Metadata-Version: 2.1
Name: netspresso-trainer
Version: 0.1.2
Summary: NetsPresso Python Package
Home-page: https://github.com/Nota-NetsPresso/netspresso-trainer
Author: NetsPresso
Author-email: netspresso@nota.ai
License: UNKNOWN
Description: <div align="center">
            <img src="./assets/netspresso_trainer_header_tmp.png" width="800"/>
        </div>
        </br>
        
        <center style="white-space: pre-line">
        Start training models (including ViTs) with <b>NetsPresso Trainer</b>,
        compress and deploy your model with <b>NetsPresso</b>!
        </center>
        </br>
        
        <div align="center">
        <p align="center">
          <a href="https://py.netspresso.ai/">Website</a> •
          <a href="#getting-started">Getting Started</a> •
          <a href="https://github.com/Nota-NetsPresso/netspresso-trainer/issues">Issues</a> •
          <a href="https://nota-netspresso.github.io/netspresso-trainer">Docs</a>
        </p>
        </div>
        
        _____
        
        
        ## Table of contents
        
        <!-- toc -->
        
        - [Getting started](#getting-started)
        - [Installation](#installation)
        
        <!-- tocstop -->
        
        ## Getting started
        
        Write your training script in `train.py` like:
        
        ```python
        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:
        
        ```bash
        python train.py\
          --data config/data/beans.yaml\
          --augmentation config/augmentation/classification.yaml\
          --model config/model/resnet/resnet50-classification.yaml\
          --training config/training/classification.yaml\
          --logging config/logging.yaml\
          --environment config/environment.yaml
        ```
        
        Or you can start NetsPresso Trainer by just executing console script which has same feature.
        
        ```bash
        netspresso-train\
          --data config/data/beans.yaml\
          --augmentation config/augmentation/classification.yaml\
          --model config/model/resnet/resnet50-classification.yaml\
          --training config/training/classification.yaml\
          --logging config/logging.yaml\
          --environment config/environment.yaml
        ```
        
        Please refer to [`scripts/example_train.sh`](./scripts/example_train.sh).
        
        NetsPresso Trainer is compatible with [NetsPresso](https://netspresso.ai/) service. We provide NetsPresso Trainer tutorial that contains whole procedure from model train to model compression and benchmark. Please refer to our [colab tutorial](https://colab.research.google.com/drive/1RBKMCPEa4x-4X31zqzTS8WgQI9TQt3e-?usp=sharing).
        
        ## Installation
        
        ### Prerequisites
        
        - Python `3.8` | `3.9` | `3.10`
        - PyTorch `1.13.0` (recommended) (compatible with: `1.11.x` - `1.13.x`)
        
        ### Install with pypi (stable)
        
        ```bash
        pip install netspresso_trainer
        ```
        
        ### Install with GitHub
        
        ```bash
        pip install git+https://github.com:Nota-NetsPresso/netspresso-trainer.git@stable
        ```
        
        To install with editable mode,
        
        ```bash
        git clone 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`](./Dockerfile) and [`docker-compose-example.yml`](./docker-compose-example.yml).  
        For docker users, we provide more detailed guide in our [Docs](https://nota-netspresso.github.io/netspresso-trainer).
        
        ## Tensorboard
        
        We provide basic tensorboard to track your training status. Run the tensorboard with the following command: 
        
        ```bash
        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.
        
        
        ## Pretrained weights
        
        Please refer to our [official documentation](https://nota-netspresso.github.io/netspresso-trainer/) for pretrained weights supported by NetsPresso Trainer.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
