Capture wattile version Fixes #322 (#324)

* Captures the Wattile version as a prop on the Wattile ob
Logs the Wattile version to the output.out artifact post training

* Adds metadata.json file to model artifacts

* Fixes formatting errors

wattile/__init__.py

@@ -0,0 +1,10 @@
+from os import path
+
+import toml
+
+b_path = path.dirname(__file__)
+proj_path = path.abspath(path.join(b_path, "..", "pyproject.toml"))
+
+with open(proj_path, "r") as f:
+    config = toml.load(f)
+    version = config["tool"]["poetry"]["version"]

wattile/entry_point.py

@@ -6,6 +6,7 @@
 import pandas as pd
 
 import wattile.data_processing as bp
+from wattile import version as wattile_version
 from wattile.data_reading import read_dataset_from_file
 from wattile.models import ModelFactory
 
@@ -35,6 +36,7 @@ def init_logging(local_results_dir):
     logger.addHandler(hdlr)
     logger.setLevel(logging.INFO)
     logger.info("PID: {}".format(PID))
+    logger.info("Trained with Wattile version: {}".format(wattile_version))
 
 
 def create_input_dataframe(configs):

wattile/models/base_model.py

@@ -8,6 +8,7 @@
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
+from wattile import version as wattile_version
 from wattile.error import ConfigsError
 from wattile.util import factors
 from wattile.visualization import timeseries_comparison
@@ -126,6 +127,13 @@ def apply_normalization(self, data: pd.DataFrame) -> pd.DataFrame:
 
         return data
 
+    def write_metadata(self):
+        """Write the metadata to a json file"""
+        path = os.path.join(self.file_prefix, "metadata.json")
+        metadata = {"wattile_version": wattile_version}
+        with open(path, "w") as fp:
+            json.dump(metadata, fp, indent=1)
+
     def main(self, train_df, val_df):
         """
         Main executable for prepping data for input to RNN model.
@@ -161,6 +169,7 @@ def train(self, train_df: pd.DataFrame, val_df: pd.DataFrame) -> None:
         val_loader = self.to_data_loader(val_data, val_batch_size, shuffle=True)
 
         self.run_training(train_loader, val_loader, val_df)
+        self.write_metadata()
 
         # Create visualization
         if self.configs["data_output"]["plot_comparison"]: