ContextQMD
Back to Dask

Configuration

docs/source/configuration.rst

2026.3.017.2 KB
Original Source

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:

  1. YAML files in ~/.config/dask/ or /etc/dask/
  2. Environment variables like DASK_DISTRIBUTED__SCHEDULER__WORK_STEALING=True
  3. 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

.. currentmodule:: dask

.. autosummary:: dask.config.get

Dask's configuration system is usually accessed using the dask.config.get function. You can use . for nested access, for example:

.. code-block:: python

import dask import dask.distributed # populate config with distributed defaults

dask.config.get("distributed.client") # use . for nested access {'heartbeat': '5s', 'scheduler-info-interval': '2s'}

dask.config.get("distributed.scheduler.unknown-task-duration") '500ms'

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("temporary-directory") is equivalent to dask.config.get("temporary_directory").

Values like "128 MiB" and "10s" are parsed using the functions in :ref:api.utilities.

Specify Configuration

YAML files


You can specify configuration values in YAML files. For example:

.. code-block:: yaml

   array:
     chunk-size: 128 MiB

   distributed:
     worker:
       memory:
         spill: 0.85  # default: 0.7
         target: 0.75  # default: 0.6
         terminate: 0.98  # default: 0.95

     dashboard:
       # Locate the dashboard if working on a Jupyter Hub server
       link: /user/<user>/proxy/8787/status


These files can live in any of the following locations:

1.  The ``~/.config/dask`` directory in the user's home directory
2.  The ``{sys.prefix}/etc/dask`` directory local to Python
3.  The ``{prefix}/etc/dask`` directories with ``{prefix}`` in `site.PREFIXES
    <https://docs.python.org/3/library/site.html#site.PREFIXES>`_
4.  The 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.

Dealing with obsolete configuration files

You may have old configuration files with deprecated keys in your file system. Ideally, you should update them to match the current configuration schema. If that's not possible e.g. because you don't have enough privileges, you can override obsolete keys with the updated keys in a higher-priority configuration file (as explained in the previous paragraph). This will silence warnings about deprecated keys. If a key has been outright removed from the latest schema, you can suppress the warnings by adding it under a special deprecated-keys section of your higher-priority configuration file.

Environment Variables


You can also specify configuration values with environment variables like
the following:

.. code-block:: bash

   export DASK_DISTRIBUTED__SCHEDULER__WORK_STEALING=True
   export DASK_DISTRIBUTED__SCHEDULER__ALLOWED_FAILURES=5
   export DASK_DISTRIBUTED__DASHBOARD__LINK="/user/<user>/proxy/8787/status"

resulting in configuration values like the following:

.. code-block:: python

   {
       '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
<https://docs.python.org/3/library/ast.html#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:

.. code-block:: python

   >>> 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

.. autosummary:: dask.config.set

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:

.. code-block:: python

dask.config.set({'optimization.fuse.ave-width': 4})

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

.. code-block:: python

with dask.config.set({'optimization.fuse.ave-width': 4}): ... arr2, = dask.optimize(arr)

Note that the set function treats underscores and hyphens identically. For example, dask.config.set({'optimization.fuse.ave_width': 4}) is equivalent to dask.config.set({'optimization.fuse.ave-width': 4}).

Finally, note that persistent objects may acquire configuration settings when they are initialized. These settings may also be cached for performance reasons. This is particularly true for dask.distributed objects such as Client, Scheduler, Worker, and Nanny.

Directly from CLI


Configuration can also be set and viewed from the CLI.

.. code-block::

	$ dask config set optimization.fuse.ave-width 4
	Updated [optimization.fuse.ave-width] to [4], config saved to ~/dask/dask.yaml

	$ dask config get optimization.fuse.ave-width
	4


Distributing configuration

It may also be desirable to package up your whole Dask configuration for use on another machine. This is used in some Dask Distributed libraries to ensure remote components have the same configuration as your local system.

This is typically handled by the downstream libraries which use base64 encoding to pass config via the DASK_INTERNAL_INHERIT_CONFIG environment variable.

.. autosummary:: dask.config.serialize dask.config.deserialize

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**

.. raw:: html

   <textarea id="configConvertUtilYAML" name="configConvertUtilYAML" rows="10" cols="50" class="configTextArea" wrap="off">
   array:
      chunk-size: 128 MiB

   distributed:
      workers:
         memory:
            spill: 0.85
            target: 0.75
            terminate: 0.98
   </textarea>

**Environment variable**

.. raw:: html

   <textarea id="configConvertUtilEnv" name="configConvertUtilEnv" rows="10" cols="50" class="configTextArea" wrap="off">
   export DASK_ARRAY__CHUNK_SIZE="128 MiB"
   export DASK_DISTRIBUTED__WORKERS__MEMORY__SPILL=0.85
   export DASK_DISTRIBUTED__WORKERS__MEMORY__TARGET=0.75
   export DASK_DISTRIBUTED__WORKERS__MEMORY__TERMINATE=0.98
   </textarea>

**Inline with dot notation**

.. raw:: html

   <textarea id="configConvertUtilCode" name="configConvertUtilCode" rows="10" cols="50" class="configTextArea" wrap="off">
   >>> dask.config.set({"array.chunk-size": "128 MiB"})
   >>> dask.config.set({"distributed.workers.memory.spill": 0.85})
   >>> dask.config.set({"distributed.workers.memory.target": 0.75})
   >>> dask.config.set({"distributed.workers.memory.terminate": 0.98})
   </textarea>

Updating Configuration
----------------------

Manipulating configuration dictionaries

.. autosummary:: dask.config.merge dask.config.update dask.config.expand_environment_variables

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:

.. code-block:: python

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:

.. code-block:: python

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:

.. code-block:: python

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:

.. code-block:: python

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

Refreshing Configuration


.. autosummary::
   dask.config.collect
   dask.config.refresh

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:

.. code-block:: python

   >>> 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:

.. code-block:: python

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

Downstream Libraries
--------------------

.. autosummary::
   dask.config.ensure_file
   dask.config.update
   dask.config.update_defaults

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:

1.  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  # <---

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

    .. code-block:: yaml

       # dask_foo/foo.yaml

       foo:
         color: red
         admin:
           a: 1
           b: 2

3.  Within a config.py file (or anywhere) load that default config file and
    update it into the global configuration:

    .. code-block:: python

       # 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.safe_load(f)

       dask.config.update_defaults(defaults)

4.  Ensure that this file is run on import by including it in ``__init__.py``:

    .. code-block:: python

       # dask_foo/__init__.py

       from . import config

5.  Within ``dask_foo`` code, use the ``dask.config.get`` function to access
    configuration values:

    .. code-block:: python

       # dask_foo/core.py

       def process(fn, color=dask.config.get('foo.color')):
           ...

6.  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:

    .. code-block:: python

        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
---

.. autofunction:: dask.config.get
.. autofunction:: dask.config.set
.. autofunction:: dask.config.merge
.. autofunction:: dask.config.update
.. autofunction:: dask.config.collect
.. autofunction:: dask.config.refresh
.. autofunction:: dask.config.ensure_file
.. autofunction:: dask.config.expand_environment_variables


Configuration Reference
-----------------------

.. contents:: :local:

.. note::
   It is possible to configure Dask inline with dot notation, with YAML or via environment variables.
   See the `conversion utility <#conversion-utility>`_ for converting the following dot notation to other forms.

Dask
~~~~

.. dask-config-block::
    :location: dask
    :config: https://raw.githubusercontent.com/dask/dask/main/dask/dask.yaml
    :schema: https://raw.githubusercontent.com/dask/dask/main/dask/dask-schema.yaml


Distributed Client
~~~~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.client
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml

Distributed Comm
~~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.comm
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml


Distributed Dashboard
~~~~~~~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.dashboard
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml


Distributed Deploy
~~~~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.deploy
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml


Distributed Scheduler
~~~~~~~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.scheduler
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml


Distributed Worker
~~~~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.worker
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml


Distributed Nanny
~~~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.nanny
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml


Distributed Admin
~~~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.admin
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml


Distributed RMM
~~~~~~~~~~~~~~~

.. dask-config-block::
    :location: distributed.rmm
    :config: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed.yaml
    :schema: https://raw.githubusercontent.com/dask/distributed/main/distributed/distributed-schema.yaml