Project exploring alignment of frames across modalities
Note, this installation was tested on
- Ubuntu 22.04
- cuda 12.4
- nvidia driver version 550.54.1
Step 1. Clone this repository and navigate to frame-align folder
git clone https://github.com/copenlu/frame-align.git
cd frame-alignThe installation also installs LLAVA model. If you are not using Linux, see instructions for installing LLaVA model here for macOS and Windows.
< TO DO: Update the instructions to avoid this confusion of OS >
Step 2. Install Package
conda create -n frame-align python=3.10 -y
conda activate frame-align
pip install --upgrade pip # enable PEP 660 support
pip install -e .- Install additional packages for training cases
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
Our set up used flash-attn==2.5.7 but it depends on your cuda version to check for dependency issues.
git pull
pip install -e .
# if you see some import errors when you upgrade,
# please try running the command below (without #)
# pip install flash-attn --no-build-isolation --no-cache-dir├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── 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, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience