dask-memusage

A low-impact profiler to figure out how much memory each task in Dask is using

Description

If you're using Dask with tasks that use a lot of memory, RAM is your bottleneck for parallelism. That means you want to know how much memory each task uses:

1. So you can set the highest parallelism level (process or threads) for each machine, given available to RAM. 2. In order to know where to focus memory optimization efforts.

dask-memusage is an MIT-licensed statistical memory profiler for Dask's Distributed scheduler that can help you with both these problems.

dask-memusage polls your processes for memory usage and records the minimum and maximum usage in a CSV.

Programming language: Python

License: MIT License

Tags: Profiler     Science And Data Analysis     Scientific     Distributed Computing    

Latest version: v1.1

README

dask-memusage

If you're using Dask with tasks that use a lot of memory, RAM is your bottleneck for parallelism. That means you want to know how much memory each task uses:

1. So you can set the highest parallelism level (process or threads) for each machine, given available to RAM.
2. In order to know where to focus memory optimization efforts.

dask-memusage is an MIT-licensed statistical memory profiler for Dask's Distributed scheduler that can help you with both these problems.

dask-memusage polls your processes for memory usage and records the minimum and maximum usage in a CSV:

task_key,min_memory_mb,max_memory_mb
"('from_sequence-map-sum-part-e15703211a549e75b11c63e0054b53e5', 0)",44.84765625,96.98046875
"('from_sequence-map-sum-part-e15703211a549e75b11c63e0054b53e5', 1)",47.015625,97.015625
"('sum-part-e15703211a549e75b11c63e0054b53e5', 0)",0,0
"('sum-part-e15703211a549e75b11c63e0054b53e5', 1)",0,0
sum-aggregate-apply-no_allocate-4c30eb545d4c778f0320d973d9fc8ea6,0,0
apply-no_allocate-4c30eb545d4c778f0320d973d9fc8ea6,47.265625,47.265625
task_key,min_memory_mb,max_memory_mb
"('from_sequence-map-sum-part-e15703211a549e75b11c63e0054b53e5', 0)",44.84765625,96.98046875
"('from_sequence-map-sum-part-e15703211a549e75b11c63e0054b53e5', 1)",47.015625,97.015625
"('sum-part-e15703211a549e75b11c63e0054b53e5', 0)",0,0
"('sum-part-e15703211a549e75b11c63e0054b53e5', 1)",0,0
sum-aggregate-apply-no_allocate-4c30eb545d4c778f0320d973d9fc8ea6,0,0
apply-no_allocate-4c30eb545d4c778f0320d973d9fc8ea6,47.265625,47.265625

Usage

Important: Make sure your workers only have a single thread! Otherwise the results will be wrong.

Installation

On the machine where you are running the Distributed scheduler, run:

$ pip install dask_memusage

Or if you're using Conda:

$ conda install -c conda-forge dask-memusage

API usage

# Add to your Scheduler object, which is e.g. your LocalCluster's scheduler
# attribute:
from dask_memoryusage import install
install(scheduler, "/tmp/memusage.csv")

CLI usage

$ dask-scheduler --preload dask_memusage --memusage.csv /tmp/memusage.csv

Limitations

• Again, make sure you only have one thread per worker process.
• This is statistical profiling, running every 10ms. Tasks that take less than that won't have accurate information.

Help

Need help? File a ticket at https://github.com/itamarst/dask-memusage/issues/new