v0.4.0

This release introduces a number of improvements to logging, multi-node training and variable rescaling, without making any major changes to the neural-lam structure.

Added

• Add support for MLFlow logging and metrics tracking. #77
  @khintz

• Add support for multi-node training.
  #103 @SimonKamuk @sadamov

• Add option to clamp output prediction using limits specified in config file #92 @SimonKamuk

• Add publication of releases to pypi.org. #71 @leifdenby, @observingClouds

Fixed

• Only print on rank 0 to avoid duplicates of all print statements.
  #103 @SimonKamuk @sadamov

• Fix MLFlow exception import introduced in #77.
  #111
  @observingClouds

• Fix duplicate tensor copy to CPU #106 @observingClouds

• Fix bug where the inverse_softplus used in clamping caused nans in the gradients #123 @SimonKamuk

• Add standardization to state diff stats from mdp datastore #122 @SimonKamuk

• Set ci/cd badges to refer to the new test matrix #130 @SimonKamuk

• use correct split of data with the --eval val or --eval test cli arguments #139 @SimonKamuk

• Fix step length calculation when dt >= 24h #141 @deinal

Maintenance

• update ci/cd testing to use cuda 12.8 #140 @SimonKamuk

• update ci/cd testing to use pre-commit v3.0.1 #140 @SimonKamuk

• update AWS GPU ci/cd to use ami with larger (200GB) root volume and ensure
  nvme drive is used for pip venvn
  #126, @leifdenby

• update ci/cd testing setup to install torch version compatible with neural-lam
  dependencies #115, @leifdenby

• switch to new npyfiles MEPS and mdp DANRA test datasets which are coincident
  in time and space (on cropped ~100x100 grid-point domain)
  #110, @leifdenby

• use dynamic versioning based on git tags and commit hashes
  #118, @observingClouds

• add detect_anomaly=True to pl.Trainer in test_training.py #124, @SimonKamuk