Start training models (including ViTs) with NetsPresso Trainer, compress and deploy your model with NetsPresso!
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Table of contents
Installation (Stable)
Prerequisites
- Python
>=3.10 - PyTorch
>=2.0.1
Install with pypi
pip install netspresso_trainer
Install with GitHub
pip install git+https://github.com/Nota-NetsPresso/netspresso-trainer.git@master
To install with editable mode,
git clone -b master https://github.com/Nota-NetsPresso/netspresso-trainer.git
pip install -e netspresso-trainer
Set-up with docker
Please clone this repository and refer to Dockerfile and docker-compose-example.yml.
For docker users, we provide more detailed guide in our Docs.
Getting started
Write your training script in train.py like:
from netspresso_trainer import train_cli
if __name__ == '__main__':
logging_dir = train_cli()
print(f"Training results are saved at: {logging_dir}")
Then, train your model with your own configuraiton:
python train.py\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Or you can start NetsPresso Trainer by just executing console script which has same feature.
netspresso-train\
--data config/data/huggingface/beans.yaml\
--augmentation config/augmentation/classification.yaml\
--model config/model/resnet/resnet50-classification.yaml\
--training config/training.yaml\
--logging config/logging.yaml\
--environment config/environment.yaml
Please refer to scripts/example_train.sh.
NetsPresso Trainer is compatible with NetsPresso service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our colab tutorial.
Dataset preparation (Local)
NetsPresso Trainer is designed to accommodate a variety of tasks, each requiring different dataset formats. You can find the specific dataset formats for each task in our documentation.
If you are interested in utilizing open datasets, you can use them by following the instructions.
Image classification
Semantic segmentation
Object detection
Pose estimation
Dataset preparation (Huggingface)
NetsPresso Trainer is also compatible with huggingface dataset. To use datasets of huggingface, please check instructions in our documentations. This enables to utilize a wide range of pre-built datasets which are beneficial for various training scenarios.
Pretrained weights
Please refer to our official documentation for pretrained weights supported by NetsPresso Trainer.
Tensorboard
We provide basic tensorboard to track your training status. Run the tensorboard with the following command:
tensorboard --logdir ./outputs --port 50001 --bind_all
where PORT for tensorboard is 50001.
Note that the default directory of saving result will be ./outputs directory.
2 maintainers
Metadata
2 maintainers
Release files for netspresso-trainer 1.4.1
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Total release size: 480.5 kB
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