Fix MLflowCallback end_run() and add support for tags and nested runs (#17130)

* ensure mlflow.end_run() is executed at end of training when mlflow.start_run() was executed by the callback

* add debug msg

* add support for MLFLOW_TAGS, MLFLOW_RUN_ID, and MLFLOW_NESTED_RUN

* update to support python 3.6+

* Validate env variables using ENV_VARS_TRUE_VALUES

* Empty-Commit

src/transformers/integrations.py

@@ -16,6 +16,7 @@ Integrations with other Python libraries.
 """
 import functools
 import importlib.util
+import json
 import numbers
 import os
 import sys
@@ -772,6 +773,7 @@ class MLflowCallback(TrainerCallback):
         self._MAX_PARAMS_TAGS_PER_BATCH = mlflow.utils.validation.MAX_PARAMS_TAGS_PER_BATCH
 
         self._initialized = False
+        self._auto_end_run = False
         self._log_artifacts = False
         self._ml_flow = mlflow
 
@@ -790,18 +792,32 @@ class MLflowCallback(TrainerCallback):
                 point to the "Default" experiment in MLflow. Otherwise, it is a case sensitive name of the experiment
                 to be activated. If an experiment with this name does not exist, a new experiment with this name is
                 created.
+            MLFLOW_TAGS (`str`, *optional*):
+                A string dump of a dictionary of key/value pair to be added to the MLflow run as tags. Example:
+                os.environ['MLFLOW_TAGS']='{"release.candidate": "RC1", "release.version": "2.2.0"}'
+            MLFLOW_NESTED_RUN (`str`, *optional*):
+                Whether to use MLflow nested runs. If set to `True` or *1*, will create a nested run inside the current
+                run.
+            MLFLOW_RUN_ID (`str`, *optional*):
+                Allow to reattach to an existing run which can be usefull when resuming training from a checkpoint.
+                When MLFLOW_RUN_ID environment variable is set, start_run attempts to resume a run with the specified
+                run ID and other parameters are ignored.
         """
-        log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper()
-        if log_artifacts in {"TRUE", "1"}:
-            self._log_artifacts = True
-        experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", None)
-        logger.debug(f"MLFLOW experiment_name={experiment_name}, run_name={args.run_name}")
+        self._log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() in ENV_VARS_TRUE_VALUES
+        self._nested_run = os.getenv("MLFLOW_NESTED_RUN", "FALSE").upper() in ENV_VARS_TRUE_VALUES
+        self._experiment_name = os.getenv("MLFLOW_EXPERIMENT_NAME", None)
+        self._run_id = os.getenv("MLFLOW_RUN_ID", None)
+        logger.debug(
+            f"MLflow experiment_name={self._experiment_name}, run_name={args.run_name}, nested={self._nested_run}, tags={self._nested_run}"
+        )
         if state.is_world_process_zero:
-            if self._ml_flow.active_run() is None:
-                if experiment_name:
+            if self._ml_flow.active_run() is None or self._nested_run or self._run_id:
+                if self._experiment_name:
                     # Use of set_experiment() ensure that Experiment is created if not exists
-                    self._ml_flow.set_experiment(experiment_name)
-                self._ml_flow.start_run(run_name=args.run_name)
+                    self._ml_flow.set_experiment(self._experiment_name)
+                self._ml_flow.start_run(run_name=args.run_name, nested=self._nested_run)
+                logger.debug(f"MLflow run started with run_id={self._ml_flow.active_run().info.run_id}")
+                self._auto_end_run = True
             combined_dict = args.to_dict()
             if hasattr(model, "config") and model.config is not None:
                 model_config = model.config.to_dict()
@@ -821,6 +837,10 @@ class MLflowCallback(TrainerCallback):
             combined_dict_items = list(combined_dict.items())
             for i in range(0, len(combined_dict_items), self._MAX_PARAMS_TAGS_PER_BATCH):
                 self._ml_flow.log_params(dict(combined_dict_items[i : i + self._MAX_PARAMS_TAGS_PER_BATCH]))
+            mlflow_tags = os.getenv("MLFLOW_TAGS", None)
+            if mlflow_tags:
+                mlflow_tags = json.loads(mlflow_tags)
+                self._ml_flow.set_tags(mlflow_tags)
         self._initialized = True
 
     def on_train_begin(self, args, state, control, model=None, **kwargs):
@@ -849,11 +869,13 @@ class MLflowCallback(TrainerCallback):
             if self._log_artifacts:
                 logger.info("Logging artifacts. This may take time.")
                 self._ml_flow.log_artifacts(args.output_dir)
+            if self._auto_end_run and self._ml_flow.active_run():
+                self._ml_flow.end_run()
 
     def __del__(self):
         # if the previous run is not terminated correctly, the fluent API will
         # not let you start a new run before the previous one is killed
-        if self._ml_flow.active_run() is not None:
+        if self._auto_end_run and self._ml_flow.active_run() is not None:
             self._ml_flow.end_run()