Resume from existing mlflow run

dan/mlflow.py

@@ -91,7 +91,9 @@ def logging_tags_metrics(
 @contextmanager
 def start_mlflow_run(config: dict):
     """
-    Create an MLflow execution context with the parameters contained in the config file
+    Create an MLflow execution context with the parameters contained in the config file.
+
+    Yields the active MLflow run, as well as a boolean saying whether a new one was created.
 
     :param config: dict, the config of the model
     """
@@ -99,10 +101,22 @@ def start_mlflow_run(config: dict):
     # Set needed variables in environment
     setup_environment(config)
 
+    run_name, run_id = config.get("run_name"), config.get("run_id")
+
+    if run_id:
+        logger.info(f"Will resume run ({run_id}).")
+
+        if run_name:
+            logger.warning(
+                "Run_name will be ignored since you specified a run_id to resume from."
+            )
+
     # Set experiment from config
     experiment_id = config.get("experiment_id")
     assert experiment_id, "Missing MLflow experiment ID in the configuration"
 
     # Start run
-    yield mlflow.start_run(run_name=config.get("run_name"), experiment_id=experiment_id)
+    yield mlflow.start_run(
+        run_id=run_id, run_name=run_name, experiment_id=experiment_id
+    ), run_id is None
     mlflow.end_run()

dan/ocr/document/train.py

@@ -24,10 +24,12 @@ try:
 
     MLFLOW = True
     logger.info("MLflow Logging available.")
+
     from dan.mlflow import make_mlflow_request, start_mlflow_run
 except ImportError:
     MLFLOW = False
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -76,8 +78,8 @@ def get_config():
     dataset_path = "."
     params = {
         "mlflow": {
-            "dataset_name": dataset_name,
             "run_name": "Test log DAN",
+            "run_id": None,
             "s3_endpoint_url": "",
             "tracking_uri": "",
             "experiment_id": "0",
@@ -237,7 +239,10 @@ def get_config():
 
 def serialize_config(config):
     """
-    Serialize a dictionary to transform it into json and remove the credentials
+    Make every field of the configuration JSON-Serializable and remove sensitive information.
+
+    - Classes are transformed using their name attribute
+    - Functions are casted to strings
     """
     # Create a copy of the original config without erase it
     serialized_config = deepcopy(config)
@@ -275,6 +280,20 @@ def serialize_config(config):
     serialized_config["training_params"]["nb_gpu"] = str(
         serialized_config["training_params"]["nb_gpu"]
     )
+
+    if (
+        "synthetic_data" in config["dataset_params"]["config"]
+        and config["dataset_params"]["config"]["synthetic_data"]
+    ):
+        # The Probability scheduler is a function and needs to be casted to string
+        serialized_config["dataset_params"]["config"]["synthetic_data"][
+            "proba_scheduler_function"
+        ] = str(
+            serialized_config["dataset_params"]["config"]["synthetic_data"][
+                "proba_scheduler_function"
+            ]
+        )
+
     return serialized_config
 
 
@@ -286,25 +305,25 @@ def run():
     config, dataset_name = get_config()
 
     if MLFLOW and "mlflow" in config:
-        config_artifact = serialize_config(config)
         labels_path = (
-            Path(config_artifact["dataset_params"]["datasets"][dataset_name])
-            / "labels.json"
+            Path(config["dataset_params"]["datasets"][dataset_name]) / "labels.json"
         )
-        with start_mlflow_run(config["mlflow"]) as run:
-            logger.info(f"Started MLflow run with ID ({run.info.run_id})")
+        with start_mlflow_run(config["mlflow"]) as (run, created):
+            if created:
+                logger.info(f"Started MLflow run with ID ({run.info.run_id})")
+            else:
+                logger.info(f"Resumed 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
             for artifact, filename in [
-                (config_artifact, "config.json"),
+                (serialize_config(config), "config.json"),
                 (labels_artifact, "labels.json"),
             ]:
                 make_mlflow_request(
@@ -312,7 +331,6 @@ def run():
                     dictionary=artifact,
                     artifact_file=filename,
                 )
-
             if (
                 config["training_params"]["use_ddp"]
                 and not config["training_params"]["force_cpu"]