Merge branch 'dev' into log_warnings

CHANGELOG.md

@@ -6,17 +6,6 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 ## [Unreleased]
 
-### Changed
-
-- The CLI has been overhauled to use subcommands.
-- Upgraded to Lightning >=2.0
-- Checkpointing is now configured to save the top-k models instead of all.
-- Nicely format logged warnings.
-
-### Fixed
-
-- Casanovo now runs on CPU and can passes all tests.
-
 ### Added
 
 -  Checkpoints now include model parameters, allowing for mismatches with the provided configuration file.
@@ -26,12 +15,21 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 ### Changed
 
+- The CLI has been overhauled to use subcommands.
+- Upgraded to Lightning >=2.0
+- Checkpointing is now configured to save the top-k models instead of all.
 - We now 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.
+
+### Removed
+
+- Remove config option for a custom Pytorch Lightning logger
 
 ### Fixed
 
+- Casanovo now runs on CPU and can passes all tests.
 - Upgrade to Depthcharge v0.2.0 to fix sinusoidal encoding.
 - Correctly refer to input peak files by their full file path.
 

casanovo/config.yaml

@@ -83,7 +83,7 @@ residues:
   "+43.006-17.027": 25.980265      # Carbamylation and NH3 loss
 # Logging frequency in training steps
 n_log: 1
-# Tensorboard object to keep track of training metrics
+# Tensorboard directory to use for keeping track of training metrics
 tb_summarywriter:
 # Number of warmup iterations for learning rate scheduler
 warmup_iters: 100_000
@@ -103,8 +103,6 @@ predict_batch_size: 1024
 n_beams: 1
 # Number of PSMs for each spectrum (I)
 top_match: 1
-# Object for logging training progress
-logger:
 # Max number of training epochs
 max_epochs: 30
 # Number of validation steps to run before training begins

casanovo/denovo/model_runner.py

@@ -170,14 +170,10 @@ def initialize_trainer(self, train: bool) -> None:
             Determines whether to set the trainer up for model training
             or evaluation / inference.
         """
-        logger = (
-            self.config.logger if self.config.logger is not None else False
-        )
         trainer_cfg = dict(
             accelerator=self.config.accelerator,
             devices=1,
             enable_checkpointing=False,
-            logger=logger,
         )
 
         if train: