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 (cfg.Foo.bar) 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 thing.

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()`.

Note

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('defaultname'
        help="the name of the object"
    ).tag(config=True)
    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
c.MyClass.name = '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 value you want it to have. The ClassName attribute of c is not the actual class, but instead is another Config instance.

Note

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

Note

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 base_config.py:

# base_config.py
c = get_config()
c.MyClass.name = 'coolname'
c.MyClass.ranking = 100

into the configuration file main_config.py:

# main_config.py
c = get_config()

# Load everything from base_config.py
load_subconfig('base_config.py')

# Now override one of the values
c.MyClass.name = '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('fooname', config=True)
    value = Float(100.0, config=True)

class Bar(Foo):
    name = Unicode('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()

c.Foo.name = 'bestname'
c.Bar.othervalue = 10

This class hierarchy and configuration file accomplishes the following:

  • The default value for Foo.name and Bar.name 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 KVArgParseConfigLoader 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:

c.InteractiveShell.autoindent = False
c.BaseIPythonApplication.profile = 'myprofile'

to your configuration file.

Changed in version 5.0: Prior to 5.0, fully specified --Class.trait=value arguments required an equals sign and no space separating the key and value. But after 5.0, these arguments can be separated by space as with aliases.

Changed in version 5.0: extra quotes around strings and literal prefixes are no longer required.

Changed in version 5.0: If a scalar (Unicode, Integer, etc.) is specified multiple times on the command-line, this will now raise. Prior to 5.0, all instances of the option before the last would be ignored.

Changed in version 5.0: In 5.0, positional extra arguments (typically a list of files) must be contiguous, for example:

mycommand file1 file2 --flag

or:

mycommand --flag file1 file2

whereas prior to 5.0, these “extra arguments” be distributed among other arguments:

mycommand file1 --flag file2

Note

Any error in configuration files which lead to this configuration file will 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 environment variable TRAITLETS_APPLICATION_RAISE_CONFIG_FILE_ERROR to '1' or 'true' to change the default value of raise_config_file_errors.

Common Arguments

Since the strictness and verbosity of the full --Class.trait=value form are not ideal for everyday use, common arguments can be specified as flags or aliases.

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).

Aliases

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").

Flags

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

Subcommands

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:

$ jupyter 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
      

      Note

      The return value of the factory 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.

Interpreting command-line strings

Prior to 5.0, we only had good support for Unicode or similar string types on the command-line. Other types were supported via ast.literal_eval(), which meant that simple types such as integers were well supported, too.

The downside of this implementation was that the literal_eval() happened before the type of the target trait was known, meaning that strings that could be interpreted as literals could end up with the wrong type, famously:

$ ipython -c 1
...
[TerminalIPythonApp] CRITICAL | Bad config encountered during initialization:
[TerminalIPythonApp] CRITICAL | The 'code_to_run' trait of a TerminalIPythonApp instance must be a unicode string, but a value of 1 <class 'int'> was specified.

This resulted in requiring redundant “double-quoting” of strings in many cases. That gets confusing when the shell also interprets quotes, so one had to:

$ ipython -c "'1'"

in order to set a string that looks like an integer.

traitlets 5.0 defers parsing of interpreting command-line strings to from_string(), which is an arbitrary function that will be called with the string given on the command-line. This eliminates the need to ‘guess’ how to interpret strings before we know what they are configuring.

Backward compatibility

It is not feasible to be perfectly backward-compatible when fixing behavior as problematic as this. However, we are doing our best to ensure that folks who had workarounds for this funky behavior are disrupted as little as we can manage. That means that we have kept what look like literals working wherever we could, so if you were double-quoting strings to ensure the were interpreted as strings, that will continue to work with warnings for the foreseeable future.

If you have an example command-line call that used to work with traitlets 4 but does not any more with traitlets 5, please let us know.

Custom traits

Custom trait types can override from_string() to specify how strings should be interpreted. This could for example allow specifying hex-encoded bytes on the command-line:

from binascii import a2b_hex
from traitlets.config import Application
from traitlets import Bytes


class HexBytes(Bytes):
    def from_string(self, s):
        return a2b_hex(s)


class App(Application):

    aliases = {"key": "App.key"}
    key = HexBytes(
        help="""
        Key to be used.

        Specify as hex on the command-line.
        """,
        config=True
    )

    def start(self):
        print(f"key={self.key}")


if __name__ == "__main__":
    App.launch_instance()
$ myprogram --key=a1b2
key=b'\xa2\xb2'

Container traits

In traitlets 5.0, items for container traits can be specified by passing the key multiple times, e.g.:

myprogram -l a -l b

to produce the list ["a", "b"]

or for dictionaries use key=value:

myprogram -d a=5 -l b=10

to produce the dict {"a": 5, "b": 10}.

In traitlets prior to 5.0, container traits (List, Dict) could technically be configured on the command-line by specifying a repr of a Python list or dict, e.g:

ipython --ScriptMagics.script_paths='{"perl": "/usr/bin/perl"}'

but that gets pretty tedious, especially with more than a couple of fields. This still works with a FutureWarning, but the new way allows container items to be specified by passing the argument multiple times:

ipython \
    --ScriptMagics.script_paths perl=/usr/bin/perl \
    --ScriptMagics.script_paths ruby=/usr/local/opt/bin/ruby

This handling is good enough that we can recommend defining aliases for container traits for the first time! For example:

from traitlets.config import Application
from traitlets import List, Dict, Integer, Unicode


class App(Application):

    aliases = {"x": "App.x", "y": "App.y"}
    x = List(Unicode(), config=True)
    y = Dict(Integer(), config=True)

    def start(self):
        print(f"x={self.x}")
        print(f"y={self.y}")


if __name__ == "__main__":
    App.launch_instance()

produces:

$ myprogram -x a -x b -y a=10 -y b=5
x=['a', 'b']
y={'a': 10, 'b': 5}

Note

Specifying the value trait of Dict was necessary to cast the values in y to integers. Otherwise, they values of y would have been the strings '10' and '5'.

For container types, List.from_string_list() is called with the list of all values specified on the command-line and is responsible for turning the list of strings into the appropriate type. Each item is then passed to List.item_from_string() which is responsible for handling the item, such as casting to integer or parsing key=value in the case of a Dict.

The deprecated ast.literal_eval() handling is preserved for backward-compatibility in the event of a single item that ‘looks like’ a list or dict literal.

If you would prefer, you can also use custom container traits which define :meth`~.TraitType.from_string` to expand a single string into a list, for example:

class PathList(List):
    def from_string(self, s):
        return s.split(os.pathsep)

which would allow:

myprogram --path /bin:/usr/local/bin:/opt/bin

to set a PathList trait with ["/bin", "/usr/local/bin", "/opt/bin"].

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 (Foo.Bar.Bam.name). 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.