# Espresso Config
A struct config parser that you can set up in the time it
takes to make an espresso. To install, run
```bash
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](https://hydra.cc), [ML Collections](https://github.com/google/ml_collections), so why another one? Espresso Config was designed to meet the following requirements:
1. Support structured configs (*i.e.*, define configurations with classes)
2. Allow nested classes in configuration
3. Functions
## Motivating Example
Imagine you want to run the following experiment:
```yaml
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:
```python
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:
```yaml
backbone: t5-large
model:
transformer@seq2seq: {}
tokenizer@tok: {}
metrics:
rouge@rouge: {}
```
Voila! To load the config, run:
```python
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`.
