Releases · mllam/neural-lam

Highlights

This release focuses on setting up the neural-lam repository and codebase to enable collaboration.

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

Clarify routine around requesting reviewer and assignee in PR template
#74
@joeloskarsson
Argument Parser updated to use action="store_true" instead of 0/1 for boolean arguments.
(#72)
@ErikLarssonDev
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

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

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
Add slack and new publication info to readme
#78
@joeloskarsson

This version has been tested with Python 3.9-3.12.

To upgrade to neural-lam v0.2.0:

Update your local version:
- git pull the latest changes from the repository and install locally pip install -e .
- Note: This release is not yet available on PyPI. You will need to install it from the GitHub repository.
Adapt any code relying on constants previously defined in neural_lam/constants.py to instead read them from the new YAML config file.