Casanovo v4.0.0

4.0.0 - 2023-12-22

Added

• Checkpoints include model parameters, allowing for mismatches with the provided configuration file.
• accelerator parameter controls the accelerator (CPU, GPU, etc) that is used.
• devices parameter controls the number of accelerators used.
• val_check_interval parameter controls the frequency of both validation epochs and model checkpointing during training.
• train_label_smoothing parameter controls the amount of label smoothing applied when calculating the training loss.

Changed

• The CLI has been overhauled to use subcommands.
• Upgraded to Lightning >=2.0.
• Checkpointing is configured to save the top-k models instead of all.
• Log steps rather than epochs as units of progress during training.
• Validation performance metrics are logged (and added to tensorboard) at the validation epoch, and training loss is logged at the end of training epoch, i.e. training and validation metrics are logged asynchronously.
• Irrelevant warning messages on the console output and in the log file are no longer shown.
• Nicely format logged warnings.
• every_n_train_steps has been renamed to val_check_interval in accordance to the corresponding Pytorch Lightning parameter.
• Training batches are randomly shuffled.
• Upgraded to Torch >=2.1.

Removed

• Remove config option for a custom Pytorch Lightning logger.
• Remove superfluous custom_encoder config option.

Fixed

• Casanovo runs on CPU and can pass all tests.
• Correctly refer to input peak files by their full file path.
• Specifying custom residues to retrain Casanovo is now possible.
• Upgrade to depthcharge v0.2.3 to fix sinusoidal encoding and for the PeptideTransformerDecoder hotfix.
• Correctly report amino acid precision and recall during validation.