Note: Benchmarks in their original form can be found
at the original github repo.
Instructions to run
Docker
All of the below commands should be run in a Docker container built using the Dockerfile in the repo, with the data and repo being exposed as volumes in the container.
To build:
docker build -t benchmarks_img .
To run an interactive shell:
docker run -it --shm-size=2G --gpus all -v /path/to/neurips2023-benchmarks:/neurips2023-benchmarks -v /path/to/datasets/:/data benchmarks_img
Reanalysis Task
Every command should be run in the reanalysis folder. The path to this folder and to the data should be provided in the config.py file.
Create buckets
First, you have to split and save the dataset into 3 buckets according to the type of splitting refered in the config.py file ('standard' for standard splitting between before 2005 / between 2005 and 2015 / after 2015, 'same_size' for the same splitting but with a equal number of sequences per bucket).
This will create a folder (named 'save' or 'save_same') with 6 .txt file containing the id of the sequences used for training and testing in each bucket.
Train
You can now train for a number of runs (called version in the logs) and epochs specified in the config.py file.
A tensorboard log while be created for each run with each bucket in the tb_logs.
Test
After specifing a list of versions in the config.py file, you'll be able to test the model.
The accuracy (RMSE in hPa) will be displayed on the terminal but also written in a log.txt file in the directory reanalysis.
