Espresso Config
A struct config parser that you can set up in the time it takes to make an espresso. To install, run
pip install espresso-config
Python 3.8 or newer is required.
Why Espresso Config?
There are a million of parsers that can turn a YAML configuration / CLI flags into a configuration file, e.g., Hydra, ML Collections, so why another one? Espresso Config was designed to meet the following requirements:
- Support structured configs (i.e., define configurations with classes)
- Allow nested classes in configuration
- Functions
Motivating Example
Imagine you want to run the following experiment:
backbone: t5-large
model:
metrics:
rouge:
_target_: torchmetrics.functional.text.rouge.rouge_score
tokenizer:
_target_: transformers.AutoTokenizer.from_pretrained
pretrained_model_name_or_path: t5-large
transformer:
_target_: transformers.AutoModelForSeq2SeqLM.from_pretrained
max_sequence_length: 64
pretrained_model_name_or_path: t5-large
Sure, you could parse that yaml file and get a dict.
But (a) working with dictionaries is tedious (b) there's no
typing, and (c) you don't want to have to declare all blocks
each time; it would be good if you could save some commonly used
configurations, such as the parameters for one of transformer
or tokenizer keys.
Espresso Config allows you to solve all off those problems by specifying a struct class as follows:
from espresso_config import (
ConfigNode,
ConfigRegistry,
ConfigParam,
ConfigFlexNode
)
@ConfigRegistry.add
class seq2seq(ConfigNode):
_target_: ConfigParam(str) = 'transformers.AutoModelForSeq2SeqLM.from_pretrained'
@ConfigRegistry.add
class tok(ConfigNode):
_target_: ConfigParam(str) = 'transformers.AutoTokenizer.from_pretrained'
@ConfigRegistry.add
class rouge(ConfigNode):
_target_: ConfigParam(str) = 'torchmetrics.functional.text.rouge.rouge_score'
class ApplicationConfig(ConfigNode):
backbone: ConfigParam(str)
class model(ConfigNode):
class transformer(ConfigNode):
_target_: ConfigParam(str)
pretrained_model_name_or_path: ConfigParam(str) = '${backbone}'
max_sequence_length: ConfigParam(int) = 64
class tokenizer(ConfigNode):
_target_: ConfigParam(str)
pretrained_model_name_or_path: ConfigParam(str) = '${backbone}'
metrics: ConfigParam(ConfigFlexNode) = {}
Then, your YAML configuration can be as simple as:
backbone: t5-large
model:
transformer@seq2seq: {}
tokenizer@tok: {}
metrics:
rouge@rouge: {}
Voila! To load the config, run:
from espresso_config import config_from_file
config = config_from_file(ApplicationConfig, path_to_yaml)
Placeholder Variable
A placeholder variable is a config value that references another
section of the config, e.g. another value or section.
It uses syntax ${path.to.key}.
Registry Reference
A registry reference is a reference to a node config that has been
added to the config registry. It uses syntax @placeholder_name.
1 maintainer
Metadata
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Release files for espresso-config 0.11.1
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Total release size: 60.7 kB
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