https://github.com/emapco/hydratrainer
🤗 transformers.Trainer wrapper with Hydra integration
https://github.com/emapco/hydratrainer
hydra omegaconf transformers
Last synced: about 1 month ago
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🤗 transformers.Trainer wrapper with Hydra integration
- Host: GitHub
- URL: https://github.com/emapco/hydratrainer
- Owner: emapco
- Created: 2025-03-02T02:44:47.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-03-03T20:11:40.000Z (3 months ago)
- Last Synced: 2025-04-14T20:56:08.515Z (2 months ago)
- Topics: hydra, omegaconf, transformers
- Language: Python
- Homepage:
- Size: 9.77 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Hydra Trainer
A package that wraps Hugging Face's Transformer Trainer with Hydra integration for better configuration management and hyperparameter optimization support.
Checkout my [powerformer repo](https://github.com/emapco/powerformer) for a concrete example.
## Features
- Hydra configuration management
- Optuna hyperparameter optimization integration
- Easy-to-extend base classes for custom datasets and trainers
- Specify TrainingArguments or hyperparameter search parameters within a hydra configuration file
- An example config, `base.yaml`, is provided in this package.
## Installation
```bash
pip install hydra-trainer
```
## Quick Start
1. Create your dataset class by extending `BaseDataset` or use any dataset that extends `datasets.Dataset`:
```python:example.py
from typing import Literal
from omegaconf import DictConfig
from hydra_trainer import BaseDataset
class ExampleDataset(BaseDataset):
def __init__(self, cfg: DictConfig, dataset_key: Literal["train", "eval"]):
super().__init__(cfg)
self.dataset_key = dataset_key
# TODO: implement dataset loading and preprocessing
raise NotImplementedError
def __len__(self):
# TODO: implement this method
raise NotImplementedError
def __getitem__(self, idx):
# TODO: implement this method
raise NotImplementedError
```
2. Create your trainer class by extending `BaseTrainer`:
```python:example.py
from typing import Literal
import optuna
from omegaconf import DictConfig
from hydra_trainer import BaseTrainer
class ExampleTrainer(BaseTrainer[ExampleDataset, DictConfig]):
def model_init_factory(self):
def model_init(trial: optuna.Trial | None = None):
model_cfg = self.get_trial_model_cfg(trial, self.cfg)
# TODO: implement model initialization
raise NotImplementedError
return model_init
def dataset_factory(
self, dataset_cfg: DictConfig, dataset_key: Literal["train", "eval"]
) -> ExampleDataset:
# TODO: implement this method
raise NotImplementedError
```
3. Set up your training script with Hydra:
```python:example.py
import hydra
from omegaconf import DictConfig
@hydra.main(config_path="hydra_trainer", config_name="base", version_base=None)
def main(cfg: DictConfig):
trainer = ExampleTrainer(cfg)
trainer.train()
if __name__ == "__main__":
main()
```
## BaseTrainer Key Features
1. **Model Initialization Factory**: Implement `model_init_factory()` to define how your model is created.
2. **Dataset Factory**: Implement `dataset_factory()` to create your training and evaluation datasets
## Configuration
The package uses Hydra for configuration management. Here's the base configuration structure:
```yaml
seed: 42
checkpoint_path: null
resume_from_checkpoint: null
do_hyperoptim: false
early_stopping_patience: 3
model: # model parameters - access them within `model_init_factory` implementation
d_model: 128
n_layers: 12
n_heads: 16
d_ff: 512
trainer: # transformers.TrainingArguments
num_train_epochs: 3
eval_strategy: steps
eval_steps: 50
logging_steps: 5
output_dir: training_output
per_device_train_batch_size: 2
per_device_eval_batch_size: 4096
learning_rate: 5e-3
weight_decay: 0.0
fp16: true
hyperopt:
n_trials: 128
patience: 2
persistence: true # set to false to use in memory storage instead of db storage
load_if_exists: true
storage_url: postgresql://postgres:[email protected]:5432/postgres
storage_heartbeat_interval: 15
storage_engine_kwargs:
pool_size: 5
connect_args:
keepalives: 1
hp_space:
training:
- name: learning_rate # TrainingArguments attribute name
type: float
low: 5e-5
high: 5e-3
step: 1e-5
log: true
model:
- name: d_model # model parameters
type: int
low: 128
high: 512
step: 128
log: true
```
## Hyperparameter Optimization
Enable hyperparameter optimization by setting `do_hyperoptim: true` in your config. The package uses Optuna for hyperparameter optimization with support for:
- Integer parameters
- Float parameters
- Categorical parameters
- Persistent storage with a relational database
- Early stopping with patient pruning