2017 · The documentation for Dask talks about repartioning to reduce overhead here. They however seem to indicate you need some knowledge of what your dataframe will look …