dicee.executer

Classes

Execute	A class for Training, Retraining and Evaluation a model.
ContinuousExecute	A subclass of Execute Class for retraining

Module Contents

class dicee.executer.Execute(args, continuous_training=False)

A class for Training, Retraining and Evaluation a model.

1. Loading & Preprocessing & Serializing input data.

2. Training & Validation & Testing

3. Storing all necessary info

args

is_continual_training

trainer = None

trained_model = None

knowledge_graph = None

report

evaluator = None

start_time = None

read_or_load_kg()

read_preprocess_index_serialize_data() → None

Read & Preprocess & Index & Serialize Input Data

1. Read or load the data from disk into memory.

2. Store the statistics of the data.

Parameter

rtype:

None

load_indexed_data() → None

Load the indexed data from disk into memory

Parameter

rtype:

None

save_trained_model() → None

Save a knowledge graph embedding model

1. Send model to eval mode and cpu.

2. Store the memory footprint of the model.

3. Save the model into disk.

4. Update the stats of KG again ?

Parameter

rtype:

None

end(form_of_labelling: str) → dict

End training

1. Store trained model.

2. Report runtimes.

3. Eval model if required.

Parameter

rtype:

A dict containing information about the training and/or evaluation

write_report() → None

Report training related information in a report.json file

start() → dict

Start training

# (1) Loading the Data # (2) Create an evaluator object. # (3) Create a trainer object. # (4) Start the training

Parameter

rtype:

A dict containing information about the training and/or evaluation

class dicee.executer.ContinuousExecute(args)

Bases: Execute

A subclass of Execute Class for retraining

1. Loading & Preprocessing & Serializing input data.

2. Training & Validation & Testing

3. Storing all necessary info

During the continual learning we can only modify * num_epochs * parameter. Trained model stored in the same folder as the seed model for the training. Trained model is noted with the current time.

previous_args

args

continual_start() → dict

Start Continual Training

1. Initialize training.

2. Start continual training.

3. Save trained model.

Parameter

rtype:

A dict containing information about the training and/or evaluation