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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

unreleased

Added

  • Added tests for loading dataset, creating graph, and training model based on reduced MEPS dataset stored on AWS S3, along with automatic running of tests on push/PR to GitHub, including push to main branch. Added caching of test data to speed up running tests. #38 #55 @SimonKamuk

  • Replaced constants.py with data_config.yaml for data configuration management #31 @sadamov

  • new metrics (nll and crps_gauss) and metrics submodule, stddiv output option c14b6b4 @joeloskarsson

  • ability to "watch" metrics and log c14b6b4 @joeloskarsson

  • pre-commit setup for linting and formatting #6, #8 @sadamov, @joeloskarsson

  • added github pull-request template to ease contribution and review process #53, @leifdenby

  • ci/cd setup for running both CPU and GPU-based testing both with pdm and pip based installs #37, @khintz, @leifdenby

Changed

Optional multi-core/GPU support for statistics calculation in create_parameter_weights.py #22 @sadamov

  • Robust restoration of optimizer and scheduler using ckpt_path #17 @sadamov

  • Updated scripts and modules to use data_config.yaml instead of constants.py #31 @sadamov

  • Added new flags in train_model.py for configuration previously in constants.py #31 @sadamov

  • moved batch-static features ("water cover") into forcing component return by WeatherDataset #13 @joeloskarsson

  • change validation metric from mae to rmse c14b6b4 @joeloskarsson

  • change RMSE definition to compute sqrt after all averaging #10 @joeloskarsson

Removed

  • WeatherDataset(torch.Dataset) no longer returns "batch-static" component of training item (only prev_state, target_state and forcing), the batch static features are instead included in forcing #13 @joeloskarsson

Maintenance

  • simplify pre-commit setup by 1) reducing linting to only cover static analysis excluding imports from external dependencies (this will be handled in build/test cicd action introduced later), 2) pinning versions of linting tools in pre-commit config (and remove from requirements.txt) and 3) using github action to run pre-commit. #29 @leifdenby

  • change copyright formulation in license to encompass all contributors #47 @joeloskarsson

  • Fix incorrect ordering of x- and y-dimensions in comments describing tensor shapes for MEPS data #52 @joeloskarsson

  • Cap numpy version to < 2.0.0 (this cap was removed in #37, see below) #68 @joeloskarsson

  • Remove numpy < 2.0.0 version cap #37 @leifdenby

  • turn neural-lam into a python package by moving all *.py-files into the neural_lam/ source directory and updating imports accordingly. This means all cli functions are now invoke through the package name, e.g. python -m neural_lam.train_model instead of python train_model.py (and can be done anywhere once the package has been installed). #32, @leifdenby

  • move from requirements.txt to pyproject.toml for defining package dependencies. #37, @leifdenby

v0.1.0

First tagged release of neural-lam, matching Oskarsson et al 2023 publication (https://arxiv.org/abs/2309.17370)