v0.7.2 - All your batch are belong to us - Micro Update

Important changes

Fixed bug in Model.set_mom which resulted in momentum never being set (affects e.g. OneCycle and CyclicalMom)
Model.fit now shuffles the fold indices for training folds prior to each epoch rather than once per training; removes the periodicity in training loss which was occasionally apparent.
Bugs found in OneCycle:
- When training multiple models, the initial LR for subsequent models was the end LR of the previous model (list in partial was being mutated)
- The model did not stop training at end of cycle
- Momentum was never altered in the optimiser

Mish activation function
Model.fit_params.val_requires_grad to control whether to compute validation epoch with gradient, default zero, built some losses might require it in the future
ParameterisedPrediction now stores copies of values for parametrised features in case they change, or need to be changed locally during prediction.
freeze_layers and unfreeze_layers methods for Model
PivotTraining callback implementing Learning to Pivot Louppe, Kagan, & Cranmer, 2016
- New example reimplementing paper's jets example
TargReplace callback for replacing target data in BatchYielder during training
Support for loss functions being fastcore partialler objects
train_models now has arguments to:
- Exclude specific fold indices from training and validation
- Train models on unique folds, e.g. when training 5 models on a file with 10 folds, each model would be trained on their own unique pair of folds
Added discussion of core concepts in LUMIN to the docs

Cases in which a NaN in the metric during training could spoil plotting and SaveBest
Bug in Model.set_mom which resulted in momentum never being set (affects e.g. OneCycle and CyclicalMom)
Bug in MetricLogger.get_results where tracking metrics could be spoilt by NaN values
Bug in train when not passing any metrics
Bug in FoldYielder when loading output pipe from Path
Bugs found in OneCycle:
- When training multiple models, the initial LR for subsequent models was the end LR of the previous model (list in partial was being mutated)
- The model did not stop training at end of cycle
- Momentum was never altered in the optimiser

Model.fit now shuffles the fold indices for training folds prior to each epoch rather than once per training; removes the periodicity in training loss which was occasionally apparent.
Validation and prediction forwards passes now performed without gradient tracking to save memory and time
MetricLogger now records loss values on batch end rather than on forwards end
on_batch_end now always called regardless of model state