| # Reproducibility Guide | |
| ## Overview | |
| This part of repo contains the implementation and experiments. This guide will help you reproduce the results using Docker or manual installation. | |
| --- | |
| ## Docker Setup (Recommended) | |
| ### 1. Build Docker Image | |
| ```bash | |
| docker build -t yambda-image . | |
| ``` | |
| ### 2. Run Container with GPU Support | |
| ```bash | |
| docker run --gpus all \ | |
| --runtime=nvidia \ | |
| -it \ | |
| -v </absolute/path/to/local/data>:/yambda/data \ | |
| yambda-image | |
| ``` | |
| --- | |
| ## Data Organization | |
| Create following structure in mounted data directory: | |
| ```bash | |
| data/ | |
| βββ flat/ | |
| β βββ 50m/ | |
| β βββ likes.parquet | |
| β βββ listens.parquet | |
| β βββ ... | |
| βββ sequential/ | |
| βββ 50m/ | |
| βββ likes.parquet | |
| βββ listens.parquet | |
| βββ ... | |
| ``` | |
| Note: | |
| Sequential data is only needed for sasrec. You can build it from flat using scripts/transform2sequential.py or download | |
| --- | |
| ## Running Experiments | |
| ### General Usage | |
| ```bash | |
| # For example random_rec | |
| cd models/random_rec/ | |
| # Show help for main script | |
| python main.py --help | |
| # Basic execution | |
| python main.py | |
| ``` | |
| ### Specific Methods | |
| #### BPR/ALS | |
| ```bash | |
| cd models/bpr_als | |
| python main.py --model bpr | |
| python main.py --model als | |
| ``` | |
| #### SASRec | |
| ```bash | |
| cd models/sasrec | |
| # Training | |
| python train.py --exp_name exp1 | |
| # Evaluation | |
| python eval.py --exp_name exp1 | |
| ``` | |
| --- | |
| ## Manual Installation (Not Recommedned) | |
| ### 1. Install Core Dependencies | |
| ```bash | |
| pip install torch torchvision torchaudio | |
| ``` | |
| ### 2. Install Implicit (CUDA 11.8 required) | |
| Implicit works only with cuda<12. See reasons [here](https://github.com/NVIDIA/nvidia-docker/issues/700#issuecomment-381073278) | |
| ```bash | |
| CUDACXX=/usr/local/cuda-11.8/bin/nvcc \ | |
| pip install implicit | |
| ``` | |
| ### 3. Install SANSA | |
| ```bash | |
| sudo apt-get install libsuitesparse-dev | |
| git clone https://github.com/glami/sansa.git | |
| cd sansa && \ | |
| SUITESPARSE_INCLUDE_DIR=/usr/include/suitesparse \ | |
| SUITESPARSE_LIBRARY_DIR=/usr/lib \ | |
| pip install . | |
| ``` | |
| ### 4. Install Project Package | |
| ```bash | |
| pip install . | |
| ``` | |