This documentation is for a development version of Traitlets. There may be significant differences from the latest stable release.

Configurable objects with traitlets.config

This document describes traitlets.config, the traitlets-based configuration system used by IPython and Jupyter.

The main concepts

There are a number of abstractions that the IPython configuration system uses. Each of these abstractions is represented by a Python class.

Configuration object: Config
A configuration object is a simple dictionary-like class that holds configuration attributes and sub-configuration objects. These classes support dotted attribute style access ( in addition to the regular dictionary style access (cfg['Foo']['bar']). The Config object is a wrapper around a simple dictionary with some convenience methods, such as merging and automatic section creation.
Application: Application

An application is a process that does a specific job. The most obvious application is the ipython command line program. Each application reads one or more configuration files and a single set of command line options and then produces a master configuration object for the application. This configuration object is then passed to the configurable objects that the application creates. These configurable objects implement the actual logic of the application and know how to configure themselves given the configuration object.

Applications always have a log attribute that is a configured Logger. This allows centralized logging configuration per-application.

Configurable: Configurable

A configurable is a regular Python class that serves as a base class for all main classes in an application. The Configurable base class is lightweight and only does one things.

This Configurable is a subclass of HasTraits that knows how to configure itself. Class level traits with the metadata config=True become values that can be configured from the command line and configuration files.

Developers create Configurable subclasses that implement all of the logic in the application. Each of these subclasses has its own configuration information that controls how instances are created.

Singletons: SingletonConfigurable
Any object for which there is a single canonical instance. These are just like Configurables, except they have a class method instance(), that returns the current active instance (or creates one if it does not exist). instance()`.


Singletons are not strictly enforced - you can have many instances of a given singleton class, but the instance() method will always return the same one.

Having described these main concepts, we can now state the main idea in our configuration system: “configuration” allows the default values of class attributes to be controlled on a class by class basis. Thus all instances of a given class are configured in the same way. Furthermore, if two instances need to be configured differently, they need to be instances of two different classes. While this model may seem a bit restrictive, we have found that it expresses most things that need to be configured extremely well. However, it is possible to create two instances of the same class that have different trait values. This is done by overriding the configuration.

Now, we show what our configuration objects and files look like.

Configuration objects and files

A configuration object is little more than a wrapper around a dictionary. A configuration file is simply a mechanism for producing that object. The main IPython configuration file is a plain Python script, which can perform extensive logic to populate the config object. IPython 2.0 introduces a JSON configuration file, which is just a direct JSON serialization of the config dictionary, which is easily processed by external software.

When both Python and JSON configuration file are present, both will be loaded, with JSON configuration having higher priority.

Python configuration Files

A Python configuration file is a pure Python file that populates a configuration object. This configuration object is a Config instance. It is available inside the config file as c, and you simply set attributes on this. All you have to know is:

  • The name of the class to configure.
  • The name of the attribute.
  • The type of each attribute.

The answers to these questions are provided by the various Configurable subclasses that an application uses. Let’s look at how this would work for a simple configurable subclass

# Sample configurable:
from traitlets.config.configurable import Configurable
from traitlets import Int, Float, Unicode, Bool

class MyClass(Configurable):
    name = Unicode(u'defaultname'
        help="the name of the object"
    ranking = Integer(0, help="the class's ranking").tag(config=True)
    value = Float(99.0)
    # The rest of the class implementation would go here..

In this example, we see that MyClass has three attributes, two of which (name, ranking) can be configured. All of the attributes are given types and default values. If a MyClass is instantiated, but not configured, these default values will be used. But let’s see how to configure this class in a configuration file

# Sample config file = 'coolname'
c.MyClass.ranking = 10

After this configuration file is loaded, the values set in it will override the class defaults anytime a MyClass is created. Furthermore, these attributes will be type checked and validated anytime they are set. This type checking is handled by the traitlets module, which provides the Unicode, Integer and Float types; see Trait Types for the full list.

It should be very clear at this point what the naming convention is for configuration attributes:

c.ClassName.attribute_name = attribute_value

Here, ClassName is the name of the class whose configuration attribute you want to set, attribute_name is the name of the attribute you want to set and attribute_value the the value you want it to have. The ClassName attribute of c is not the actual class, but instead is another Config instance.


The careful reader may wonder how the ClassName (MyClass in the above example) attribute of the configuration object c gets created. These attributes are created on the fly by the Config instance, using a simple naming convention. Any attribute of a Config instance whose name begins with an uppercase character is assumed to be a sub-configuration and a new empty Config instance is dynamically created for that attribute. This allows deeply hierarchical information created easily (c.Foo.Bar.value) on the fly.

JSON configuration Files

A JSON configuration file is simply a file that contains a Config dictionary serialized to JSON. A JSON configuration file has the same base name as a Python configuration file, but with a .json extension.

Configuration described in previous section could be written as follows in a JSON configuration file:

  "MyClass": {
    "name": "coolname",
    "ranking": 10

JSON configuration files can be more easily generated or processed by programs or other languages.

Configuration files inheritance


This section only applies to Python configuration files.

Let’s say you want to have different configuration files for various purposes. Our configuration system makes it easy for one configuration file to inherit the information in another configuration file. The load_subconfig() command can be used in a configuration file for this purpose. Here is a simple example that loads all of the values from the file

c = get_config() = 'coolname'
c.MyClass.ranking = 100

into the configuration file

c = get_config()

# Load everything from

# Now override one of the values = 'bettername'

In a situation like this the load_subconfig() makes sure that the search path for sub-configuration files is inherited from that of the parent. Thus, you can typically put the two in the same directory and everything will just work.

Class based configuration inheritance

There is another aspect of configuration where inheritance comes into play. Sometimes, your classes will have an inheritance hierarchy that you want to be reflected in the configuration system. Here is a simple example:

from traitlets.config.configurable import Configurable
from traitlets import Integer, Float, Unicode, Bool

class Foo(Configurable):
    name = Unicode(u'fooname', config=True)
    value = Float(100.0, config=True)

class Bar(Foo):
    name = Unicode(u'barname', config=True)
    othervalue = Int(0, config=True)

Now, we can create a configuration file to configure instances of Foo and Bar:

# config file
c = get_config() = u'bestname'
c.Bar.othervalue = 10

This class hierarchy and configuration file accomplishes the following:

  • The default value for and will be ‘bestname’. Because Bar is a Foo subclass it also picks up the configuration information for Foo.
  • The default value for Foo.value and Bar.value will be 100.0, which is the value specified as the class default.
  • The default value for Bar.othervalue will be 10 as set in the configuration file. Because Foo is the parent of Bar it doesn’t know anything about the othervalue attribute.

Command-line arguments

All configurable options can also be supplied at the command line when launching the application. Applications use a parser called KeyValueLoader to load values into a Config object.

By default, values are assigned in much the same way as in a config file:

$ ipython --InteractiveShell.autoindent=False --BaseIPythonApplication.profile='myprofile'

Is the same as adding:


to your configuration file. Key/Value arguments always take a value, separated by ‘=’ and no spaces.


By default any error in configuration files with lead to this configuration file be ignored by default. Application subclasses may specify raise_config_file_errors = True to exit on failure to load config files, instead of the default of logging the failures.

New in version 4.3: The environement variable TRAITLETS_APPLICATION_RAISE_CONFIG_FILE_ERROR to '1' or 'true' to change the defautl value of raise_config_file_errors.

Common Arguments

Since the strictness and verbosity of the KVLoader above are not ideal for everyday use, common arguments can be specified as flags or aliases.

Flags and Aliases are handled by argparse instead, allowing for more flexible parsing. In general, flags and aliases are prefixed by --, except for those that are single characters, in which case they can be specified with a single -, e.g.:

$ ipython -i -c "import numpy; x=numpy.linspace(0,1)" --profile testing --colors=lightbg

Flags and aliases are declared by specifying flags and aliases attributes as dictionaries on subclasses of Application.

A key in both those dictionaries might be a string or tuple of strings. One-character strings are converted into “short” options (like -v); longer strings are “long” options (like --verbose).


For convenience, applications have a mapping of commonly used traits, so you don’t have to specify the whole class name:

$ ipython --profile myprofile
# and
$ ipython --profile='myprofile'
# are equivalent to
$ ipython --BaseIPythonApplication.profile='myprofile'

When specifying alias dictionary in code, the values might be the strings like 'Class.trait' or two-tuples like ('Class.trait', "Some help message").


Applications can also be passed flags. Flags are options that take no arguments. They are simply wrappers for setting one or more configurables with predefined values, often True/False.

For instance:

$ ipcontroller --debug
# is equivalent to
$ ipcontroller --Application.log_level=DEBUG
# and
$ ipython --matplotlib
# is equivalent to
$ ipython --matplotlib auto
# or
$ ipython --no-banner
# is equivalent to
$ ipython --TerminalIPythonApp.display_banner=False


Configurable applications can also have subcommands. Subcommands are modeled after git, and are called with the form command subcommand [...args]. For instance, the QtConsole is a subcommand of terminal IPython:

$ ipython qtconsole --profile myprofile

Subcommands are specified as a dictionary on Application instances, mapping subcommand names to two-tuples containing these:

  1. A subclass of Application to handle the subcommand. This can be specified as: - simply as a class, where its SingletonConfigurable.instance()

    will be invoked (straight-forward, but loads subclasses on import time);

    • as a string which can be imported to produce the above class;

    • as a factory function accepting a single argument like that:

      app_factory(parent_app: Application) -> Application


      The return value of the facory above is an instance, not a class, son the SingletonConfigurable.instance() is not invoked in this case.

    In all cases, the instanciated app is stored in Application.subapp and its Application.initialize() is invoked.

  2. A short description of the subcommand for use in help output.

To see a list of the available aliases, flags, and subcommands for a configurable application, simply pass -h or --help. And to see the full list of configurable options (very long), pass --help-all.

Design requirements

Here are the main requirements we wanted our configuration system to have:

  • Support for hierarchical configuration information.
  • Full integration with command line option parsers. Often, you want to read a configuration file, but then override some of the values with command line options. Our configuration system automates this process and allows each command line option to be linked to a particular attribute in the configuration hierarchy that it will override.
  • Configuration files that are themselves valid Python code. This accomplishes many things. First, it becomes possible to put logic in your configuration files that sets attributes based on your operating system, network setup, Python version, etc. Second, Python has a super simple syntax for accessing hierarchical data structures, namely regular attribute access ( Third, using Python makes it easy for users to import configuration attributes from one configuration file to another. Fourth, even though Python is dynamically typed, it does have types that can be checked at runtime. Thus, a 1 in a config file is the integer ‘1’, while a '1' is a string.
  • A fully automated method for getting the configuration information to the classes that need it at runtime. Writing code that walks a configuration hierarchy to extract a particular attribute is painful. When you have complex configuration information with hundreds of attributes, this makes you want to cry.
  • Type checking and validation that doesn’t require the entire configuration hierarchy to be specified statically before runtime. Python is a very dynamic language and you don’t always know everything that needs to be configured when a program starts.