文件列表:

components/
docker/
notebooks/
.dockerignore
docker_build.sh
docker_run_data_preparation_and_training.sh
dvc.yaml
params.yaml
requirements.txt

# Generative Adversarial Network for Nintendo Entertainment System Music ## About This project uses a Generative Adversarial Network (GAN) to create new 8-bit style music, trained on the [The NES Music Database](https://github.com/chrisdonahue/nesmdb). You can explore generated samples FIXME: [here](https://colab.research.google.com/github/alyona-chur/nesm-gan/blob/main/gan_for_nes_music_results_0.ipyn). The development of this project incorporates some concepts from the '[Udemy End-to-End Machine Learning Course](https://www.udemy.com/course/sustainable-and-scalable-machine-learning-project-development/)', along with other resources. ## Usage Follow these instructions to use the project: 1. **Setting Up the Environment.** - Check CUDA and cuDNN versions in '''docker/gpu.Dockerfile''' if using a GPU. - Build the Docker container by running: ``` ./docker_build.sh -t [cpu|gpu] ``` 2. **Downloading, Preparing Data, and Training Models.** ``` ./docker_run_data_preparation_and_training.sh -t [cpu|gpu] ``` Generated samples are saved to '''./data/samples''' during training. 3. TODO: To generate music... ### Tested Configurations Tested configurations include: - GPU: Ubuntu 20.04 Docker version 20.10.7 Driver Version: 535.171.04 CUDA Version: 12.2 - CPU: Ubuntu 20.04 Docker version 24.0.7

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