make mlflow optional

requirements.txt

@@ -21,7 +21,6 @@ hf_transfer
 colorama
 numba
 numpy>=1.24.4
-mlflow
 # qlora things
 evaluate==0.4.1
 scipy

setup.py

@@ -82,5 +82,8 @@ def parse_requirements():
         "auto-gptq": [
             "auto-gptq==0.5.1",
         ],
+        "mlflow": [
+            "mlflow",
+        ],
     },
 )

src/axolotl/core/trainer_builder.py

@@ -5,6 +5,7 @@
 
 import abc
 import importlib
+import importlib.util
 import logging
 import math
 import sys
@@ -34,7 +35,6 @@
     EvalFirstStepCallback,
     GPUStatsCallback,
     LossWatchDogCallback,
-    SaveAxolotlConfigtoMlflowCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
     bench_eval_callback_factory,
@@ -62,6 +62,10 @@
 LOG = logging.getLogger("axolotl.core.trainer_builder")
 
 
+def is_mlflow_available():
+    return importlib.util.find_spec("mlflow") is not None
+
+
 def _sanitize_kwargs_for_tagging(tag_names, kwargs=None):
     if isinstance(tag_names, str):
         tag_names = [tag_names]
@@ -648,7 +652,11 @@ def get_callbacks(self):
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
-        if self.cfg.use_mlflow:
+        if self.cfg.use_mlflow and is_mlflow_available():
+            from axolotl.utils.callbacks.mlflow_ import (
+                SaveAxolotlConfigtoMlflowCallback,
+            )
+
             callbacks.append(
                 SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
             )

src/axolotl/utils/callbacks.py → src/axolotl/utils/callbacks/__init__.py

@@ -9,7 +9,6 @@
 from typing import TYPE_CHECKING, Dict, List
 
 import evaluate
-import mlflow
 import numpy as np
 import pandas as pd
 import torch
@@ -42,8 +41,8 @@
 if TYPE_CHECKING:
     from axolotl.core.trainer_builder import AxolotlTrainingArguments
 
-LOG = logging.getLogger("axolotl.callbacks")
 IGNORE_INDEX = -100
+LOG = logging.getLogger("axolotl.callbacks")
 
 
 class EvalFirstStepCallback(
@@ -756,31 +755,3 @@ 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

src/axolotl/utils/callbacks/mlflow_.py

@@ -0,0 +1,44 @@
+"""MLFlow module for trainer callbacks"""
+import logging
+from shutil import copyfile
+from tempfile import NamedTemporaryFile
+from typing import TYPE_CHECKING
+
+import mlflow
+from transformers import TrainerCallback, TrainerControl, TrainerState
+
+from axolotl.utils.distributed import is_main_process
+
+if TYPE_CHECKING:
+    from axolotl.core.trainer_builder import AxolotlTrainingArguments
+
+LOG = logging.getLogger("axolotl.callbacks")
+
+
+class SaveAxolotlConfigtoMlflowCallback(TrainerCallback):
+    # pylint: disable=duplicate-code
+    """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