Add mlflow callback for pushing config to mlflow artifacts (#1125)

* Update callbacks.py

adding callback for mlflow

* Update trainer_builder.py

* clean up

src/axolotl/core/trainer_builder.py

@@ -28,6 +28,7 @@
     EvalFirstStepCallback,
     GPUStatsCallback,
     LossWatchDogCallback,
+    SaveAxolotlConfigtoMlflowCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
@@ -543,6 +544,10 @@ def get_callbacks(self):
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
+        if self.cfg.use_mlflow:
+            callbacks.append(
+                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
+            )
 
         if self.cfg.loss_watchdog_threshold is not None:
             callbacks.append(LossWatchDogCallback(self.cfg))

src/axolotl/utils/callbacks.py

@@ -9,6 +9,7 @@
 from typing import TYPE_CHECKING, Dict, List
 
 import evaluate
+import mlflow
 import numpy as np
 import pandas as pd
 import torch
@@ -575,3 +576,31 @@ def on_train_begin(
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
         return control
+
+
+class SaveAxolotlConfigtoMlflowCallback(TrainerCallback):
+    """Callback to save axolotl config to mlflow"""
+
+    def __init__(self, axolotl_config_path):
+        self.axolotl_config_path = axolotl_config_path
+
+    def on_train_begin(
+        self,
+        args: AxolotlTrainingArguments,  # pylint: disable=unused-argument
+        state: TrainerState,  # pylint: disable=unused-argument
+        control: TrainerControl,
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        if is_main_process():
+            try:
+                with NamedTemporaryFile(
+                    mode="w", delete=False, suffix=".yml", prefix="axolotl_config_"
+                ) as temp_file:
+                    copyfile(self.axolotl_config_path, temp_file.name)
+                    mlflow.log_artifact(temp_file.name, artifact_path="")
+                    LOG.info(
+                        "The Axolotl config has been saved to the MLflow artifacts."
+                    )
+            except (FileNotFoundError, ConnectionError) as err:
+                LOG.warning(f"Error while saving Axolotl config to MLflow: {err}")
+        return control