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 in production.
Configuration is specified in one of the following ways:
YAML files in
~/.config/dask/
or/etc/dask/
Environment variables like
DASK_DISTRIBUTED__SCHEDULER__WORK_STEALING=True
Default settings within sub-libraries
This combination makes it easy to specify configuration in a variety of settings ranging from personal workstations, to IT-mandated configuration, to docker images.
Access Configuration¶
|
Get elements from global config |
Configuration is usually read by using the dask.config
module, either with
the 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 sense
for 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 to
dask.config.get('num-workers')
.
Values like "128 MiB"
and "10s"
are parsed using the functions in
Utilities.
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 directoryThe
{sys.prefix}/etc/dask
directory local to PythonThe root directory (specified by the
DASK_ROOT_CONFIG
environment variable or/etc/dask/
by default)
Dask searches for all YAML files within each of these directories and merges
them together, preferring configuration files closer to the user over system
configuration files (preference follows the order in the list above).
Additionally, users can specify a path with the DASK_CONFIG
environment
variable, which takes precedence at the top of the list above.
The contents of these YAML files are merged together, allowing different
Dask subprojects like dask-kubernetes
or dask-ml
to manage configuration
files separately, but have them merge into the same global configuration.
Note: for historical reasons we also look in the ``~/.dask`` directory for config files. This is deprecated and will soon be removed.
Environment Variables¶
You can also specify configuration values with environment variables like the 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_
, then
transforms keys by converting to lower case and changing double-underscores to
nested structures.
Dask tries to parse all values with ast.literal_eval, letting users
pass numeric and boolean values (such as True
in the example above) as well
as lists, dictionaries, and so on with normal Python syntax.
Environment variables take precedence over configuration values found in YAML files.
Defaults¶
Additionally, individual subprojects may add their own default values when they are imported. These are always added with lower priority than the YAML files or 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¶
|
Temporarily set configuration values within a context manager |
Configuration is stored within a normal Python dictionary in
dask.config.config
and can be modified using normal Python operations.
Additionally, you can temporarily set a configuration value using the
dask.config.set
function. This function accepts a dictionary as an input
and 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})
is
equivalent to dask.config.set({'scheduler.work_stealing': True})
.
Conversion Utility¶
It is possible to configure Dask inline with dot notation, with YAML or via environment variables. You can enter your own configuration items below to convert back and forth.
Warning
This utility is designed to improve understanding of converting between different notations and does not claim to be a perfect implementation. Please use for reference only.
YAML
Environment variable
Inline with dot notation
Updating Configuration¶
Manipulating configuration dictionaries¶
|
Update a sequence of nested dictionaries |
|
Update a nested dictionary with values from another |
Expand environment variables in a nested config dictionary |
As described above, configuration can come from many places, including several YAML files, environment variables, and project defaults. Each of these provides 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, and respecting 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 configuration
in place with a new configuration. This can be done with priority being given
to either config. This is often used to update the global configuration in
dask.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 a
configuration. This can be done with the expand_environment_variables
function:
dask.config.config = dask.config.expand_environment_variables(dask.config.config)
Refreshing Configuration¶
|
Collect configuration from paths and environment variables |
|
Update configuration by re-reading yaml files and env variables |
If you change your environment variables or YAML files, Dask will not
immediately see the changes. Instead, you can call refresh
to go through
the 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 configuration
without modifying the global configuration. You might use this to determine
the configuration of particular paths not yet on the config path:
>>> dask.config.collect(paths=[...])
{...}
Downstream Libraries¶
|
Copy file to default location if it does not already exist |
|
Update a nested dictionary with values from another |
|
Add a new set of defaults to the configuration |
Downstream Dask libraries often follow a standard convention to use the central
Dask configuration. This section provides recommendations for integration
using a fictional project, dask-foo
, as an example.
Downstream projects typically follow the following convention:
Maintain default configuration in a YAML file within their source directory:
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 and update 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’s configuration directory if it doesn’t already exist.We also comment the file to make it easier for us to change defaults in the future.
# ... continued from above dask.config.ensure_file(source=fn, comment=True)
The user can investigate
~/.config/dask/*.yaml
to see all of the commented 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 thedask.config.get
function to access configuration 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 included in your package. This can be accomplished by including the following line in 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 safe
within namespaces. It places config files in an easy to access location
by default (~/.config/dask/\*.yaml
), so that users can easily discover what
they can change, but maintains the actual defaults within the source code, so
that they more closely track changes in the library.
However, downstream libraries may choose alternative solutions, such as
isolating their configuration within their library, rather than using the
global dask.config system. All functions in the dask.config
module also
work with parameters, and do not need to mutate global state.
API¶
-
dask.config.
get
(key, default='__no_default__', config={'temporary-directory': None, 'dataframe': {'shuffle-compression': None}, 'array': {'svg': {'size': 120}, 'chunk-size': '128MiB', 'rechunk-threshold': 4}, 'optimization': {'fuse': {'active': True, 'ave-width': 1, 'max-width': None, 'max-height': inf, 'max-depth-new-edges': None, 'subgraphs': None, 'rename-keys': True}}})¶ Get elements from global config
Use ‘.’ for nested access
See also
Examples
>>> from dask import config >>> config.get('foo') {'x': 1, 'y': 2}
>>> config.get('foo.x') 1
>>> config.get('foo.x.y', default=123) 123
-
dask.config.
set
(arg=None, config={'array': {'chunk-size': '128MiB', 'rechunk-threshold': 4, 'svg': {'size': 120}}, 'dataframe': {'shuffle-compression': None}, 'optimization': {'fuse': {'active': True, 'ave-width': 1, 'max-depth-new-edges': None, 'max-height': inf, 'max-width': None, 'rename-keys': True, 'subgraphs': None}}, 'temporary-directory': None}, lock=<unlocked _thread.lock object>, **kwargs)¶ Temporarily set configuration values within a context manager
- Parameters
- argmapping or None, optional
A mapping of configuration key-value pairs to set.
- **kwargs :
Additional key-value pairs to set. If
arg
is provided, values set inarg
will be applied before those inkwargs
. Double-underscores (__
) in keyword arguments will be replaced with.
, allowing nested values to be easily set.
See also
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)
-
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
Examples
>>> a = {'x': 1, 'y': {'a': 2}} >>> b = {'y': {'b': 3}} >>> merge(a, b) {'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
Examples
>>> a = {'x': 1, 'y': {'a': 2}} >>> b = {'x': 2, 'y': {'b': 3}} >>> update(a, b) {'x': 2, 'y': {'a': 2, 'b': 3}}
>>> a = {'x': 1, 'y': {'a': 2}} >>> b = {'x': 2, 'y': {'b': 3}} >>> update(a, b, priority='old') {'x': 1, 'y': {'a': 2, 'b': 3}}
-
dask.config.
collect
(paths=['/etc/dask', '/opt/anaconda3/etc/dask', '/home/user/.config/dask', '/home/user/.dask'], env=None)¶ Collect configuration from paths and environment variables
- Parameters
- pathsList[str]
A list of paths to search for yaml config files
- envdict
The system environment variables
- Returns
- config: dict
See also
dask.config.refresh
collect configuration and update into primary config
-
dask.config.
refresh
(config={'temporary-directory': None, 'dataframe': {'shuffle-compression': None}, 'array': {'svg': {'size': 120}, 'chunk-size': '128MiB', 'rechunk-threshold': 4}, 'optimization': {'fuse': {'active': True, 'ave-width': 1, 'max-width': None, 'max-height': inf, 'max-depth-new-edges': None, 'subgraphs': None, 'rename-keys': True}}}, defaults=[{'temporary-directory': None, 'dataframe': {'shuffle-compression': None}, 'array': {'svg': {'size': 120}}, 'optimization': {'fuse': {'active': True, 'ave-width': 1, 'max-width': None, 'max-height': inf, 'max-depth-new-edges': None, 'subgraphs': None, 'rename-keys': True}}}, {'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 if passed 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 variables
Note that some functionality only checks configuration once at startup and may not change behavior, even if configuration changes. It is recommended to restart your python process if convenient to ensure that new configuration changes take place.
See also
dask.config.collect
for parameters
dask.config.update_defaults
-
dask.config.
ensure_file
(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 if if does not already exist. It also comments out that file by default.
This is to be used by downstream modules (like dask.distributed) that may have default configuration files that they wish to include in the default configuration path.
- Parameters
- sourcestring, filename
Source configuration file, typically within a source directory.
- destinationstring, directory
Destination directory. Configurable by
DASK_CONFIG
environment variable, falling back to ~/.config/dask.- commentbool, True by default
Whether or not to comment out the config file when copying.
-
dask.config.
expand_environment_variables
(config)¶ Expand environment variables in a nested config dictionary
This function will recursively search through any nested dictionaries and/or lists.
- Parameters
- configdict, iterable, or str
Input object to search for environment variables
- Returns
- configsame type as input
Examples
>>> expand_environment_variables({'x': [1, 2, '$USER']}) {'x': [1, 2, 'my-username']}