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ML Collections is a library of Python collections designed for ML usecases.

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ML Collections

ML Collections is a library of Python Collections designed for ML use cases.

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The two classes called ConfigDict and FrozenConfigDict are "dict-like" data structures with dot access to nested elements. Together, they are supposed to be used as a main way of expressing configurations of experiments and models.

This document describes example usage of ConfigDict, FrozenConfigDict, FieldReference.


  • Dot-based access to fields.
  • Locking mechanism to prevent spelling mistakes.
  • Lazy computation.
  • FrozenConfigDict() class which is immutable and hashable.
  • Type safety.
  • "Did you mean" functionality.
  • Human readable printing (with valid references and cycles), using valid YAML format.
  • Fields can be passed as keyword arguments using the ** operator.
  • There are two exceptions to the strong type-safety of the ConfigDict. int values can be passed in to fields of type float. In such a case, the value is type-converted to a float before being stored. Similarly, all string types (including Unicode strings) can be stored in fields of type str or unicode.

Basic Usage

import ml_collections

cfg = ml_collections.ConfigDict()
cfg.float_field = 12.6
cfg.integer_field = 123
cfg.another_integer_field = 234
cfg.nested = ml_collections.ConfigDict()
cfg.nested.string_field = 'tom'

print(cfg.integer_field)  # Prints 123.
print(cfg['integer_field'])  # Prints 123 as well.

  cfg.integer_field = 'tom'  # Raises TypeError as this field is an integer.
except TypeError as e:

cfg.float_field = 12  # Works: `Int` types can be assigned to `Float`.
cfg.nested.string_field = u'bob'  # `String` fields can store Unicode strings.



A FrozenConfigDictis an immutable, hashable type of ConfigDict:

import ml_collections

initial_dictionary = {
    'int': 1,
    'list': [1, 2],
    'tuple': (1, 2, 3),
    'set': {1, 2, 3, 4},
    'dict_tuple_list': {'tuple_list': ([1, 2], 3)}

cfg = ml_collections.ConfigDict(initial_dictionary)
frozen_dict = ml_collections.FrozenConfigDict(initial_dictionary)

print(frozen_dict.tuple)  # Prints tuple (1, 2, 3)
print(frozen_dict.list)  # Prints tuple (1, 2)
print(frozen_dict.set)  # Prints frozenset {1, 2, 3, 4}
print(frozen_dict.dict_tuple_list.tuple_list[0])  # Prints tuple (1, 2)

frozen_cfg = ml_collections.FrozenConfigDict(cfg)
print(frozen_cfg == frozen_dict)  # True
print(hash(frozen_cfg) == hash(frozen_dict))  # True

try: = 2 # Raises TypeError as FrozenConfigDict is immutable.
except AttributeError as e:

# Converting between `FrozenConfigDict` and `ConfigDict`:
thawed_frozen_cfg = ml_collections.ConfigDict(frozen_dict)
print(thawed_frozen_cfg == cfg)  # True
frozen_cfg_to_cfg = frozen_dict.as_configdict()
print(frozen_cfg_to_cfg == cfg)  # True

FieldReferences and placeholders

A FieldReference is useful for having multiple fields use the same value. It can also be used for lazy computation.

You can use placeholder() as a shortcut to create a FieldReference (field) with a None default value. This is useful if a program uses optional configuration fields.

import ml_collections
from ml_collections.config_dict import config_dict

placeholder = ml_collections.FieldReference(0)
cfg = ml_collections.ConfigDict()
cfg.placeholder = placeholder
cfg.optional = config_dict.placeholder(int)
cfg.nested = ml_collections.ConfigDict()
cfg.nested.placeholder = placeholder

  cfg.optional = 'tom'  # Raises Type error as this field is an integer.
except TypeError as e:

cfg.optional = 1555  # Works fine.
cfg.placeholder = 1  # Changes the value of both placeholder and
                     # nested.placeholder fields.


Note that the indirection provided by FieldReferences will be lost if accessed through a ConfigDict.

import ml_collections

placeholder = ml_collections.FieldReference(0)
cfg.field1 = placeholder
cfg.field2 = placeholder  # This field will be tied to cfg.field1.
cfg.field3 = cfg.field1  # This will just be an int field initialized to 0.

Lazy computation

Using a FieldReference in a standard operation (addition, subtraction, multiplication, etc...) will return another FieldReference that points to the original's value. You can use FieldReference.get() to execute the operations and get the reference's computed value, and FieldReference.set() to change the original reference's value.

import ml_collections

ref = ml_collections.FieldReference(1)
print(ref.get())  # Prints 1

add_ten = ref.get() + 10  # ref.get() is an integer and so is add_ten
add_ten_lazy = ref + 10  # add_ten_lazy is a FieldReference - NOT an integer

print(add_ten)  # Prints 11
print(add_ten_lazy.get())  # Prints 11 because ref's value is 1

# Addition is lazily computed for FieldReferences so changing ref will change
# the value that is used to compute add_ten.
print(add_ten)  # Prints 11
print(add_ten_lazy.get())  # Prints 15 because ref's value is 5

If a FieldReference has None as its original value, or any operation has an argument of None, then the lazy computation will evaluate to None.

We can also use fields in a ConfigDict in lazy computation. In this case a field will only be lazily evaluated if ConfigDict.get_ref() is used to get it.

import ml_collections

config = ml_collections.ConfigDict()
config.reference_field = ml_collections.FieldReference(1)
config.integer_field = 2
config.float_field = 2.5

# No lazy evaluatuations because we didn't use get_ref()
config.no_lazy = config.integer_field * config.float_field

# This will lazily evaluate ONLY config.integer_field
config.lazy_integer = config.get_ref('integer_field') * config.float_field

# This will lazily evaluate ONLY config.float_field
config.lazy_float = config.integer_field * config.get_ref('float_field')

# This will lazily evaluate BOTH config.integer_field and config.float_Field
config.lazy_both = (config.get_ref('integer_field') *

config.integer_field = 3
print(config.no_lazy)  # Prints 5.0 - It uses integer_field's original value

print(config.lazy_integer)  # Prints 7.5

config.float_field = 3.5
print(config.lazy_float)  # Prints 7.0
print(config.lazy_both)  # Prints 10.5

Changing lazily computed values

Lazily computed values in a ConfigDict can be overridden in the same way as regular values. The reference to the FieldReference used for the lazy computation will be lost and all computations downstream in the reference graph will use the new value.

import ml_collections

config = ml_collections.ConfigDict()
config.reference = 1
config.reference_0 = config.get_ref('reference') + 10
config.reference_1 = config.get_ref('reference') + 20
config.reference_1_0 = config.get_ref('reference_1') + 100

print(config.reference)  # Prints 1.
print(config.reference_0)  # Prints 11.
print(config.reference_1)  # Prints 21.
print(config.reference_1_0)  # Prints 121.

config.reference_1 = 30

print(config.reference)  # Prints 1 (unchanged).
print(config.reference_0)  # Prints 11 (unchanged).
print(config.reference_1)  # Prints 30.
print(config.reference_1_0)  # Prints 130.


You cannot create cycles using references. Fortunately the only way to create a cycle is by assigning a computed field to one that is not the result of computation. This is forbidden:

import ml_collections
from ml_collections.config_dict import config_dict

config = ml_collections.ConfigDict()
config.integer_field = 1
config.bigger_integer_field = config.get_ref('integer_field') + 10

  # Raises a MutabilityError because setting config.integer_field would
  # cause a cycle.
  config.integer_field = config.get_ref('bigger_integer_field') + 2
except config_dict.MutabilityError as e:

Advanced usage

Here are some more advanced examples showing lazy computation with different operators and data types.

import ml_collections

config = ml_collections.ConfigDict()
config.float_field = 12.6
config.integer_field = 123
config.list_field = [0, 1, 2]

config.float_multiply_field = config.get_ref('float_field') * 3
print(config.float_multiply_field)  # Prints 37.8

config.float_field = 10.0
print(config.float_multiply_field)  # Prints 30.0

config.longer_list_field = config.get_ref('list_field') + [3, 4, 5]
print(config.longer_list_field)  # Prints [0, 1, 2, 3, 4, 5]

config.list_field = [-1]
print(config.longer_list_field)  # Prints [-1, 3, 4, 5]

# Both operands can be references
config.ref_subtraction = (
    config.get_ref('float_field') - config.get_ref('integer_field'))
print(config.ref_subtraction)  # Prints -113.0

config.integer_field = 10
print(config.ref_subtraction)  # Prints 0.0

Equality checking

You can use == and .eq_as_configdict() to check equality among ConfigDict and FrozenConfigDict objects.

import ml_collections

dict_1 = {'list': [1, 2]}
dict_2 = {'list': (1, 2)}
cfg_1 = ml_collections.ConfigDict(dict_1)
frozen_cfg_1 = ml_collections.FrozenConfigDict(dict_1)
frozen_cfg_2 = ml_collections.FrozenConfigDict(dict_2)

# True because FrozenConfigDict converts lists to tuples
print(frozen_cfg_1.items() == frozen_cfg_2.items())
# False because == distinguishes the underlying difference
print(frozen_cfg_1 == frozen_cfg_2)

# False because == distinguishes these types
print(frozen_cfg_1 == cfg_1)
# But eq_as_configdict() treats both as ConfigDict, so these are True:

Equality checking with lazy computation

Equality checks see if the computed values are the same. Equality is satisfied if two sets of computations are different as long as they result in the same value.

import ml_collections

cfg_1 = ml_collections.ConfigDict()
cfg_1.a = 1
cfg_1.b = cfg_1.get_ref('a') + 2

cfg_2 = ml_collections.ConfigDict()
cfg_2.a = 1
cfg_2.b = cfg_2.get_ref('a') * 3

# True because all computed values are the same
print(cfg_1 == cfg_2)

Locking and copying

Here is an example with lock() and deepcopy():

import copy
import ml_collections

cfg = ml_collections.ConfigDict()
cfg.integer_field = 123

# Locking prohibits the addition and deletion of new fields but allows
# modification of existing values.
  cfg.integer_field = 124  # Raises AttributeError and suggests valid field.
except AttributeError as e:
with cfg.unlocked():
  cfg.integer_field = 1555  # Works fine too.

# Get a copy of the config dict.
new_cfg = copy.deepcopy(cfg)
new_cfg.integer_field = -123  # Works fine.


Dictionary attributes and initialization

import ml_collections

referenced_dict = {'inner_float': 3.14}
d = {
    'referenced_dict_1': referenced_dict,
    'referenced_dict_2': referenced_dict,
    'list_containing_dict': [{'key': 'value'}],

# We can initialize on a dictionary
cfg = ml_collections.ConfigDict(d)

# Reference structure is preserved
print(id(cfg.referenced_dict_1) == id(cfg.referenced_dict_2))  # True

# And the dict attributes have been converted to ConfigDict
print(type(cfg.referenced_dict_1))  # ConfigDict

# However, the initialization does not look inside of lists, so dicts inside
# lists are not converted to ConfigDict
print(type(cfg.list_containing_dict[0]))  # dict

More Examples

For more examples, take a look at ml_collections/config_dict/examples/

For examples and gotchas specifically about initializing a ConfigDict, see ml_collections/config_dict/examples/

Config Flags

This library adds flag definitions to absl.flags to handle config files. It does not wrap absl.flags so if using any standard flag definitions alongside config file flags, users must also import absl.flags.

Currently, this module adds two new flag types, namely DEFINE_config_file which accepts a path to a Python file that generates a configuration, and DEFINE_config_dict which accepts a configuration directly. Configurations are dict-like structures (see ConfigDict) whose nested elements can be overridden using special command-line flags. See the examples below for more details.


Use ml_collections.config_flags alongside absl.flags. For example:

from absl import app
from absl import flags

from ml_collections.config_flags import config_flags


def main(_):

if __name__ == '__main__':

# Note that this is a valid Python script.
# get_config() can return an arbitrary dict-like object. However, it is advised
# to use ml_collections.ConfigDict.
# See ml_collections/config_dict/examples/

import ml_collections

def get_config():
  config = ml_collections.ConfigDict()
  config.field1 = 1
  config.field2 = 'tom'
  config.nested = ml_collections.ConfigDict()
  config.nested.field = 2.23
  config.tuple = (1, 2, 3)
  return config

Now, after running:

python \
                 --my_config.field1=8 \
                 --my_config.nested.field=2.1 \
                 --my_config.tuple='(1, 2, (1, 2))'

we get:

field1: 8
field2: tom
  field: 2.1
tuple: !!python/tuple
- 1
- 2
- !!python/tuple
  - 1
  - 2

Usage of DEFINE_config_dict is similar to DEFINE_config_file, the main difference is the configuration is defined in instead of in a separate file.

from absl import app
from absl import flags

import ml_collections
from ml_collections.config_flags import config_flags

config = ml_collections.ConfigDict()
config.field1 = 1
config.field2 = 'tom'
config.nested = ml_collections.ConfigDict()
config.nested.field = 2.23
config.tuple = (1, 2, 3)

config_flags.DEFINE_config_dict('my_config', config)

def main(_):

if __name__ == '__main__':

config_file flags are compatible with the command-line flag syntax. All the following options are supported for non-boolean values in configurations:

  • -(-)config.field=value
  • -(-)config.field value

Options for boolean values are slightly different:

  • -(-)config.boolean_field: set boolean value to True.
  • -(-)noconfig.boolean_field: set boolean value to False.
  • -(-)config.boolean_field=value: value is true, false, True or False.

Note that -(-)config.boolean_field value is not supported.

Parameterising the get_config() function

It's sometimes useful to be able to pass parameters into get_config, and change what is returned based on this configuration. One example is if you are grid searching over parameters which have a different hierarchical structure - the flag needs to be present in the resulting ConfigDict. It would be possible to include the union of all possible leaf values in your ConfigDict, but this produces a confusing config result as you have to remember which parameters will actually have an effect and which won't.

A better system is to pass some configuration, indicating which structure of ConfigDict should be returned. An example is the following config file:

import ml_collections

def get_config(config_string):
  possible_structures = {
      'linear': ml_collections.ConfigDict({
          'model_constructor': 'snt.Linear',
          'model_config': ml_collections.ConfigDict({
              'output_size': 42,
      'lstm': ml_collections.ConfigDict({
          'model_constructor': 'snt.LSTM',
          'model_config': ml_collections.ConfigDict({
              'hidden_size': 108,

  return possible_structures[config_string]

The value of config_string will be anything that is to the right of the first colon in the config file path, if one exists. If no colon exists, no value is passed to get_config (producing a TypeError if get_config expects a value.)

The above example can be run like:

python -- \

or like:

python -- \

Additional features

  • Loads any valid python script which defines get_config() function returning any python object.
  • Automatic locking of the loaded object, if the loaded object defines a callable .lock() method.
  • Supports command-line overriding of arbitrarily nested values in dict-like objects (with key/attribute based getters/setters) of the following types:
    • types.IntType (integer)
    • types.FloatType (float)
    • types.BooleanType (bool)
    • types.StringType (string)
    • types.TupleType (tuple)
  • Overriding is type safe.
  • Overriding of TupleType can be done by passing in the tuple as a string (see the example in the Usage section).
  • The overriding tuple object can be of a different size and have different types than the original. Nested tuples are also supported.


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