Add: mlflow for experiment tracking

README.md

@@ -10,7 +10,7 @@ Features:
 - Integrated with xformer, flash attention, rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb
+- Log results and optionally checkpoints to wandb or mlflow
 - And more!
 
 
@@ -694,6 +694,10 @@ wandb_name: # Set the name of your wandb run
 wandb_run_id: # Set the ID of your wandb run
 wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
 
+# mlflow configuration if you're using it
+mlflow_tracking_uri: # URI to mlflow
+mlflow_experiment_name: # Your experiment name
+
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 

requirements.txt

@@ -21,6 +21,7 @@ hf_transfer
 colorama
 numba
 numpy>=1.24.4
+mlflow
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0

src/axolotl/cli/__init__.py

@@ -29,6 +29,7 @@
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
+from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.models import load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.trainer import prepare_optim_env
@@ -289,6 +290,9 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
     normalize_config(cfg)
 
     setup_wandb_env_vars(cfg)
+
+    setup_mlflow_env_vars(cfg)
+
     return cfg
 
 

src/axolotl/core/trainer_builder.py

@@ -738,7 +738,12 @@ def build(self, total_num_steps):
             False if self.cfg.ddp else None
         )
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
-        training_arguments_kwargs["report_to"] = "wandb" if self.cfg.use_wandb else None
+        report_to = None
+        if self.cfg.use_wandb:
+            report_to = "wandb"
+        if self.cfg.use_mlflow:
+            report_to = "mlflow"
+        training_arguments_kwargs["report_to"] = report_to
         training_arguments_kwargs["run_name"] = (
             self.cfg.wandb_name if self.cfg.use_wandb else None
         )

src/axolotl/utils/mlflow_.py

@@ -0,0 +1,18 @@
+"""Module for mlflow utilities"""
+
+import os
+
+from axolotl.utils.dict import DictDefault
+
+
+def setup_mlflow_env_vars(cfg: DictDefault):
+    for key in cfg.keys():
+        if key.startswith("mlflow_"):
+            value = cfg.get(key, "")
+
+            if value and isinstance(value, str) and len(value) > 0:
+                os.environ[key.upper()] = value
+
+    # Enable mlflow if experiment name is present
+    if cfg.mlflow_experiment_name and len(cfg.mlflow_experiment_name) > 0:
+        cfg.use_mlflow = True