aics-dask-utils 0.2.4

Utility functions and documentation related to Dask and AICS

AICS Dask Utils

Documentation related to Dask, Distributed, and related packages. Utility functions commonly used by AICS projects.

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Features

• Distributed handler to manage various debugging or cluster configurations
• Documentation on example cluster deployments

Basics

Before we jump into quick starts there are some basic definitions to understand.

Task

A task is a single static function to be processed. Simple enough. However, relevant to AICS, is that when using aicsimageio (and / or dask.array.Array), your image (or dask.array.Array) is split up into many tasks. This is dependent on the image reader and the size of the file you are reading. But in general it is safe to assume that each image you read is split many thousands of tasks. If you want to see how many tasks your image is split into you can either compute:

1. Psuedo-code: sum(2 * size(channel) for channel if channel not in ["Y", "X"])
2. Dask graph length: len(AICSImage.dask_data.__dask_graph__())

Map

Apply a given function to the provided iterables as used as parameters to the function. Given lambda x: x + 1 and [1, 2, 3], the result of map(func, *iterables) in this case would be [2, 3, 4]. Usually, you are provided back an iterable of future objects back from a map operation. The results from the map operation are not guaranteed to be in the order of the iterable that went in as operations are started as resources become available and item to item variance may result in different output ordering.

Future

An object that will become available but is currently not defined. There is no guarantee that the object is a valid result or an error and you should handle errors once the future's state has resolved (usually this means after a gather operation).

Gather

Block the process from moving forward until all futures are resolved. Control flow here would mean that you could potentially generate thousands of futures and keep moving on locally while those futures slowly resolve but if you ever want a hard stop and wait for some set of futures to complete, you would need gather them.

Other Comments

Dask tries to mirror the standard library concurrent.futures wherever possible which is what allows for this library to have simple wrappers around Dask to allow for easy debugging as we are simply swapping out distributed.Client.map with concurrent.futures.ThreadPoolExecutor.map for example. If at any point in your code you don't want to use dask for some reason or another, it is equally valid to use concurrent.futures.ThreadPoolExecutor or concurrent.futures.ProcessPoolExecutor.

Basic Mapping with Distributed Handler

If you have an iterable (or iterables) that would result in less than hundreds of thousands of tasks, it you can simply use the normal map provided by the DistributedHandler.client.

Important Note: Notice, "... iterable that would result in less than hundreds of thousands of tasks...". This is important because what happens when you try to map over a thousand image paths, each which spawns an AICSImage object. Each one adds thousands more tasks to the scheduler to complete. This will break and you should look to Large Iterable Batching instead.

from aics_dask_utils import DistributedHandler

# `None` address provided means use local machine threads
with DistributedHandler(None) as handler:
    futures = handler.client.map(
        lambda x: x + 1,
        [1, 2, 3]
    )

    results = handler.gather(futures)

from distributed import LocalCluster
cluster = LocalCluster()

# Actual address provided means use the dask scheduler
with DistributedHandler(cluster.scheduler_address) as handler:
    futures = handler.client.map(
        lambda x: x + 1,
        [1, 2, 3]
    )

    results = handler.gather(futures)

Large Iterable Batching

If you have an iterable (or iterables) that would result in more than hundreds of thousands of tasks, you should use handler.batched_map to reduce the load on the client. This will batch your requests rather than send than all at once.

from aics_dask_utils import DistributedHandler

# `None` address provided means use local machine threads
with DistributedHandler(None) as handler:
    results = handler.batched_map(
        lambda x: x + 1,
        range(1e9) # 1 billion
    )

from distributed import LocalCluster
cluster = LocalCluster()

# Actual address provided means use the dask scheduler
with DistributedHandler(cluster.scheduler_address) as handler:
    results = handler.batched_map(
        lambda x: x + 1,
        range(1e9) # 1 billion
    )

Note: Notice that there is no handler.gather call after batched_map. This is because batched_map gathers results at the end of each batch rather than simply returning their future's.

Installation

Stable Release: pip install aics_dask_utils
Development Head: pip install git+https://github.com/AllenCellModeling/aics_dask_utils.git

Documentation

For full package documentation please visit AllenCellModeling.github.io/aics_dask_utils.

Development

See CONTRIBUTING.md for information related to developing the code.

Additional Comments

This README, provided tooling, and documentation are not meant to be all encompassing of the various operations you can do with dask and other similar computing systems. For further reading go to dask.org.

Free software: Allen Institute Software License