def compute_memory_usage(config, training_set_metadata) -> int:
update_config_with_metadata(config, training_set_metadata)
lm = LudwigModel.create_model(config)
model_size = lm.get_model_size() # number of parameters in model
batch_size = config[TRAINER][BATCH_SIZE]
return model_size * (BYTES_PER_WEIGHT +
BYTES_OPTIMIZER_PER_WEIGHT) * batch_size
def setup_model_scaffolding(
raw_df,
input_features,
output_features
):
# setup input feature for testing
config = {'input_features': input_features,
'output_features': output_features}
# setup model scaffolding to for testing
model = LudwigModel(config)
training_set, _, _, training_set_metadata = preprocess_for_training(
config,
training_set=raw_df,
skip_save_processed_input=True
)
model.training_set_metadata = training_set_metadata
update_config_with_metadata(
model.config,
training_set_metadata
)
model.model = model.create_model(model.config)
# setup batcher to go through synthetic data
with training_set.initialize_batcher() as batcher:
yield model, batcher
def compute_memory_usage(config, training_set_metadata) -> int:
update_config_with_metadata(config, training_set_metadata)
lm = LudwigModel.create_model(config)
model_tensors = lm.collect_weights()
total_size = 0
batch_size = config[TRAINING][BATCH_SIZE]
for tnsr in model_tensors:
total_size += tnsr[1].numpy().size * batch_size
total_bytes = total_size * 32 # assumes 32-bit precision
return total_bytes