python环境下Gin包的安装和使用Gin Config

Gin Config

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.

Table of Contents

[TOC]

Basic usage

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.

1. Setup

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 gin

Import additional TensorFlow-specific functionality via the gin.tf module:

import gin.tf

Import additional PyTorch-specific functionality via the gin.torch module:

import gin.torch

2. Configuring default values with Gin (@gin.configurable and "bindings")

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 decorator:

@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 the caller).

Classes can also be marked as configurable, in which case the configuration applies to constructor parameters:

@gin.configurable
class DNN(object):
  # Constructor

最后

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