Robust mlflow requests

Requires !57 (merged)

Closes #28 (closed)

dan/ocr/document/train.py

@@ -24,7 +24,7 @@ try:
 
     MLFLOW = True
     logger.info("MLflow Logging available.")
-    from dan.mlflow import start_mlflow_run
+    from dan.mlflow import start_mlflow_run, make_mlflow_request
 except ImportError:
     MLFLOW = False
 
@@ -70,17 +70,17 @@ def get_config():
     """
     Retrieve model configuration
     """
-    dataset_name = "esposalles"
-    dataset_level = "page"
+    dataset_name = "synist"
+    dataset_level = "manual_text_lines"
     dataset_variant = ""
-    dataset_path = "/home/training_data/ATR_paragraph/Esposalles"
+    dataset_path = "."
     params = {
         "mlflow": {
             "dataset_name": dataset_name,
             "run_name": "Test log DAN",
             "s3_endpoint_url": "",
             "tracking_uri": "",
-            "experiment_id": "9",
+            "experiment_id": "0",
             "aws_access_key_id": "",
             "aws_secret_access_key": "",
         },
@@ -287,18 +287,25 @@ def run():
             / "labels.json"
         )
         with start_mlflow_run(config["mlflow"]) as run:
-            logger.info(f"Set tags to MLflow on {config['mlflow']['run_name']}")
-            mlflow.set_tags({"Dataset": config["mlflow"]["dataset_name"]})
+            logger.info(f"Started MLflow run with ID ({run.info.run_id})")
+            
+            make_mlflow_request(
+                mlflow_method=mlflow.set_tags,
+                tags={"Dataset": dataset_name}
+            )
 
             # Get the labels json file
             with open(labels_path) as json_file:
                 labels_artifact = json.load(json_file)
 
             # Log MLflow artifacts
-            mlflow.log_dict(config_artifact, "config.json")
-            mlflow.log_dict(labels_artifact, "labels.json")
-
-            logger.info(f"Started MLflow run with ID ({run.info.run_id})")
+            for artifact, filename in [(config_artifact, "config.json"), (labels_artifact, "labels.json")]:
+                make_mlflow_request(
+                    mlflow_method=mlflow.log_dict,
+                    dictionary=artifact,
+                    artifact_file=filename,
+                )
+            
             if (
                 config["training_params"]["use_ddp"]
                 and not config["training_params"]["force_cpu"]