This is our gpt2 model for dtu's mlops course exam project, it needs at least a single 8 * A100 nodes for pre-training gpt2-small(124M) on open source dataset OpenWebText for 4 days, so we choose to finetune gpt2-xl(1.5B) version on Shaespeare documents obased on our access to a server with 6 A100 GPUs.
The directory structure of the project looks like this:
├── Makefile <- Makefile with convenience commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- Documentation folder
│ │
│ ├── index.md <- Homepage for your documentation
│ │
│ ├── mkdocs.yml <- Configuration file for mkdocs
│ │
│ └── source/ <- Source directory for documentation files
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
│
├── pyproject.toml <- Project configuration file
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment
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├── requirements_dev.txt <- The requirements file for reproducing the analysis environment
│
├── tests <- Test files
│
├── gpt2 <- Source code for use in this project.
│ │
│ ├── __init__.py <- Makes folder a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ ├── __init__.py
│ │ └── make_dataset.py
│ │
│ ├── models <- model implementations, training script and prediction script
│ │ ├── __init__.py
│ │ ├── model.py
│ │
│ ├── visualization <- Scripts to create exploratory and results oriented visualizations
│ │ ├── __init__.py
│ │ └── visualize.py
│ ├── train_model.py <- script for training the model
│ └── predict_model.py <- script for predicting from a model
│
└── LICENSE <- Open-source license if one is chosenCreated using mlops_template, a cookiecutter template for getting started with Machine Learning Operations (MLOps).