Configuration

Taking full advantage of Dask sometimes requires user configuration.This might be to control logging verbosity, specify cluster configuration,provide credentials for security, or any of several other options that arise inproduction.

Configuration is specified in one of the following ways:

• YAML files in ~/.config/dask/ or /etc/dask/
• Environment variables like DASK_DISTRIBUTEDSCHEDULERWORK_STEALING=True
• Default settings within sub-librariesThis combination makes it easy to specify configuration in a variety ofsettings ranging from personal workstations, to IT-mandated configuration, todocker images.

Access Configuration

Configuration is usually read by using the dask.config module, either withthe config dictionary or the get function:

>>> import dask
>>> import dask.distributed  # populate config with distributed defaults
>>> dask.config.config
{
    "array": {
        "chunk-size": "128 MiB",
    }
    "distributed": {
        "logging": {
            "distributed": "info",
            "bokeh": "critical",
            "tornado": "critical"
         },
         "admin": {
             "log-format": "%(name)s - %(levelname)s - %(message)s"
         }
    }
}
 
>>> dask.config.get("distributed.logging")
{
    'distributed': 'info',
    'bokeh': 'critical',
    'tornado': 'critical'
}
 
>>> dask.config.get('distributed.logging.bokeh')  # use `.` for nested access
'critical'

You may wish to inspect the dask.config.config dictionary to get a sensefor what configuration is being used by your current system.

Note that the get function treats underscores and hyphens identically.For example, dask.config.get('num_workers') is equivalent todask.config.get('num-workers').

Values like "128 MiB" and "10s" are parsed using the functions inUtilities.

Specify Configuration

YAML files

You can specify configuration values in YAML files like the following:

array:
   chunk-size: 128 MiB
 
distributed:
  logging:
    distributed: info
    bokeh: critical
    tornado: critical
 
  scheduler:
    work-stealing: True
    allowed-failures: 5
 
   admin:
     log-format: '%(name)s - %(levelname)s - %(message)s'

These files can live in any of the following locations:

• The ~/.config/dask directory in the user’s home directory
• The {sys.prefix}/etc/dask directory local to Python
• The root directory (specified by the DASKROOT_CONFIG environmentvariable or /etc/dask/ by default)Dask searches for _all YAML files within each of these directories and mergesthem together, preferring configuration files closer to the user over systemconfiguration files (preference follows the order in the list above).Additionally, users can specify a path with the DASK_CONFIG environmentvariable, which takes precedence at the top of the list above.

The contents of these YAML files are merged together, allowing differentDask subprojects like dask-kubernetes or dask-ml to manage configurationfiles separately, but have them merge into the same global configuration.

Note: for historical reasons we also look in the ~/.dask directory forconfig files. This is deprecated and will soon be removed.

Environment Variables

You can also specify configuration values with environment variables likethe following:

export DASK_DISTRIBUTED__SCHEDULER__WORK_STEALING=True
export DASK_DISTRIBUTED__SCHEDULER__ALLOWED_FAILURES=5

resulting in configuration values like the following:

{
    'distributed': {
        'scheduler': {
            'work-stealing': True,
            'allowed-failures': 5
        }
    }
}

Dask searches for all environment variables that start with DASK_, thentransforms keys by converting to lower case and changing double-underscores tonested structures.

Dask tries to parse all values with ast.literal_eval, letting userspass numeric and boolean values (such as True in the example above) as wellas lists, dictionaries, and so on with normal Python syntax.

Environment variables take precedence over configuration values found in YAMLfiles.

Defaults

Additionally, individual subprojects may add their own default values when theyare imported. These are always added with lower priority than the YAML filesor environment variables mentioned above:

>>> import dask.config
>>> dask.config.config  # no configuration by default
{}
 
>>> import dask.distributed
>>> dask.config.config  # New values have been added
{
    'scheduler': ...,
    'worker': ...,
    'tls': ...
}

Directly within Python

Configuration is stored within a normal Python dictionary indask.config.config and can be modified using normal Python operations.

Additionally, you can temporarily set a configuration value using thedask.config.set function. This function accepts a dictionary as an inputand interprets "." as nested access:

>>> dask.config.set({'scheduler.work-stealing': True})

This function can also be used as a context manager for consistent cleanup:

with dask.config.set({'scheduler.work-stealing': True}):
    ...

Note that the set function treats underscores and hyphens identically.For example, dask.config.set({'scheduler.work-stealing': True}) isequivalent to dask.config.set({'scheduler.work_stealing': True}).

Updating Configuration

Manipulating configuration dictionaries

As described above, configuration can come from many places, including severalYAML files, environment variables, and project defaults. Each of theseprovides a configuration that is possibly nested like the following:

x = {'a': 0, 'c': {'d': 4}}
y = {'a': 1, 'b': 2, 'c': {'e': 5}}

Dask will merge these configurations respecting nested data structures, andrespecting order:

>>> dask.config.merge(x, y)
{'a': 1, 'b': 2, 'c': {'d': 4, 'e': 5}}

You can also use the update function to update the existing configurationin place with a new configuration. This can be done with priority being givento either config. This is often used to update the global configuration indask.config.config:

dask.config.update(dask.config, new, priority='new')  # Give priority to new values
dask.config.update(dask.config, new, priority='old')  # Give priority to old values

Sometimes it is useful to expand environment variables stored within aconfiguration. This can be done with the expand_environment_variablesfunction:

dask.config.config = dask.config.expand_environment_variables(dask.config.config)

Refreshing Configuration

If you change your environment variables or YAML files, Dask will notimmediately see the changes. Instead, you can call refresh to go throughthe configuration collection process and update the default configuration:

>>> dask.config.config
{}
 
>>> # make some changes to yaml files
 
>>> dask.config.refresh()
>>> dask.config.config
{...}

This function uses dask.config.collect, which returns the configurationwithout modifying the global configuration. You might use this to determinethe configuration of particular paths not yet on the config path:

>>> dask.config.collect(paths=[...])
{...}

Downstream Libraries

Downstream Dask libraries often follow a standard convention to use the centralDask configuration. This section provides recommendations for integrationusing a fictional project, dask-foo, as an example.

Downstream projects typically follow the following convention:

• Maintain default configuration in a YAML file within their sourcedirectory:

setup.py
dask_foo/__init__.py
dask_foo/config.py
dask_foo/core.py
dask_foo/foo.yaml  # <---

• Place configuration in that file within a namespace for the project:

# dask_foo/foo.yaml
 
foo:
  color: red
  admin:
    a: 1
    b: 2

• Within a config.py file (or anywhere) load that default config file andupdate it into the global configuration:

# dask_foo/config.py
import os
import yaml
 
import dask.config
 
fn = os.path.join(os.path.dirname(__file__), 'foo.yaml')
 
with open(fn) as f:
    defaults = yaml.load(f)
 
dask.config.update_defaults(defaults)

• Within that same config.py file, copy the 'foo.yaml' file to the user’sconfiguration directory if it doesn’t already exist.

We also comment the file to make it easier for us to change defaults in thefuture.

# ... continued from above
 
dask.config.ensure_file(source=fn, comment=True)

The user can investigate ~/.config/dask/*.yaml to see all of thecommented out configuration files to which they have access.

• Ensure that this file is run on import by including it in init.py:

# dask_foo/__init__.py
 
from . import config

• Within dask_foo code, use the dask.config.get function to accessconfiguration values:

# dask_foo/core.py
 
def process(fn, color=dask.config.get('foo.color')):
    ...

• You may also want to ensure that your yaml configuration files are includedin your package. This can be accomplished by including the following linein your MANIFEST.in:

recursive-include <PACKAGE_NAME> *.yaml

and the following in your setup.py setup call:

from setuptools import setup
 
setup(...,
      include_package_data=True,
      ...)

This process keeps configuration in a central place, but also keeps it safewithin namespaces. It places config files in an easy to access locationby default (~/.config/dask/*.yaml), so that users can easily discover whatthey can change, but maintains the actual defaults within the source code, sothat they more closely track changes in the library.

However, downstream libraries may choose alternative solutions, such asisolating their configuration within their library, rather than using theglobal dask.config system. All functions in the dask.config module alsowork with parameters, and do not need to mutate global state.

API

• dask.config.get(key, default='no_default', config={'temporary-directory': None, 'array': {'svg': {'size': 120}, 'chunk-size': '128MiB', 'rechunk-threshold': 4}})
• Get elements from global config

Use ‘.’ for nested access

See also

• dask.config.set

Examples

>>> from dask import config
>>> config.get('foo')  # doctest: +SKIP
{'x': 1, 'y': 2}

>>> config.get('foo.x')  # doctest: +SKIP
1

>>> config.get('foo.x.y', default=123)  # doctest: +SKIP
123

• dask.config.set(arg=None, config={'array': {'chunk-size': '128MiB', 'rechunk-threshold': 4, 'svg': {'size': 120}}, 'temporary-directory': None}, lock=, **kwargs)
• Temporarily set configuration values within a context manager

Parameters:

• arg:mapping or None, optional

• A mapping of configuration key-value pairs to set.

• **kwargs :

• Additional key-value pairs to set. If arg is provided, values setin arg will be applied before those in kwargs.Double-underscores (__) in keyword arguments will be replaced with., allowing nested values to be easily set.

See also

• dask.config.get

Examples

>>> import dask

Set 'foo.bar' in a context, by providing a mapping.

>>> with dask.config.set({'foo.bar': 123}):
...     pass

Set 'foo.bar' in a context, by providing a keyword argument.

>>> with dask.config.set(foo__bar=123):
...     pass

Set 'foo.bar' globally.

>>> dask.config.set(foo__bar=123)  # doctest: +SKIP

• dask.config.merge(*dicts)
• Update a sequence of nested dictionaries

This prefers the values in the latter dictionaries to those in the former

See also

• dask.config.update

Examples

>>> a = {'x': 1, 'y': {'a': 2}}
>>> b = {'y': {'b': 3}}
>>> merge(a, b)  # doctest: +SKIP
{'x': 1, 'y': {'a': 2, 'b': 3}}

• dask.config.update(old, new, priority='new')
• Update a nested dictionary with values from another

This is like dict.update except that it smoothly merges nested values

This operates in-place and modifies old

Parameters:

• priority: string {‘old’, ‘new’}
• If new (default) then the new dictionary has preference.Otherwise the old dictionary does.

See also

• dask.config.merge

Examples

>>> a = {'x': 1, 'y': {'a': 2}}
>>> b = {'x': 2, 'y': {'b': 3}}
>>> update(a, b)  # doctest: +SKIP
{'x': 2, 'y': {'a': 2, 'b': 3}}

>>> a = {'x': 1, 'y': {'a': 2}}
>>> b = {'x': 2, 'y': {'b': 3}}
>>> update(a, b, priority='old')  # doctest: +SKIP
{'x': 1, 'y': {'a': 2, 'b': 3}}

• dask.config.collect(paths=['/etc/dask', '/home/docs/checkouts/readthedocs.org/user_builds/dask/envs/latest/etc/dask', '/home/docs/.config/dask', '/home/docs/.dask'], env=None)
• Collect configuration from paths and environment variables

Parameters:

• paths:List[str]

• A list of paths to search for yaml config files

• env:dict

• The system environment variablesReturns:
• config: dict

See also

• dask.config.refresh
• collect configuration and update into primary config

• dask.config.refresh(config={'temporary-directory': None, 'array': {'svg': {'size': 120}, 'chunk-size': '128MiB', 'rechunk-threshold': 4}}, defaults=[{'temporary-directory': None, 'array': {'svg': {'size': 120}}}, {'array': {'chunk-size': '128MiB', 'rechunk-threshold': 4}}], **kwargs)
• Update configuration by re-reading yaml files and env variables

This mutates the global dask.config.config, or the config parameter ifpassed in.

This goes through the following stages:

• Clearing out all old configuration
• Updating from the stored defaults from downstream libraries(see update_defaults)
• Updating from yaml files and environment variablesNote that some functionality only checks configuration once at startup andmay not change behavior, even if configuration changes. It is recommendedto restart your python process if convenient to ensure that newconfiguration changes take place.

See also

• dask.config.collect
• for parameters
• dask.config.update_defaults

• dask.config.ensurefile(_source, destination=None, comment=True)
• Copy file to default location if it does not already exist

This tries to move a default configuration file to a default location ifif does not already exist. It also comments out that file by default.

This is to be used by downstream modules (like dask.distributed) that mayhave default configuration files that they wish to include in the defaultconfiguration path.

Parameters:

• source:string, filename

• Source configuration file, typically within a source directory.

• destination:string, directory

• Destination directory. Configurable by DASK_CONFIG environmentvariable, falling back to ~/.config/dask.

• comment:bool, True by default

• Whether or not to comment out the config file when copying.

• dask.config.expandenvironment_variables(_config)
• Expand environment variables in a nested config dictionary

This function will recursively search through any nested dictionariesand/or lists.

Parameters:

• config:dict, iterable, or str
• Input object to search for environment variablesReturns:
• config:same type as input

Examples

>>> expand_environment_variables({'x': [1, 2, '$USER']})  # doctest: +SKIP
{'x': [1, 2, 'my-username']}