Authors: Dan Holtmann-Rice, Sergio Guadarrama, Nathan Silberman Contributors: Oscar Ramirez, Marek Fiser
Gin provides a lightweight configuration framework for Python, based on
dependency injection. Functions or classes can be decorated with
@gin.configurable, allowing default parameter values to be supplied from a
config file (or passed via the command line) using a simple but powerful syntax.
This removes the need to define and maintain configuration objects (e.g.
protos), or write boilerplate parameter plumbing and factory code, while often
dramatically expanding a project's flexibility and configurability.
Gin is particularly well suited for machine learning experiments (e.g. using TensorFlow), which tend to have many parameters, often nested in complex ways.
This is not an official Google product.
This section provides a high-level overview of Gin's main features, ordered roughly from "basic" to "advanced". More details on these and other features can be found in the user guide.
Install Gin with pip:
pip install gin-config
Install Gin from source:
git clone https://github.com/google/gin-config cd gin-config python -m setup.py install
Import Gin (without TensorFlow functionality):
Import additional TensorFlow-specific functionality via the
Import additional PyTorch-specific functionality via the
At its most basic, Gin can be seen as a way of providing or changing default
values for function or constructor parameters. To make a function's parameters
"configurable", Gin provides the
@gin.configurable def dnn(inputs, num_outputs, layer_sizes=(512, 512), activation_fn=tf.nn.relu): ...
This decorator registers the
dnn function with Gin, and automatically makes
all of its parameters configurable. To set ("bind") a value for the
layer_sizes parameter above within a ".gin" configuration file:
# Inside "config.gin" dnn.layer_sizes = (1024, 512, 128)
Bindings have syntax
function_name.parameter_name = value. All Python literal
values are supported as
value (numbers, strings, lists, tuples, dicts). Once
the config file has been parsed by Gin, any future calls to
dnn will use the
Gin-specified value for
layer_sizes (unless a value is explicitly provided by
Classes can also be marked as configurable, in which case the configuration applies to constructor parameters:
@gin.configurable class DNN(object): # Constructor parameters become configurable. def __init__(self, num_outputs, layer_sizes=(512, 512), activation_fn=tf.nn.relu): ... def __call__(inputs): ...
Within a config file, the class name is used when binding values to constructor parameters:
# Inside "config.gin" DNN.layer_sizes = (1024, 512, 128)
Finally, after defining or importing all configurable classes or functions,
parse your config file to bind your configurations (to also permit multiple
config files and command line overrides, see
Note that no other changes are required to the Python code, beyond adding the
gin.configurable decorator and a call to one of Gin's parsing functions.
In addition to accepting Python literal values, Gin also supports passing other
Gin-configurable functions or classes. In the example above, we might want to
activation_fn parameter. If we have registered, say
with Gin (see registering external functions), we can
pass it to
activation_fn by referring to it as
# Inside "config.gin" dnn.activation_fn = @tf.nn.tanh
Gin refers to
@name constructs as configurable references. Configurable
references work for classes as well:
def train_fn(..., optimizer_cls, learning_rate): optimizer = optimizer_cls(learning_rate) ...
Then, within a config file:
# Inside "config.gin" train_fn.optimizer_cls = @tf.train.GradientDescentOptimizer ...
Sometimes it is necessary to pass the result of calling a specific function or
class constructor. Gin supports "evaluating" configurable references via the
@name() syntax. For example, say we wanted to use the class form of
above (which implements
__call__ to "behave" like a function) in the following
def build_model(inputs, network_fn, ...): logits = network_fn(inputs) ...
We could pass an instance of the
DNN class to the
# Inside "config.gin" build_model.network_fn = @DNN()
To use evaluated references, all of the referenced function or class's
parameters must be provided via Gin. The call to the function or constructor
takes place just before the call to the function to which the result is
passed, In the above example, this would be just before
build_model is called.
The result is not cached, so a new
DNN instance will be constructed for each
What happens if we want to configure the same function in different ways? For
instance, imagine we're building a GAN, where we might have a "generator"
network and a "discriminator" network. We'd like to use the
dnn function above
to construct both, but with different parameters:
def build_model(inputs, generator_network_fn, discriminator_network_fn, ...): ...
To handle this case, Gin provides "scopes", which provide a name for a specific
set of bindings for a given function or class. In both bindings and references,
the "scope name" precedes the function name, separated by a "
# Inside "config.gin" build_model.generator_network_fn = @generator/dnn build_model.discriminator_network_fn = @discriminator/dnn generator/dnn.layer_sizes = (128, 256) generator/dnn.num_outputs = 784 discriminator/dnn.layer_sizes = (512, 256) discriminator/dnn.num_outputs = 1 dnn.activation_fn = @tf.nn.tanh
In this example, the generator network has increasing layer widths and 784 outputs, while the discriminator network has decreasing layer widths and 1 output.
Any parameters set on the "root" (unscoped) function name are inherited by
scoped variants (unless explicitly overridden), so in the above example both the
generator and the discriminator use the
tf.nn.tanh activation function.
The greatest degree of flexibility and configurability in a project is achieved
by writing small modular functions and "wiring them up" hierarchically via
(possibly scoped) references. For example, this code sketches a generic training
setup that could be used with the
@gin.configurable def build_model_fn(network_fn, loss_fn, optimize_loss_fn): def model_fn(features, labels): logits = network_fn(features) loss = loss_fn(labels, logits) train_op = optimize_loss_fn(loss) ... return model_fn @gin.configurable def optimize_loss(loss, optimizer_cls, learning_rate): optimizer = optimizer_cls(learning_rate) return optimizer.minimize(loss) @gin.configurable def input_fn(file_pattern, batch_size, ...): ... @gin.configurable def run_training(train_input_fn, eval_input_fn, estimator, steps=1000): estimator.train(train_input_fn, steps=steps) estimator.evaluate(eval_input_fn) ...
In conjunction with suitable external configurables to register TensorFlow
Estimator and various optimizers), this could be
configured as follows:
# Inside "config.gin" run_training.train_input_fn = @train/input_fn run_training.eval_input_fn = @eval/input_fn input_fn.batch_size = 64 # Shared by both train and eval... train/input_fn.file_pattern = ... eval/input_fn.file_pattern = ... run_training.estimator = @tf.estimator.Estimator() tf.estimator.Estimator.model_fn = @build_model_fn() build_model_fn.network_fn = @dnn dnn.layer_sizes = (1024, 512, 256) build_model_fn.loss_fn = @tf.losses.sparse_softmax_cross_entropy build_model_fn.optimize_loss_fn = @optimize_loss optimize_loss.optimizer_cls = @tf.train.MomentumOptimizer MomentumOptimizer.momentum = 0.9 optimize_loss.learning_rate = 0.01
Note that it is straightforward to switch between different network functions, optimizers, datasets, loss functions, etc. via different config files.
Additional features described in more detail in the user guide include:
At a high level, we recommend using the minimal feature set required to achieve your project's desired degree of configurability. Many projects may only require the features outlined in sections 2 or 3 above. Extreme configurability comes at some cost to understandability, and the tradeoff should be carefully evaluated for a given project.
Gin is still in alpha development and some corner-case behaviors may be changed in backwards-incompatible ways. We recommend the following best practices:
@name()), especially when combined with macros (where the fact that the value is not cached may be surprising to new users).
scope1/scope2/function_name). While supported there is some ongoing debate around ordering and behavior.
@name) as a parameter of a scoped function (
some_scope/fn.param), the unscoped reference gets called in the scope of the function it is passed to... but don't rely on this behavior.
In short, use Gin responsibly :)
A quick reference for syntax unique to Gin (which otherwise supports
non-control-flow Python syntax, including literal values and line
continuations). Note that where function and class names are used, these may
include a dotted module name prefix (
||Decorator in Python code that registers a function or class with Gin, wrapping/replacing it with a "configurable" version that respects Gin parameter overrides. A function or class annotated with `@gin.configurable` will have its parameters overridden by any provided configs even when called directly from other Python code. .|
||Decorator in Python code that only registers a function or class with Gin, but does *not* replace it with its "configurable" version. Functions or classes annotated with `@gin.register` will *not* have their parameters overridden by Gin configs when called directly from other Python code. However, any references in config strings or files to these functions (`@some_name` syntax, see below) will apply any provided configuration.|
||Basic syntax of a Gin binding. Once this is parsed, when the
function or class named
||A reference to another function or class named
||An evaluated reference. Instead of supplying the function
or class directly, the result of calling
||A scoped binding. The binding is only active when
||A scoped reference. When this is called, the call will be within
||A macro. This provides a shorthand name for the expression on the right hand side.|
||A reference to the macro