Managing Memory — Dask.distributed 2025.2.0 documentation
Managing Memory¶ Dask.distributed stores the results of tasks in the distributed memory of the worker nodes. The central scheduler tracks all data on the cluster and determines when data should be freed.
dask.dataframe.DataFrame.memory_usage — Dask …
DataFrame. memory_usage (deep = False, index = True) [source] ¶ Return the memory usage of each column in bytes. This docstring was copied from pandas.core.frame.DataFrame.memory_usage.
Worker Memory Management — Dask.distributed 2025.2.0 …
Worker Memory Management¶ For cluster-wide memory-management, see Managing Memory. Workers are given a target memory limit to stay under with the command line --memory-limit keyword or the memory_limit= Python keyword argument, which sets the memory limit per worker processes launched by dask worker
Managing worker memory on a dask localcluster - Stack Overflow
Dec 26, 2018 · I am trying to load a dataset with dask but when it is time to compute my dataset I keep getting problems like this: WARNING - Worker exceeded 95% memory budget. Restarting. I am just working o...
Dask — Dask documentation
Low-memory processing data in a streaming way that minimizes memory use. Good for preprocessing especially for text or JSON data prior ingestion into dataframes. Dask bags are similar in this regard to Spark RDDs or vanilla Python data structures and iterators.
Active Memory Manager — Dask.distributed 2025.2.0 …
Active Memory Manager¶ The Active Memory Manager, or AMM, is an experimental daemon that optimizes memory usage of workers across the Dask cluster. It is enabled by default but can be disabled/configured. See Enabling the Active Memory Manager for details.
Measuring Dask memory usage with dask-memusage
Mar 11, 2021 · How a tool called dask-memusage can help you find peak memory usage of the tasks in your Dask execution graph. How to further pinpoint high memory usage using the Fil memory profiler , so you can reduce memory usage.
Tackling unmanaged memory with Dask | by Laurie Thompson
Jul 1, 2021 · Identify the most common causes of unmanaged memory; Use the Dask dashboard to monitor it; Learn techniques to deflate it in some of the most typical cases
Dask Best Practices
This page contains suggestions for Dask best practices and includes solutions to common Dask problems. This document specifically focuses on best practices that are shared among all of the Dask APIs. Readers may first want to investigate one of …
python - Memory clean up of Dask workers - Stack Overflow
Jan 18, 2020 · If your custom Python code does have some memory leak of its own then yes, you can Ask Dask workers to periodically restart themselves. See the dask-worker --help man page and look for keywords that start with --lifetime