Removed workers as argument

src/progpy/data_models/lstm_model.py

@@ -439,8 +439,6 @@ def from_data(cls, inputs, outputs, event_states=None, t_met=None, **kwargs):
  If early stopping is desired. Default is True
  early_stop.cfg (dict):
  Configuration to pass into early stopping callback (if enabled). See keras documentation (https://keras.io/api/callbacks/early_stopping) for options. E.g., {'patience': 5}
- workers (int):
- Number of workers to use when training. One worker indicates no multiprocessing
 
  Returns:
  LSTMStateTransitionModel: Generated Model
@@ -460,7 +458,6 @@ def from_data(cls, inputs, outputs, event_states=None, t_met=None, **kwargs):
  'normalize': True,
  'early_stop': True,
  'early_stop.cfg': {'patience': 3, 'monitor': 'loss'},
- 'workers': 1
  }.copy() # Copy is needed to avoid updating default
 
  params.update(LSTMStateTransitionModel.default_params)
@@ -498,10 +495,6 @@ def from_data(cls, inputs, outputs, event_states=None, t_met=None, **kwargs):
  raise TypeError(f"epochs must be an integer greater than 0, not {type(params['epochs'])}")
  if params['epochs'] < 1:
  raise ValueError(f"epochs must be greater than 0, got {params['epochs']}")
- if not isinstance(params['workers'], int):
- raise TypeError(f"workers must be positive integer, got {type(params['workers'])}")
- if params['workers'] < 1:
- raise ValueError(f"workers must be positive integer, got {params['workers']}")
  if np.isscalar(inputs): # Is scalar (e.g., SimResult)
  inputs = [inputs]
  if np.isscalar(outputs):
@@ -587,9 +580,7 @@ def from_data(cls, inputs, outputs, event_states=None, t_met=None, **kwargs):
  output_data,
  epochs=params['epochs'],
  callbacks=callbacks,
- validation_split=params['validation_split'],
- workers=params['workers'],
- use_multiprocessing=(params['workers'] > 1))
+ validation_split=params['validation_split'])
 
  # Split model into separate models
  n_state_layers = params['layers'] + 1 + (params['dropout'] > 0) + (params['normalize'])