From af9265e1225ea206b9d4ceca166cbad435a3f799 Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Fri, 28 Nov 2025 22:29:03 +0000
Subject: [PATCH 01/13] first commit (yet to fully verify)

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 docs/training/logging.md                      |  43 +++++++
 src/megatron/bridge/training/checkpointing.py |  13 ++-
 src/megatron/bridge/training/config.py        |  12 ++
 src/megatron/bridge/training/state.py         | 110 +++++++++++++++++-
 .../bridge/training/utils/mlflow_utils.py     |  69 +++++++++++
 .../bridge/training/utils/train_utils.py      |  62 ++++++++++
 .../llama/conf/llama32_1b_finetune.yaml       |  11 +-
 .../llama/conf/llama32_1b_pretrain.yaml       |   6 +
 8 files changed, 322 insertions(+), 4 deletions(-)
 create mode 100644 src/megatron/bridge/training/utils/mlflow_utils.py

diff --git a/docs/training/logging.md b/docs/training/logging.md
index 82d30d56a5..043cfc4258 100644
--- a/docs/training/logging.md
+++ b/docs/training/logging.md
@@ -154,6 +154,49 @@ The plugin automatically forwards the `WANDB_API_KEY` and by default injects CLI
 This allows seamless integration of W&B logging into your training workflow without manual configuration.
 
 
+### MLFlow
+
+Megatron Bridge can also log metrics and artifacts to MLFlow, following the same pattern as the W&B integration.
+
+#### What Gets Logged
+
+When enabled, MLFlow receives:
+
+- Flattened training configuration as run parameters
+- Scalar metrics (losses, learning rate, batch size, throughput, timers, memory, runtime, norms, energy, etc.)
+- Checkpoint artifacts saved under an experiment-specific artifact path per iteration
+
+#### Enable MLFlow Logging
+
+  1) Install MLFlow (if not already available):
+
+  ```bash
+  pip install mlflow
+  ```
+
+  2) Configure the tracking server (Optional):
+  - Either set `MLFLOW_TRACKING_URI` in the environment, or
+  - Pass an explicit `mlflow_tracking_uri` in the logger config.
+
+  3) Configure logging in your training setup.
+
+  ```python
+  from megatron.bridge.training.config import LoggerConfig
+
+  cfg.logger = LoggerConfig(
+      tensorboard_dir="./runs/tensorboard",       # optional but recommended
+      mlflow_experiment="my_megatron_experiment",
+      mlflow_run_name="llama32_1b_pretrain_run",
+      mlflow_tracking_uri="http://mlflow:5000",  # optional
+      mlflow_tags={                               # optional
+          "project": "llama32",
+          "phase": "pretrain",
+      },
+  )
+  ```
+
+
+
 #### Progress Log
 
 When `logger.log_progress` is enabled, the framework generates a `progress.txt` file in the checkpoint save directory.
diff --git a/src/megatron/bridge/training/checkpointing.py b/src/megatron/bridge/training/checkpointing.py
index 6091924f1b..8f36496e8d 100644
--- a/src/megatron/bridge/training/checkpointing.py
+++ b/src/megatron/bridge/training/checkpointing.py
@@ -59,7 +59,7 @@
 from megatron.bridge.training.state import GlobalState, TrainState
 from megatron.bridge.training.tokenizers.config import TokenizerConfig
 from megatron.bridge.training.tokenizers.tokenizer import MegatronTokenizer
-from megatron.bridge.training.utils import wandb_utils
+from megatron.bridge.training.utils import wandb_utils, mlflow_utils
 from megatron.bridge.training.utils.checkpoint_utils import (
     checkpoint_exists,
     ensure_directory_exists,
@@ -740,11 +740,21 @@ def wandb_finalize_fn() -> None:
                 wandb_writer=state.wandb_logger,
             )
 
+        def mlflow_finalize_fn() -> None:
+            mlflow_utils.on_save_checkpoint_success(
+                checkpoint_name,
+                save_dir,
+                train_state.step,
+                mlflow_logger=state.mlflow_logger,
+            )
+
         if ckpt_cfg.async_save:
             assert async_save_request is not None
             async_save_request.add_finalize_fn(wandb_finalize_fn)
+            async_save_request.add_finalize_fn(mlflow_finalize_fn)
         else:
             wandb_finalize_fn()
+            mlflow_finalize_fn()
 
     if ckpt_cfg.async_save:
         schedule_async_save(state, async_save_request)
@@ -1667,6 +1677,7 @@ def _load_checkpoint_from_path(
 
     if not torch.distributed.is_initialized() or is_last_rank():
         wandb_utils.on_load_checkpoint_success(checkpoint_name, load_dir, state.wandb_logger)
+        mlflow_utils.on_load_checkpoint_success(checkpoint_name, load_dir, state.mlflow_logger)
 
     torch.cuda.empty_cache()
 
diff --git a/src/megatron/bridge/training/config.py b/src/megatron/bridge/training/config.py
index 6565cd9f57..1abb8fbde3 100644
--- a/src/megatron/bridge/training/config.py
+++ b/src/megatron/bridge/training/config.py
@@ -890,6 +890,18 @@ class LoggerConfig:
     wandb_entity: Optional[str] = None
     """The wandb entity name."""
 
+    mlflow_experiment: Optional[str] = None
+    """The MLFlow experiment name."""
+
+    mlflow_run_name: Optional[str] = None
+    """The MLFlow run name."""
+
+    mlflow_tracking_uri: Optional[str] = None
+    """Optional MLFlow tracking URI."""
+
+    mlflow_tags: Optional[dict[str, str]] = None
+    """Optional tags to apply to the MLFlow run."""
+
     logging_level: int = logging.INFO
     """Set default logging level"""
 
diff --git a/src/megatron/bridge/training/state.py b/src/megatron/bridge/training/state.py
index c2500aa765..9a14556116 100644
--- a/src/megatron/bridge/training/state.py
+++ b/src/megatron/bridge/training/state.py
@@ -31,7 +31,7 @@
 from megatron.bridge.training.nvrx_straggler import NVRxStragglerDetectionManager
 from megatron.bridge.training.tokenizers.tokenizer import build_tokenizer
 from megatron.bridge.training.utils.sig_utils import DistributedSignalHandler
-from megatron.bridge.utils.common_utils import get_rank_safe, get_world_size_safe
+from megatron.bridge.utils.common_utils import get_rank_safe, get_world_size_safe, warn_rank_0
 
 
 @dataclass
@@ -124,6 +124,7 @@ def __init__(self) -> None:
         self._tokenizer: Optional[Any] = None
         self._tensorboard_logger: Optional[SummaryWriter] = None
         self._wandb_logger: Optional[Any] = None
+        self._mlflow_logger: Optional[Any] = None
         self._timers: Optional[Timers] = None
         self._train_state: Optional[TrainState] = None
         self.rank_monitor_client: Optional[Any] = None
@@ -234,12 +235,88 @@ def safe_serialize(obj):
                 self._wandb_logger = None
         return self._wandb_logger
 
+    @property
+    def mlflow_logger(self) -> Optional[Any]:
+        """The MLFlow logger instance.
+
+        Uses the configuration under LoggerConfig to create or resume an MLFlow run.
+        Restricted to the last rank to avoid duplicate entries or probably any racing conditions.
+        """
+        if self._mlflow_logger is None:
+            cfg = self.cfg
+            if cfg is None:
+                self._mlflow_logger = None
+                return self._mlflow_logger
+
+            logger_cfg = cfg.logger
+            if logger_cfg.mlflow_experiment and get_rank_safe() == (get_world_size_safe() - 1):
+                if logger_cfg.mlflow_run_name == "":
+                    raise ValueError("Please specify the mlflow_run_name for MLFlow logging!")
+    
+                import mlflow
+                # set tracking URI
+                if logger_cfg.mlflow_tracking_uri:
+                    mlflow.set_tracking_uri(logger_cfg.mlflow_tracking_uri)
+
+                # Set or get experiment
+                mlflow.set_experiment(logger_cfg.mlflow_experiment)
+
+                # Prepare tags and params
+                def safe_serialize(obj: Any) -> str:
+                    """Safely convert any object to a JSON-serializable string."""
+                    try:
+                        result = str(obj)
+                        if not isinstance(result, str):
+                            return f"<{type(obj).__name__}>"
+                        return result
+                    except Exception:
+                        return f"<{type(obj).__name__}>"
+
+                def _flatten_dict(d: dict[str, Any], parent_key: str = "", sep: str = ".") -> dict[str, Any]:
+                    items: dict[str, Any] = {}
+                    for k, v in d.items():
+                        new_key = f"{parent_key}{sep}{k}" if parent_key else k
+                        if isinstance(v, dict):
+                            items.update(_flatten_dict(v, new_key, sep=sep))
+                        else:
+                            if isinstance(v, (list, tuple)):
+                                v = [safe_serialize(x) for x in v]
+                            items[new_key] = v
+                    return items
+
+                config_dict = cfg.to_dict()
+                sanitized_config = json.loads(json.dumps(config_dict, default=safe_serialize))
+                flat_params = _flatten_dict(sanitized_config)
+
+                # Start or resume a run
+                run_name = logger_cfg.mlflow_run_name
+                tags = logger_cfg.mlflow_tags or {}
+
+                active_run = mlflow.active_run()
+                if active_run is None:
+                    mlflow.start_run(run_name=run_name, tags=tags or None)
+                elif tags:
+                    # If there is already an active run, at least set provided tags
+                    mlflow.set_tags(tags)
+
+                # Log flattened configuration as params (best-effort)
+                stringified_params = {
+                    key: (safe_serialize(value) if not isinstance(value, (int, float, bool, str)) else value)
+                    for key, value in flat_params.items()
+                }
+                mlflow.log_params(stringified_params)
+                self._mlflow_logger = mlflow
+            else:
+                self._mlflow_logger = None
+        return self._mlflow_logger
+
     @property
     def timers(self) -> Timers:
         """The Megatron Timers instance used for tracking execution times."""
         if self._timers is None:
             self._timers = Timers(self.cfg.logger.timing_log_level, self.cfg.logger.timing_log_option)
             self._timers.write_to_wandb = types.MethodType(_timers_write_to_wandb, self._timers)
+            self._timers.write_to_mlflow = types.MethodType(_timers_write_to_mlflow, self._timers)
         return self._timers
 
     @property
@@ -344,6 +421,7 @@ def reset_for_restart(self) -> None:
         self._train_state = None
         self._tensorboard_logger = None
         self._wandb_logger = None
+        self._mlflow_logger = None
         self._energy_monitor = None
         self._energy_monitor_created = False
         self._signal_handler = None
@@ -371,3 +449,33 @@ def _timers_write_to_wandb(
         for name in name_to_min_max_time:
             _, max_time = name_to_min_max_time[name]
             writer.log({name + "-time": max_time}, iteration)
+
+
+def _sanitize_mlflow_metric_name(metric_name: str) -> str:
+    """Sanitize metric names for MLFlow by replacing forward slashes with underscores."""
+    return metric_name.replace("/", "_")
+
+
+def _timers_write_to_mlflow(
+    self: Timers,
+    names: list[str],
+    logger: Any,
+    iteration: int,
+    normalizer: float = 1.0,
+    reset: bool = True,
+    barrier: bool = False,
+) -> None:
+    """Patch to write timers to MLFlow for Megatron Core Timers."""
+    assert normalizer > 0.0
+    name_to_min_max_time = self._get_global_min_max_time(names, reset, barrier, normalizer)
+    if logger is not None:
+        metrics: dict[str, float] = {}
+        for name in name_to_min_max_time:
+            _, max_time = name_to_min_max_time[name]
+            sanitized_name = _sanitize_mlflow_metric_name(name + "-time")
+            metrics[sanitized_name] = max_time
+        try:
+            logger.log_metrics(metrics, step=iteration)
+        except Exception:
+            # continue training
+            warn_rank_0("Failed to log timer metrics to MLFlow; continuing without timer metrics.")
diff --git a/src/megatron/bridge/training/utils/mlflow_utils.py b/src/megatron/bridge/training/utils/mlflow_utils.py
new file mode 100644
index 0000000000..778fe69c3b
--- /dev/null
+++ b/src/megatron/bridge/training/utils/mlflow_utils.py
@@ -0,0 +1,69 @@
+from pathlib import Path
+from typing import Any, Optional
+
+from megatron.bridge.utils.common_utils import print_rank_last
+
+
+def on_save_checkpoint_success(
+    checkpoint_path: str,
+    save_dir: str,
+    iteration: int,
+    mlflow_logger: Optional[Any],
+) -> None:
+    """Callback executed after a checkpoint is successfully saved.
+
+    If an MLFlow logger is provided, logs the checkpoint directory as an MLFlow
+    artifact under a structured artifact path that includes the iteration number.
+
+    Args:
+        checkpoint_path: The path to the specific checkpoint file/directory saved.
+        save_dir: The base directory where checkpoints are being saved.
+        iteration: The training iteration at which the checkpoint was saved.
+        mlflow_logger: The MLFlow module (e.g., ``mlflow``) with an active run.
+                       If None, this function is a no-op.
+    """
+    if mlflow_logger is None:
+        return
+
+    try:
+        checkpoint_path = str(Path(checkpoint_path).resolve())
+        base_name = Path(save_dir).name or "checkpoints"
+        artifact_subdir = f"{base_name}/iter_{iteration:07d}"
+        mlflow_logger.log_artifacts(checkpoint_path, artifact_path=artifact_subdir)
+    except Exception as exc:
+        # continue training
+        print_rank_last(f"Failed to log checkpoint artifacts to MLFlow: {exc}")
+
+
+def on_load_checkpoint_success(
+    checkpoint_path: str,
+    load_dir: str,
+    mlflow_logger: Optional[Any],
+) -> None:
+    """Callback executed after a checkpoint is successfully loaded.
+
+    For MLFlow, this emits a simple metric and tag to document which checkpoint
+    was loaded during the run. It does not perform artifact lookups.
+
+    Args:
+        checkpoint_path: The path to the specific checkpoint file/directory loaded.
+        load_dir: The base directory from which the checkpoint was loaded.
+        mlflow_logger: The MLFlow module (e.g., ``mlflow``) with an active run.
+                       If None, this function is a no-op.
+    """
+    if mlflow_logger is None:
+        return
+
+    try:
+        resolved_ckpt = str(Path(checkpoint_path).resolve())
+        resolved_load_dir = str(Path(load_dir).resolve())
+        mlflow_logger.set_tags(
+            {
+                "last_loaded_checkpoint": resolved_ckpt,
+                "checkpoint_base_dir": resolved_load_dir,
+            }
+        )
+    except Exception as exc:
+        print_rank_last(f"Failed to record loaded checkpoint information to MLFlow: {exc}")
+
+
diff --git a/src/megatron/bridge/training/utils/train_utils.py b/src/megatron/bridge/training/utils/train_utils.py
index 98fb14fa14..d1eb68944d 100644
--- a/src/megatron/bridge/training/utils/train_utils.py
+++ b/src/megatron/bridge/training/utils/train_utils.py
@@ -75,6 +75,16 @@
 }
 
 
+def _sanitize_mlflow_metric_name(metric_name: str) -> str:
+    """Sanitize metric names for MLFlow by replacing forward slashes with underscores."""
+    return metric_name.replace("/", "_")
+
+
+def _sanitize_mlflow_metrics(metrics: dict[str, Any]) -> dict[str, Any]:
+    """Sanitize all metric names in a dictionary for MLFlow logging."""
+    return {_sanitize_mlflow_metric_name(key): value for key, value in metrics.items()}
+
+
 def param_is_not_shared(param: nn.Parameter) -> bool:
     """Check if a parameter is marked as not shared.
 
@@ -366,6 +376,7 @@ def training_log(
     iteration = train_state.step
     writer = global_state.tensorboard_logger
     wandb_writer = global_state.wandb_logger
+    mlflow_logger = global_state.mlflow_logger
     energy_monitor = global_state.energy_monitor
     logger_config = config.logger
     train_config = config.train
@@ -437,6 +448,8 @@ def training_log(
         timers.write(timers_to_log, writer, iteration, normalizer=total_iterations, reset=reset_in_tb)
         if hasattr(timers, "write_to_wandb"):
             timers.write_to_wandb(timers_to_log, wandb_writer, iteration, normalizer=total_iterations, reset=True)
+        if hasattr(timers, "write_to_mlflow"):
+            timers.write_to_mlflow(timers_to_log, mlflow_logger, iteration, normalizer=total_iterations, reset=True)
 
     if writer and (iteration % logger_config.tensorboard_log_interval == 0):
         if config.profiling:
@@ -458,6 +471,8 @@ def training_log(
                 writer.add_scalar(metric, value, iteration)
             if wandb_writer:
                 wandb_writer.log(throughput_report, iteration)
+            if mlflow_logger and throughput_report:
+                mlflow_logger.log_metrics(_sanitize_mlflow_metrics(throughput_report), step=iteration)
         if logger_config.log_memory_to_tensorboard:
             memory_report = report_memory(memory_keys=logger_config.memory_keys)
             memory_report = {f"memory/{mem_stat}": val for (mem_stat, val) in memory_report.items()}
@@ -465,6 +480,8 @@ def training_log(
                 writer.add_scalar(metric, value, iteration)
             if wandb_writer:
                 wandb_writer.log(memory_report, iteration)
+            if mlflow_logger and memory_report:
+                mlflow_logger.log_metrics(_sanitize_mlflow_metrics(memory_report), step=iteration)
         if logger_config.log_runtime_to_tensorboard:
             runtime_report = report_runtime(
                 train_state=train_state,
@@ -477,38 +494,60 @@ def training_log(
                 writer.add_scalar(metric, value, iteration)
             if wandb_writer:
                 wandb_writer.log(runtime_report, iteration)
+            if mlflow_logger and runtime_report:
+                mlflow_logger.log_metrics(_sanitize_mlflow_metrics(runtime_report), step=iteration)
         if logger_config.log_l2_norm_grad_to_tensorboard:
             l2_report = report_l2_norm_grad(model)
             for metric, value in l2_report.items():
                 writer.add_scalar(metric, value, iteration)
             if wandb_writer:
                 wandb_writer.log(l2_report, iteration)
+            if mlflow_logger and l2_report:
+                mlflow_logger.log_metrics(_sanitize_mlflow_metrics(l2_report), step=iteration)
         if wandb_writer:
             wandb_writer.log({"samples vs steps": train_state.consumed_train_samples}, iteration)
+        if mlflow_logger:
+            mlflow_logger.log_metrics(
+                    {"samples vs steps": train_state.consumed_train_samples}, step=iteration
+                )
         writer.add_scalar("learning-rate", learning_rate, iteration)
         writer.add_scalar("learning-rate vs samples", learning_rate, train_state.consumed_train_samples)
         if wandb_writer:
             wandb_writer.log({"learning-rate": learning_rate}, iteration)
+        if mlflow_logger and learning_rate is not None:
+            mlflow_logger.log_metrics({"learning-rate": learning_rate}, step=iteration)
         if config.optimizer.decoupled_lr is not None:
             writer.add_scalar("decoupled-learning-rate", decoupled_learning_rate, iteration)
         if global_state.train_state.skipped_train_samples > 0:
             writer.add_scalar("skipped-train-samples", global_state.train_state.skipped_train_samples, iteration)
             if wandb_writer:
                 wandb_writer.log({"skipped-train-samples": global_state.train_state.skipped_train_samples}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics(
+                    {"skipped-train-samples": global_state.train_state.skipped_train_samples},
+                    step=iteration,
+                )
         writer.add_scalar("batch-size", batch_size, iteration)
         writer.add_scalar("batch-size vs samples", batch_size, global_state.train_state.consumed_train_samples)
         if wandb_writer:
             wandb_writer.log({"batch-size": batch_size}, iteration)
+        if mlflow_logger:
+            mlflow_logger.log_metrics({"batch-size": batch_size}, step=iteration)
         for key in loss_dict:
             writer.add_scalar(key, loss_dict[key], iteration)
             writer.add_scalar(key + " vs samples", loss_dict[key], global_state.train_state.consumed_train_samples)
             if wandb_writer:
                 wandb_writer.log({key: loss_dict[key]}, iteration)
+        if mlflow_logger and loss_dict:
+            loss_metrics = {key: float(val) for key, val in loss_dict.items()}
+            mlflow_logger.log_metrics(loss_metrics, step=iteration)
         if logger_config.log_loss_scale_to_tensorboard:
             writer.add_scalar("loss-scale", loss_scale, iteration)
             writer.add_scalar("loss-scale vs samples", loss_scale, global_state.train_state.consumed_train_samples)
             if wandb_writer:
                 wandb_writer.log({"loss-scale": loss_scale}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics({"loss-scale": loss_scale}, step=iteration)
         if logger_config.log_world_size_to_tensorboard:
             writer.add_scalar("world-size", get_world_size_safe(), iteration)
             writer.add_scalar(
@@ -516,11 +555,15 @@ def training_log(
             )
             if wandb_writer:
                 wandb_writer.log({"world-size": get_world_size_safe()}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics({"world-size": get_world_size_safe()}, step=iteration)
         if grad_norm is not None:
             writer.add_scalar("grad-norm", grad_norm, iteration)
             writer.add_scalar("grad-norm vs samples", grad_norm, global_state.train_state.consumed_train_samples)
             if wandb_writer:
                 wandb_writer.log({"grad-norm": grad_norm}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics({"grad-norm": grad_norm}, step=iteration)
         if num_zeros_in_grad is not None:
             writer.add_scalar("num-zeros", num_zeros_in_grad, iteration)
             writer.add_scalar(
@@ -528,11 +571,15 @@ def training_log(
             )
             if wandb_writer:
                 wandb_writer.log({"num-zeros": num_zeros_in_grad}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics({"num-zeros": num_zeros_in_grad}, step=iteration)
         if params_norm is not None:
             writer.add_scalar("params-norm", params_norm, iteration)
             writer.add_scalar("params-norm vs samples", params_norm, global_state.train_state.consumed_train_samples)
             if wandb_writer:
                 wandb_writer.log({"params-norm": params_norm}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics({"params-norm": params_norm}, step=iteration)
 
     if config.model.num_moe_experts is not None:
         moe_loss_scale = 1 / get_num_microbatches()
@@ -582,12 +629,22 @@ def training_log(
                 if wandb_writer:
                     wandb_writer.log({"throughput/tflops/device": per_gpu_tf}, iteration)
                     wandb_writer.log({"throughput/tflops": per_gpu_tf * get_world_size_safe()}, iteration)
+                if mlflow_logger:
+                    mlflow_logger.log_metrics(
+                        _sanitize_mlflow_metrics({
+                            "throughput/tflops/device": per_gpu_tf,
+                            "throughput/tflops": per_gpu_tf * get_world_size_safe(),
+                        }),
+                        step=iteration,
+                    )
 
         if logger_config.log_timers_to_tensorboard:
             if writer:
                 writer.add_scalar("iteration-time", elapsed_time_per_iteration, iteration)
             if wandb_writer:
                 wandb_writer.log({"iteration-time": elapsed_time_per_iteration}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics({"iteration-time": elapsed_time_per_iteration}, step=iteration)
         log_string = f" [{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}]"
         log_string += " iteration {:8d}/{:8d} |".format(iteration, train_config.train_iters)
         log_string += " consumed samples: {:12d} |".format(global_state.train_state.consumed_train_samples)
@@ -609,6 +666,11 @@ def training_log(
             if wandb_writer:
                 wandb_writer.log({"iter-energy/gpu": energy}, iteration)
                 wandb_writer.log({"power/gpu": power}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics(
+                    _sanitize_mlflow_metrics({"iter-energy/gpu": float(energy), "power/gpu": float(power)}),
+                    step=iteration,
+                )
 
         # Decoupled_learning_rate should be not None only on first and last pipeline stage.
         log_string += f" learning rate: {learning_rate:.6E} |"
diff --git a/tutorials/recipes/llama/conf/llama32_1b_finetune.yaml b/tutorials/recipes/llama/conf/llama32_1b_finetune.yaml
index 02c3897a6b..3617c712b9 100644
--- a/tutorials/recipes/llama/conf/llama32_1b_finetune.yaml
+++ b/tutorials/recipes/llama/conf/llama32_1b_finetune.yaml
@@ -47,9 +47,9 @@ checkpoint:
   # Directory to save finetuned checkpoints
   save: ./checkpoints/llama32_1b_finetuned
   # Directory to resume from during training
-  load: ./checkpoints/llama32_1b
+  load: ./checkpoints/llama32_1b_finetuned
   # Directory for pretrained weights in Megatron format
-  pretrained_checkpoint: ./path/to/pretrained/checkpoint/
+  pretrained_checkpoint: ./checkpoints/llama32_1b
 
   save_interval: 50
 
@@ -72,6 +72,13 @@ logger:
   tensorboard_dir: ./logs/llama32_1b_finetuned
   # wandb_project: my_finetune_project
   # wandb_entity: my_team
+  # mlflow_experiment: llama32_1b_finetuned
+  # mlflow_run_name: llama32_1b_finetuned_run
+  # mlflow_tags:
+  #   project: llama32
+  #   phase: finetune
+  #   variant: mlflow_example
+
 
 # Random seed
 rng:
diff --git a/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml b/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
index 9b9e8bf1d9..a88a0385f2 100644
--- a/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
+++ b/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
@@ -62,6 +62,12 @@ logger:
   tensorboard_dir: ./logs/llama32_1b
   # wandb_project: my_project  # Uncomment to enable W&B logging
   # wandb_entity: my_team
+  # mlflow_experiment: llama32_1b_pretrain
+  # mlflow_run_name: llama32_1b_pretrain_run
+  # mlflow_tags:
+  #   project: llama32
+  #   phase: pretrain
+  #   variant: mlflow_example
 
 # Random seed
 rng:

From 97fa74875f4126537d45d156f70ea03358eac436 Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Mon, 1 Dec 2025 03:44:59 +0000
Subject: [PATCH 02/13] few fixes, ruff formatting

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 docs/training/logging.md                      |  8 +++----
 src/megatron/bridge/training/checkpointing.py |  2 +-
 src/megatron/bridge/training/state.py         | 10 +++------
 .../bridge/training/utils/mlflow_utils.py     |  2 --
 .../bridge/training/utils/train_utils.py      | 21 +++++++------------
 5 files changed, 16 insertions(+), 27 deletions(-)

diff --git a/docs/training/logging.md b/docs/training/logging.md
index 043cfc4258..e22cd64afc 100644
--- a/docs/training/logging.md
+++ b/docs/training/logging.md
@@ -156,13 +156,13 @@ This allows seamless integration of W&B logging into your training workflow with
 
 ### MLFlow
 
-Megatron Bridge can also log metrics and artifacts to MLFlow, following the same pattern as the W&B integration.
+Megatron Bridge can log metrics and artifacts to MLFlow, following the same pattern as the W&B integration.
 
 #### What Gets Logged
 
 When enabled, MLFlow receives:
 
-- Flattened training configuration as run parameters
+- Training configuration as run parameters
 - Scalar metrics (losses, learning rate, batch size, throughput, timers, memory, runtime, norms, energy, etc.)
 - Checkpoint artifacts saved under an experiment-specific artifact path per iteration
 
@@ -184,11 +184,11 @@ When enabled, MLFlow receives:
   from megatron.bridge.training.config import LoggerConfig
 
   cfg.logger = LoggerConfig(
-      tensorboard_dir="./runs/tensorboard",       # optional but recommended
+      tensorboard_dir="./runs/tensorboard",
       mlflow_experiment="my_megatron_experiment",
       mlflow_run_name="llama32_1b_pretrain_run",
       mlflow_tracking_uri="http://mlflow:5000",  # optional
-      mlflow_tags={                               # optional
+      mlflow_tags={                              # optional
           "project": "llama32",
           "phase": "pretrain",
       },
diff --git a/src/megatron/bridge/training/checkpointing.py b/src/megatron/bridge/training/checkpointing.py
index 8f36496e8d..a340f9e177 100644
--- a/src/megatron/bridge/training/checkpointing.py
+++ b/src/megatron/bridge/training/checkpointing.py
@@ -59,7 +59,7 @@
 from megatron.bridge.training.state import GlobalState, TrainState
 from megatron.bridge.training.tokenizers.config import TokenizerConfig
 from megatron.bridge.training.tokenizers.tokenizer import MegatronTokenizer
-from megatron.bridge.training.utils import wandb_utils, mlflow_utils
+from megatron.bridge.training.utils import mlflow_utils, wandb_utils
 from megatron.bridge.training.utils.checkpoint_utils import (
     checkpoint_exists,
     ensure_directory_exists,
diff --git a/src/megatron/bridge/training/state.py b/src/megatron/bridge/training/state.py
index 9a14556116..bd2ad9d663 100644
--- a/src/megatron/bridge/training/state.py
+++ b/src/megatron/bridge/training/state.py
@@ -252,8 +252,9 @@ def mlflow_logger(self) -> Optional[Any]:
             if logger_cfg.mlflow_experiment and get_rank_safe() == (get_world_size_safe() - 1):
                 if logger_cfg.mlflow_run_name == "":
                     raise ValueError("Please specify the mlflow_run_name for MLFlow logging!")
-    
+
                 import mlflow
+
                 # set tracking URI
                 if logger_cfg.mlflow_tracking_uri:
                     mlflow.set_tracking_uri(logger_cfg.mlflow_tracking_uri)
@@ -451,11 +452,6 @@ def _timers_write_to_wandb(
             writer.log({name + "-time": max_time}, iteration)
 
 
-def _sanitize_mlflow_metric_name(metric_name: str) -> str:
-    """Sanitize metric names for MLFlow by replacing forward slashes with underscores."""
-    return metric_name.replace("/", "_")
-
-
 def _timers_write_to_mlflow(
     self: Timers,
     names: list[str],
@@ -472,7 +468,7 @@ def _timers_write_to_mlflow(
         metrics: dict[str, float] = {}
         for name in name_to_min_max_time:
             _, max_time = name_to_min_max_time[name]
-            sanitized_name = _sanitize_mlflow_metric_name(name + "-time")
+            sanitized_name = name.replace("/", "_") + "-time"
             metrics[sanitized_name] = max_time
         try:
             logger.log_metrics(metrics, step=iteration)
diff --git a/src/megatron/bridge/training/utils/mlflow_utils.py b/src/megatron/bridge/training/utils/mlflow_utils.py
index 778fe69c3b..bdb376e060 100644
--- a/src/megatron/bridge/training/utils/mlflow_utils.py
+++ b/src/megatron/bridge/training/utils/mlflow_utils.py
@@ -65,5 +65,3 @@ def on_load_checkpoint_success(
         )
     except Exception as exc:
         print_rank_last(f"Failed to record loaded checkpoint information to MLFlow: {exc}")
-
-
diff --git a/src/megatron/bridge/training/utils/train_utils.py b/src/megatron/bridge/training/utils/train_utils.py
index d1eb68944d..69b932fb86 100644
--- a/src/megatron/bridge/training/utils/train_utils.py
+++ b/src/megatron/bridge/training/utils/train_utils.py
@@ -75,14 +75,9 @@
 }
 
 
-def _sanitize_mlflow_metric_name(metric_name: str) -> str:
-    """Sanitize metric names for MLFlow by replacing forward slashes with underscores."""
-    return metric_name.replace("/", "_")
-
-
 def _sanitize_mlflow_metrics(metrics: dict[str, Any]) -> dict[str, Any]:
     """Sanitize all metric names in a dictionary for MLFlow logging."""
-    return {_sanitize_mlflow_metric_name(key): value for key, value in metrics.items()}
+    return {key.replace("/", "_"): value for key, value in metrics.items()}
 
 
 def param_is_not_shared(param: nn.Parameter) -> bool:
@@ -507,9 +502,7 @@ def training_log(
         if wandb_writer:
             wandb_writer.log({"samples vs steps": train_state.consumed_train_samples}, iteration)
         if mlflow_logger:
-            mlflow_logger.log_metrics(
-                    {"samples vs steps": train_state.consumed_train_samples}, step=iteration
-                )
+            mlflow_logger.log_metrics({"samples vs steps": train_state.consumed_train_samples}, step=iteration)
         writer.add_scalar("learning-rate", learning_rate, iteration)
         writer.add_scalar("learning-rate vs samples", learning_rate, train_state.consumed_train_samples)
         if wandb_writer:
@@ -631,10 +624,12 @@ def training_log(
                     wandb_writer.log({"throughput/tflops": per_gpu_tf * get_world_size_safe()}, iteration)
                 if mlflow_logger:
                     mlflow_logger.log_metrics(
-                        _sanitize_mlflow_metrics({
-                            "throughput/tflops/device": per_gpu_tf,
-                            "throughput/tflops": per_gpu_tf * get_world_size_safe(),
-                        }),
+                        _sanitize_mlflow_metrics(
+                            {
+                                "throughput/tflops/device": per_gpu_tf,
+                                "throughput/tflops": per_gpu_tf * get_world_size_safe(),
+                            }
+                        ),
                         step=iteration,
                     )
 

From 4397ee9d065975259fd1d2ef75c558e422d5f10f Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Tue, 2 Dec 2025 11:45:15 -0500
Subject: [PATCH 03/13] fixes mlflow to be consistent with wandb logic

Co-authored-by: Philip Petrakian <pgpetrak@gmail.com>
Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 src/megatron/bridge/training/utils/train_utils.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/src/megatron/bridge/training/utils/train_utils.py b/src/megatron/bridge/training/utils/train_utils.py
index 69b932fb86..58616437c7 100644
--- a/src/megatron/bridge/training/utils/train_utils.py
+++ b/src/megatron/bridge/training/utils/train_utils.py
@@ -466,7 +466,7 @@ def training_log(
                 writer.add_scalar(metric, value, iteration)
             if wandb_writer:
                 wandb_writer.log(throughput_report, iteration)
-            if mlflow_logger and throughput_report:
+            if mlflow_logger:
                 mlflow_logger.log_metrics(_sanitize_mlflow_metrics(throughput_report), step=iteration)
         if logger_config.log_memory_to_tensorboard:
             memory_report = report_memory(memory_keys=logger_config.memory_keys)
@@ -475,7 +475,7 @@ def training_log(
                 writer.add_scalar(metric, value, iteration)
             if wandb_writer:
                 wandb_writer.log(memory_report, iteration)
-            if mlflow_logger and memory_report:
+            if mlflow_logger:
                 mlflow_logger.log_metrics(_sanitize_mlflow_metrics(memory_report), step=iteration)
         if logger_config.log_runtime_to_tensorboard:
             runtime_report = report_runtime(
@@ -489,7 +489,7 @@ def training_log(
                 writer.add_scalar(metric, value, iteration)
             if wandb_writer:
                 wandb_writer.log(runtime_report, iteration)
-            if mlflow_logger and runtime_report:
+            if mlflow_logger:
                 mlflow_logger.log_metrics(_sanitize_mlflow_metrics(runtime_report), step=iteration)
         if logger_config.log_l2_norm_grad_to_tensorboard:
             l2_report = report_l2_norm_grad(model)
@@ -497,7 +497,7 @@ def training_log(
                 writer.add_scalar(metric, value, iteration)
             if wandb_writer:
                 wandb_writer.log(l2_report, iteration)
-            if mlflow_logger and l2_report:
+            if mlflow_logger:
                 mlflow_logger.log_metrics(_sanitize_mlflow_metrics(l2_report), step=iteration)
         if wandb_writer:
             wandb_writer.log({"samples vs steps": train_state.consumed_train_samples}, iteration)
@@ -505,7 +505,7 @@ def training_log(
             mlflow_logger.log_metrics({"samples vs steps": train_state.consumed_train_samples}, step=iteration)
         writer.add_scalar("learning-rate", learning_rate, iteration)
         writer.add_scalar("learning-rate vs samples", learning_rate, train_state.consumed_train_samples)
-        if wandb_writer:
+        if wandb_writer and learning_rate is not None:
             wandb_writer.log({"learning-rate": learning_rate}, iteration)
         if mlflow_logger and learning_rate is not None:
             mlflow_logger.log_metrics({"learning-rate": learning_rate}, step=iteration)
@@ -531,7 +531,7 @@ def training_log(
             writer.add_scalar(key + " vs samples", loss_dict[key], global_state.train_state.consumed_train_samples)
             if wandb_writer:
                 wandb_writer.log({key: loss_dict[key]}, iteration)
-        if mlflow_logger and loss_dict:
+        if mlflow_logger:
             loss_metrics = {key: float(val) for key, val in loss_dict.items()}
             mlflow_logger.log_metrics(loss_metrics, step=iteration)
         if logger_config.log_loss_scale_to_tensorboard:

From de13928e8c9b730a4cbbcd9938e041e27e771101 Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Tue, 2 Dec 2025 15:50:50 -0500
Subject: [PATCH 04/13] moved _sanitize_mlflow_metrics to mlflow_utils.py

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 src/megatron/bridge/training/utils/mlflow_utils.py | 5 +++++
 src/megatron/bridge/training/utils/train_utils.py  | 6 +-----
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/src/megatron/bridge/training/utils/mlflow_utils.py b/src/megatron/bridge/training/utils/mlflow_utils.py
index bdb376e060..c86c487b31 100644
--- a/src/megatron/bridge/training/utils/mlflow_utils.py
+++ b/src/megatron/bridge/training/utils/mlflow_utils.py
@@ -65,3 +65,8 @@ def on_load_checkpoint_success(
         )
     except Exception as exc:
         print_rank_last(f"Failed to record loaded checkpoint information to MLFlow: {exc}")
+
+
+def _sanitize_mlflow_metrics(metrics: dict[str, Any]) -> dict[str, Any]:
+    """Sanitize all metric names in a dictionary for MLFlow logging."""
+    return {key.replace("/", "_"): value for key, value in metrics.items()}
diff --git a/src/megatron/bridge/training/utils/train_utils.py b/src/megatron/bridge/training/utils/train_utils.py
index 58616437c7..13a8766786 100644
--- a/src/megatron/bridge/training/utils/train_utils.py
+++ b/src/megatron/bridge/training/utils/train_utils.py
@@ -34,6 +34,7 @@
 from megatron.bridge.training.forward_step_func_types import ForwardStepCallable
 from megatron.bridge.training.state import GlobalState, TrainState
 from megatron.bridge.training.utils.flop_utils import num_floating_point_operations
+from megatron.bridge.training.utils.mlflow_utils import _sanitize_mlflow_metrics
 from megatron.bridge.training.utils.pg_utils import get_pg_collection
 from megatron.bridge.training.utils.theoretical_memory_utils import report_theoretical_memory
 from megatron.bridge.utils.common_utils import get_world_size_safe, is_last_rank, print_rank_0, print_rank_last
@@ -75,11 +76,6 @@
 }
 
 
-def _sanitize_mlflow_metrics(metrics: dict[str, Any]) -> dict[str, Any]:
-    """Sanitize all metric names in a dictionary for MLFlow logging."""
-    return {key.replace("/", "_"): value for key, value in metrics.items()}
-
-
 def param_is_not_shared(param: nn.Parameter) -> bool:
     """Check if a parameter is marked as not shared.
 

From 9df13b4abda95e6a1b1d48e2aeeceb0e0bd590cc Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Fri, 5 Dec 2025 14:49:40 +0000
Subject: [PATCH 05/13] moved save_serialize to log_utils

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 src/megatron/bridge/training/state.py         | 31 +------------------
 .../bridge/training/utils/log_utils.py        | 19 ++++++++++++
 2 files changed, 20 insertions(+), 30 deletions(-)

diff --git a/src/megatron/bridge/training/state.py b/src/megatron/bridge/training/state.py
index bd2ad9d663..0d4d65d25b 100644
--- a/src/megatron/bridge/training/state.py
+++ b/src/megatron/bridge/training/state.py
@@ -30,6 +30,7 @@
 from megatron.bridge.training.config import ConfigContainer
 from megatron.bridge.training.nvrx_straggler import NVRxStragglerDetectionManager
 from megatron.bridge.training.tokenizers.tokenizer import build_tokenizer
+from megatron.bridge.training.utils.log_utils import safe_serialize
 from megatron.bridge.training.utils.sig_utils import DistributedSignalHandler
 from megatron.bridge.utils.common_utils import get_rank_safe, get_world_size_safe, warn_rank_0
 
@@ -194,26 +195,6 @@ def wandb_logger(self) -> Optional[Any]:
 
                 save_dir = self.cfg.logger.wandb_save_dir or os.path.join(self.cfg.checkpoint.save, "wandb")
 
-                # Sanitize config for WandB by doing a JSON round-trip
-                # This ensures all objects are converted to basic Python types that WandB can handle
-                def safe_serialize(obj):
-                    """Safely convert any object to a JSON-serializable type.
-
-                    Handles objects with broken __str__ or __repr__ methods that return
-                    non-string types (e.g., PipelineParallelLayerLayout returns list).
-                    """
-                    try:
-                        # Try str() first
-                        result = str(obj)
-                        # Verify it actually returns a string
-                        if not isinstance(result, str):
-                            # __str__ returned non-string type, use type name instead
-                            return f"<{type(obj).__name__}>"
-                        return result
-                    except Exception:
-                        # __str__ raised an exception, use type name as fallback
-                        return f"<{type(obj).__name__}>"
-
                 config_dict = self.cfg.to_dict()
                 sanitized_config = json.loads(json.dumps(config_dict, default=safe_serialize))
 
@@ -263,16 +244,6 @@ def mlflow_logger(self) -> Optional[Any]:
                 mlflow.set_experiment(logger_cfg.mlflow_experiment)
 
                 # Prepare tags and params
-                def safe_serialize(obj: Any) -> str:
-                    """Safely convert any object to a JSON-serializable string."""
-                    try:
-                        result = str(obj)
-                        if not isinstance(result, str):
-                            return f"<{type(obj).__name__}>"
-                        return result
-                    except Exception:
-                        return f"<{type(obj).__name__}>"
-
                 def _flatten_dict(d: dict[str, Any], parent_key: str = "", sep: str = ".") -> dict[str, Any]:
                     items: dict[str, Any] = {}
                     for k, v in d.items():
diff --git a/src/megatron/bridge/training/utils/log_utils.py b/src/megatron/bridge/training/utils/log_utils.py
index e1540a8149..f62813af3e 100644
--- a/src/megatron/bridge/training/utils/log_utils.py
+++ b/src/megatron/bridge/training/utils/log_utils.py
@@ -173,3 +173,22 @@ def log_single_rank(logger: logging.Logger, *args: Any, rank: int = 0, **kwargs:
             logger.log(*args, **kwargs)
     else:
         logger.log(*args, **kwargs)
+
+
+def safe_serialize(obj) -> str:
+    """Safely convert any object to a JSON-serializable type.
+
+    Handles objects with broken __str__ or __repr__ methods that return
+    non-string types (e.g., PipelineParallelLayerLayout returns list).
+    """
+    try:
+        # Try str() first
+        result = str(obj)
+        # Verify it actually returns a string
+        if not isinstance(result, str):
+            # __str__ returned non-string type, use type name instead
+            return f"<{type(obj).__name__}>"
+        return result
+    except Exception:
+        # __str__ raised an exception, use type name as fallback
+        return f"<{type(obj).__name__}>"

From e38df2110a9ddbfb7d8d058ed67d4b30cee36132 Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Fri, 5 Dec 2025 15:24:21 +0000
Subject: [PATCH 06/13] added unit tests

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 .../training/utils/test_mlflow_utils.py       | 297 ++++++++++++++++++
 .../llama/conf/llama32_1b_pretrain.yaml       |  12 +-
 2 files changed, 303 insertions(+), 6 deletions(-)
 create mode 100644 tests/unit_tests/training/utils/test_mlflow_utils.py

diff --git a/tests/unit_tests/training/utils/test_mlflow_utils.py b/tests/unit_tests/training/utils/test_mlflow_utils.py
new file mode 100644
index 0000000000..b9ccbcd756
--- /dev/null
+++ b/tests/unit_tests/training/utils/test_mlflow_utils.py
@@ -0,0 +1,297 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tempfile
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+from megatron.bridge.training.utils.mlflow_utils import (
+    _sanitize_mlflow_metrics,
+    on_load_checkpoint_success,
+    on_save_checkpoint_success,
+)
+
+
+class TestOnSaveCheckpointSuccess:
+    """Test cases for on_save_checkpoint_success function."""
+
+    def test_noop_when_mlflow_logger_is_none(self):
+        """Test that the function does nothing when mlflow_logger is None."""
+        # Should not raise any exception
+        on_save_checkpoint_success(
+            checkpoint_path="/path/to/checkpoint",
+            save_dir="/path/to",
+            iteration=100,
+            mlflow_logger=None,
+        )
+
+    def test_logs_artifacts_with_correct_path(self):
+        """Test that log_artifacts is called with correct arguments."""
+        mock_mlflow = MagicMock()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            checkpoint_path = Path(tmpdir) / "checkpoint"
+            checkpoint_path.mkdir()
+            save_dir = tmpdir
+
+            on_save_checkpoint_success(
+                checkpoint_path=str(checkpoint_path),
+                save_dir=save_dir,
+                iteration=1000,
+                mlflow_logger=mock_mlflow,
+            )
+
+            mock_mlflow.log_artifacts.assert_called_once()
+            call_args = mock_mlflow.log_artifacts.call_args
+
+            # Verify the checkpoint path is resolved
+            assert call_args[0][0] == str(checkpoint_path.resolve())
+
+            # Verify artifact_path format includes iteration
+            artifact_path = call_args[1]["artifact_path"]
+            base_name = Path(save_dir).name
+            assert artifact_path == f"{base_name}/iter_0001000"
+
+    def test_artifact_path_format_with_different_iterations(self):
+        """Test that iteration is zero-padded to 7 digits in artifact path."""
+        mock_mlflow = MagicMock()
+
+        test_cases = [
+            (0, "iter_0000000"),
+            (1, "iter_0000001"),
+            (999, "iter_0000999"),
+            (1234567, "iter_1234567"),
+            (9999999, "iter_9999999"),
+        ]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            checkpoint_path = Path(tmpdir) / "checkpoint"
+            checkpoint_path.mkdir()
+
+            for iteration, expected_suffix in test_cases:
+                mock_mlflow.reset_mock()
+
+                on_save_checkpoint_success(
+                    checkpoint_path=str(checkpoint_path),
+                    save_dir=tmpdir,
+                    iteration=iteration,
+                    mlflow_logger=mock_mlflow,
+                )
+
+                artifact_path = mock_mlflow.log_artifacts.call_args[1]["artifact_path"]
+                assert artifact_path.endswith(expected_suffix), (
+                    f"Expected artifact_path to end with {expected_suffix}, got {artifact_path}"
+                )
+
+    def test_uses_checkpoints_as_default_base_name(self):
+        """Test that 'checkpoints' is used when save_dir has no name."""
+        mock_mlflow = MagicMock()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            checkpoint_path = Path(tmpdir) / "checkpoint"
+            checkpoint_path.mkdir()
+
+            # Use root-like path that would have empty name
+            on_save_checkpoint_success(
+                checkpoint_path=str(checkpoint_path),
+                save_dir="/",
+                iteration=100,
+                mlflow_logger=mock_mlflow,
+            )
+
+            artifact_path = mock_mlflow.log_artifacts.call_args[1]["artifact_path"]
+            assert artifact_path.startswith("checkpoints/")
+
+    def test_handles_exception_gracefully(self):
+        """Test that exceptions are caught and logged, not raised."""
+        mock_mlflow = MagicMock()
+        mock_mlflow.log_artifacts.side_effect = Exception("MLFlow connection error")
+
+        with patch("megatron.bridge.training.utils.mlflow_utils.print_rank_last") as mock_print:
+            # Should not raise exception
+            on_save_checkpoint_success(
+                checkpoint_path="/path/to/checkpoint",
+                save_dir="/path/to",
+                iteration=100,
+                mlflow_logger=mock_mlflow,
+            )
+
+            # Should print error message
+            mock_print.assert_called_once()
+            error_msg = mock_print.call_args[0][0]
+            assert "Failed to log checkpoint artifacts to MLFlow" in error_msg
+            assert "MLFlow connection error" in error_msg
+
+
+class TestOnLoadCheckpointSuccess:
+    """Test cases for on_load_checkpoint_success function."""
+
+    def test_noop_when_mlflow_logger_is_none(self):
+        """Test that the function does nothing when mlflow_logger is None."""
+        # Should not raise any exception
+        on_load_checkpoint_success(
+            checkpoint_path="/path/to/checkpoint",
+            load_dir="/path/to",
+            mlflow_logger=None,
+        )
+
+    def test_sets_correct_tags(self):
+        """Test that set_tags is called with correct checkpoint information."""
+        mock_mlflow = MagicMock()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            checkpoint_path = Path(tmpdir) / "checkpoint"
+            checkpoint_path.mkdir()
+            load_dir = tmpdir
+
+            on_load_checkpoint_success(
+                checkpoint_path=str(checkpoint_path),
+                load_dir=load_dir,
+                mlflow_logger=mock_mlflow,
+            )
+
+            mock_mlflow.set_tags.assert_called_once()
+            tags = mock_mlflow.set_tags.call_args[0][0]
+
+            assert "last_loaded_checkpoint" in tags
+            assert "checkpoint_base_dir" in tags
+            assert tags["last_loaded_checkpoint"] == str(checkpoint_path.resolve())
+            assert tags["checkpoint_base_dir"] == str(Path(load_dir).resolve())
+
+    def test_resolves_relative_paths(self):
+        """Test that relative paths are resolved to absolute paths."""
+        mock_mlflow = MagicMock()
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            checkpoint_path = Path(tmpdir) / "checkpoint"
+            checkpoint_path.mkdir()
+
+            on_load_checkpoint_success(
+                checkpoint_path=str(checkpoint_path),
+                load_dir=tmpdir,
+                mlflow_logger=mock_mlflow,
+            )
+
+            tags = mock_mlflow.set_tags.call_args[0][0]
+
+            # Both paths should be absolute (resolved)
+            assert Path(tags["last_loaded_checkpoint"]).is_absolute()
+            assert Path(tags["checkpoint_base_dir"]).is_absolute()
+
+    def test_handles_exception_gracefully(self):
+        """Test that exceptions are caught and logged, not raised."""
+        mock_mlflow = MagicMock()
+        mock_mlflow.set_tags.side_effect = Exception("MLFlow API error")
+
+        with patch("megatron.bridge.training.utils.mlflow_utils.print_rank_last") as mock_print:
+            # Should not raise exception
+            on_load_checkpoint_success(
+                checkpoint_path="/path/to/checkpoint",
+                load_dir="/path/to",
+                mlflow_logger=mock_mlflow,
+            )
+
+            # Should print error message
+            mock_print.assert_called_once()
+            error_msg = mock_print.call_args[0][0]
+            assert "Failed to record loaded checkpoint information to MLFlow" in error_msg
+            assert "MLFlow API error" in error_msg
+
+
+class TestSanitizeMlflowMetrics:
+    """Test cases for _sanitize_mlflow_metrics function."""
+
+    def test_replaces_slashes_with_underscores(self):
+        """Test that forward slashes are replaced with underscores."""
+        metrics = {
+            "train/loss": 0.5,
+            "train/accuracy": 0.95,
+            "eval/loss": 0.3,
+        }
+
+        result = _sanitize_mlflow_metrics(metrics)
+
+        assert result == {
+            "train_loss": 0.5,
+            "train_accuracy": 0.95,
+            "eval_loss": 0.3,
+        }
+
+    def test_handles_multiple_slashes(self):
+        """Test that multiple slashes in a key are all replaced."""
+        metrics = {
+            "train/layer/0/loss": 1.0,
+            "model/encoder/attention/weight": 0.5,
+        }
+
+        result = _sanitize_mlflow_metrics(metrics)
+
+        assert result == {
+            "train_layer_0_loss": 1.0,
+            "model_encoder_attention_weight": 0.5,
+        }
+
+    def test_preserves_keys_without_slashes(self):
+        """Test that keys without slashes are unchanged."""
+        metrics = {
+            "loss": 0.5,
+            "accuracy": 0.95,
+            "learning_rate": 0.001,
+        }
+
+        result = _sanitize_mlflow_metrics(metrics)
+
+        assert result == metrics
+
+    def test_handles_empty_dict(self):
+        """Test that empty dictionary returns empty dictionary."""
+        result = _sanitize_mlflow_metrics({})
+        assert result == {}
+
+    def test_preserves_values(self):
+        """Test that metric values are preserved unchanged."""
+        metrics = {
+            "train/int_metric": 42,
+            "train/float_metric": 3.14159,
+            "train/string_metric": "value",
+            "train/none_metric": None,
+            "train/list_metric": [1, 2, 3],
+        }
+
+        result = _sanitize_mlflow_metrics(metrics)
+
+        assert result["train_int_metric"] == 42
+        assert result["train_float_metric"] == 3.14159
+        assert result["train_string_metric"] == "value"
+        assert result["train_none_metric"] is None
+        assert result["train_list_metric"] == [1, 2, 3]
+
+    def test_mixed_keys(self):
+        """Test dictionary with both slash and non-slash keys."""
+        metrics = {
+            "train/loss": 0.5,
+            "global_step": 1000,
+            "eval/accuracy": 0.9,
+            "learning_rate": 0.001,
+        }
+
+        result = _sanitize_mlflow_metrics(metrics)
+
+        assert result == {
+            "train_loss": 0.5,
+            "global_step": 1000,
+            "eval_accuracy": 0.9,
+            "learning_rate": 0.001,
+        }
+
diff --git a/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml b/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
index a88a0385f2..f6f9ee17f5 100644
--- a/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
+++ b/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
@@ -62,12 +62,12 @@ logger:
   tensorboard_dir: ./logs/llama32_1b
   # wandb_project: my_project  # Uncomment to enable W&B logging
   # wandb_entity: my_team
-  # mlflow_experiment: llama32_1b_pretrain
-  # mlflow_run_name: llama32_1b_pretrain_run
-  # mlflow_tags:
-  #   project: llama32
-  #   phase: pretrain
-  #   variant: mlflow_example
+  mlflow_experiment: llama32_1b_pretrain
+  mlflow_run_name: llama32_1b_pretrain_run
+  mlflow_tags:
+    project: llama32
+    phase: pretrain
+    variant: mlflow_example
 
 # Random seed
 rng:

From 346aa5bc5334dc592e82bde7bcf9b1b5533b5f38 Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Fri, 5 Dec 2025 20:10:21 +0000
Subject: [PATCH 07/13] added finalize function to validate mlflow installation

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 docs/training/logging.md                      |  4 +--
 src/megatron/bridge/training/config.py        | 26 +++++++++++++++++++
 .../training/utils/test_mlflow_utils.py       |  1 -
 3 files changed, 28 insertions(+), 3 deletions(-)

diff --git a/docs/training/logging.md b/docs/training/logging.md
index e22cd64afc..139f309a4f 100644
--- a/docs/training/logging.md
+++ b/docs/training/logging.md
@@ -168,10 +168,10 @@ When enabled, MLFlow receives:
 
 #### Enable MLFlow Logging
 
-  1) Install MLFlow (if not already available):
+  1) Install MLFlow (installed by default with Megatron Bridge):
 
   ```bash
-  pip install mlflow
+  pip install mlflow / uv add mlflow
   ```
 
   2) Configure the tracking server (Optional):
diff --git a/src/megatron/bridge/training/config.py b/src/megatron/bridge/training/config.py
index 1abb8fbde3..e707ef51ff 100644
--- a/src/megatron/bridge/training/config.py
+++ b/src/megatron/bridge/training/config.py
@@ -920,6 +920,31 @@ class LoggerConfig:
     save_config_filepath: Optional[str] = None
     """If set, save the task configuration (ConfigContainer) to this file."""
 
+    def finalize(self) -> None:
+        """Validate logger settings and optional MLFlow dependency."""
+        using_mlflow = any(
+            [
+                self.mlflow_experiment,
+                self.mlflow_run_name,
+                self.mlflow_tracking_uri,
+                self.mlflow_tags,
+            ]
+        )
+
+        if using_mlflow:
+            try:
+                import importlib
+
+                importlib.import_module("mlflow")
+            except ModuleNotFoundError as exc:
+                raise ModuleNotFoundError(
+                    "MLFlow logging is configured, but the 'mlflow' package is not installed. "
+                    "Install it via pip install mlflow or uv add mlflow"
+                ) from exc
+
+            if self.mlflow_experiment and (self.mlflow_run_name is None or self.mlflow_run_name == ""):
+                raise ValueError("Set logger.mlflow_run_name when enabling MLFlow logging.")
+
 
 @dataclass(kw_only=True)
 class ProfilingConfig:
@@ -1266,6 +1291,7 @@ def validate(self) -> None:
         if hasattr(self.model, "finalize"):
             self.model.finalize()
 
+        self.logger.finalize()
         self.train.finalize()
         self.scheduler.finalize()
         self.checkpoint.finalize()
diff --git a/tests/unit_tests/training/utils/test_mlflow_utils.py b/tests/unit_tests/training/utils/test_mlflow_utils.py
index b9ccbcd756..23ca7b4c83 100644
--- a/tests/unit_tests/training/utils/test_mlflow_utils.py
+++ b/tests/unit_tests/training/utils/test_mlflow_utils.py
@@ -294,4 +294,3 @@ def test_mixed_keys(self):
             "eval_accuracy": 0.9,
             "learning_rate": 0.001,
         }
-

From 4c21b4c3a6ccbc64ac2834aeaffe78d30ef3f9bd Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Fri, 5 Dec 2025 20:11:02 +0000
Subject: [PATCH 08/13] build: Adding dependencies

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 pyproject.toml |   1 +
 uv.lock        | 979 ++++++++++++++++++++++++++++++++++++++++++++++---
 2 files changed, 926 insertions(+), 54 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index b4cff354df..f316a2ac0f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -88,6 +88,7 @@ dependencies = [
     "flash-linear-attention",
     "timm",
     "open-clip-torch>=3.2.0",
+    "mlflow>=3.2.0",
 ]
 
 
diff --git a/uv.lock b/uv.lock
index 3dadccfc0e..d96bda064c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'linux'",
@@ -243,6 +243,21 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b", size = 13929, upload-time = "2024-07-26T18:15:02.05Z" },
 ]
 
+[[package]]
+name = "alembic"
+version = "1.17.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mako" },
+    { name = "sqlalchemy" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = "sha256:bbe9751705c5e0f14877f02d46c53d10885e377e3d90eda810a016f9baa19e8e", size = 1988064, upload-time = "2025-11-14T20:35:04.057Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ba/88/6237e97e3385b57b5f1528647addea5cc03d4d65d5979ab24327d41fb00d/alembic-1.17.2-py3-none-any.whl", hash = "sha256:f483dd1fe93f6c5d49217055e4d15b905b425b6af906746abb35b69c1996c4e6", size = 248554, upload-time = "2025-11-14T20:35:05.699Z" },
+]
+
 [[package]]
 name = "aniso8601"
 version = "10.0.1"
@@ -637,6 +652,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9d/2a/9186535ce58db529927f6cf5990a849aa9e052eea3e2cfefe20b9e1802da/bracex-2.6-py3-none-any.whl", hash = "sha256:0b0049264e7340b3ec782b5cb99beb325f36c3782a32e36e876452fd49a09952", size = 11508, upload-time = "2025-06-22T19:12:29.781Z" },
 ]
 
+[[package]]
+name = "cachetools"
+version = "6.2.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/fb/44/ca1675be2a83aeee1886ab745b28cda92093066590233cc501890eb8417a/cachetools-6.2.2.tar.gz", hash = "sha256:8e6d266b25e539df852251cfd6f990b4bc3a141db73b939058d809ebd2590fc6", size = 31571, upload-time = "2025-11-13T17:42:51.465Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/e6/46/eb6eca305c77a4489affe1c5d8f4cae82f285d9addd8de4ec084a7184221/cachetools-6.2.2-py3-none-any.whl", hash = "sha256:6c09c98183bf58560c97b2abfcedcbaf6a896a490f534b031b661d3723b45ace", size = 11503, upload-time = "2025-11-13T17:42:50.232Z" },
+]
+
 [[package]]
 name = "catalogue"
 version = "2.0.10"
@@ -885,6 +909,169 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/76/56/6d6872f79d14c0cb02f1646cbb4592eef935857c0951a105874b7b62a0c3/contextlib2-21.6.0-py2.py3-none-any.whl", hash = "sha256:3fbdb64466afd23abaf6c977627b75b6139a5a3e8ce38405c5b413aed7a0471f", size = 13277, upload-time = "2021-06-27T06:54:20.972Z" },
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.2"
+source = { registry = "https://pypi.org/simple" }
+resolution-markers = [
+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux'",
+]
+dependencies = [
+    { name = "numpy", marker = "python_full_version < '3.11'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/12/a3/da4153ec8fe25d263aa48c1a4cbde7f49b59af86f0b6f7862788c60da737/contourpy-1.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ba38e3f9f330af820c4b27ceb4b9c7feee5fe0493ea53a8720f4792667465934", size = 268551, upload-time = "2025-04-15T17:34:46.581Z" },
+    { url = "https://files.pythonhosted.org/packages/2f/6c/330de89ae1087eb622bfca0177d32a7ece50c3ef07b28002de4757d9d875/contourpy-1.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:dc41ba0714aa2968d1f8674ec97504a8f7e334f48eeacebcaa6256213acb0989", size = 253399, upload-time = "2025-04-15T17:34:51.427Z" },
+    { url = "https://files.pythonhosted.org/packages/c1/bd/20c6726b1b7f81a8bee5271bed5c165f0a8e1f572578a9d27e2ccb763cb2/contourpy-1.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9be002b31c558d1ddf1b9b415b162c603405414bacd6932d031c5b5a8b757f0d", size = 312061, upload-time = "2025-04-15T17:34:55.961Z" },
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 version = "0.7.0"
@@ -2221,6 +2808,7 @@ dependencies = [
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     { name = "mamba-ssm" },
     { name = "megatron-core", extra = ["dev", "mlm"] },
+    { name = "mlflow" },
     { name = "nvidia-resiliency-ext" },
     { name = "omegaconf" },
     { name = "open-clip-torch" },
@@ -2293,6 +2881,7 @@ requires-dist = [
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     { name = "mamba-ssm" },
     { name = "megatron-core", extras = ["dev", "mlm"], directory = "3rdparty/Megatron-LM" },
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 version = "0.49.3"
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From 492e7bf93c0a8bb1a72d9c6754257876d1191525 Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Fri, 5 Dec 2025 16:43:28 -0500
Subject: [PATCH 09/13] Disable MLflow settings in llama32_1b_pretrain.yaml

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 .../recipes/llama/conf/llama32_1b_pretrain.yaml      | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml b/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
index f6f9ee17f5..1e1c557237 100644
--- a/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
+++ b/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
@@ -62,12 +62,12 @@ logger:
   tensorboard_dir: ./logs/llama32_1b
   # wandb_project: my_project  # Uncomment to enable W&B logging
   # wandb_entity: my_team
-  mlflow_experiment: llama32_1b_pretrain
-  mlflow_run_name: llama32_1b_pretrain_run
-  mlflow_tags:
-    project: llama32
-    phase: pretrain
-    variant: mlflow_example
+  # mlflow_experiment: llama32_1b_pretrain
+  # mlflow_run_name: llama32_1b_pretrain_run
+  # mlflow_tags:
+  #  project: llama32
+  #  phase: pretrain
+  #  variant: mlflow_example
 
 # Random seed
 rng:

From b04184f854e8935aa6997be6b40269ade20b2161 Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Sat, 6 Dec 2025 01:03:27 +0000
Subject: [PATCH 10/13] added copyright info to mlflow_utils

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 src/megatron/bridge/training/utils/mlflow_utils.py | 14 ++++++++++++++
 .../recipes/llama/conf/llama32_1b_finetune.yaml    |  6 ++++--
 .../recipes/llama/conf/llama32_1b_pretrain.yaml    | 13 +++++++------
 3 files changed, 25 insertions(+), 8 deletions(-)

diff --git a/src/megatron/bridge/training/utils/mlflow_utils.py b/src/megatron/bridge/training/utils/mlflow_utils.py
index c86c487b31..b05eb32406 100644
--- a/src/megatron/bridge/training/utils/mlflow_utils.py
+++ b/src/megatron/bridge/training/utils/mlflow_utils.py
@@ -1,3 +1,17 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from pathlib import Path
 from typing import Any, Optional
 
diff --git a/tutorials/recipes/llama/conf/llama32_1b_finetune.yaml b/tutorials/recipes/llama/conf/llama32_1b_finetune.yaml
index 3617c712b9..9bfb0db98d 100644
--- a/tutorials/recipes/llama/conf/llama32_1b_finetune.yaml
+++ b/tutorials/recipes/llama/conf/llama32_1b_finetune.yaml
@@ -72,12 +72,14 @@ logger:
   tensorboard_dir: ./logs/llama32_1b_finetuned
   # wandb_project: my_finetune_project
   # wandb_entity: my_team
-  # mlflow_experiment: llama32_1b_finetuned
-  # mlflow_run_name: llama32_1b_finetuned_run
+  # mlflow_experiment: llama32_1b_finetuned  # Uncomment to enable MLFlow logging
+  # mlflow_run_name: llama32_1b_finetuned
   # mlflow_tags:
   #   project: llama32
   #   phase: finetune
   #   variant: mlflow_example
+  #   mlflow_tracking_uri: http://localhost:5000  # Optional: use remote MLflow server
+
 
 
 # Random seed
diff --git a/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml b/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
index 1e1c557237..7a8a9a2c47 100644
--- a/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
+++ b/tutorials/recipes/llama/conf/llama32_1b_pretrain.yaml
@@ -25,8 +25,8 @@ dataset:
 # Training configuration
 train:
   train_iters: 100
-  global_batch_size: 256
-  micro_batch_size: 2
+  global_batch_size: 8
+  micro_batch_size: 1
   eval_iters: 10
   eval_interval: 50
 
@@ -62,12 +62,13 @@ logger:
   tensorboard_dir: ./logs/llama32_1b
   # wandb_project: my_project  # Uncomment to enable W&B logging
   # wandb_entity: my_team
-  # mlflow_experiment: llama32_1b_pretrain
+  # mlflow_experiment: llama32_1b_pretrain  # Uncomment to enable MLFlow logging
   # mlflow_run_name: llama32_1b_pretrain_run
   # mlflow_tags:
-  #  project: llama32
-  #  phase: pretrain
-  #  variant: mlflow_example
+  #   project: llama32
+  #   phase: pretrain
+  #   variant: mlflow_example
+  #   mlflow_tracking_uri: http://localhost:5000  # Optional: use remote MLflow server
 
 # Random seed
 rng:

From b0d5c15458632dbb063b2672f552f2577a903fd6 Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Tue, 9 Dec 2025 16:38:19 +0000
Subject: [PATCH 11/13] fixes: removed warn_rank_0, used get_checkpoint_name
 for consistency

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 src/megatron/bridge/training/state.py              | 6 +++---
 src/megatron/bridge/training/utils/mlflow_utils.py | 4 +++-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/megatron/bridge/training/state.py b/src/megatron/bridge/training/state.py
index 0d4d65d25b..9fc4191e87 100644
--- a/src/megatron/bridge/training/state.py
+++ b/src/megatron/bridge/training/state.py
@@ -32,7 +32,7 @@
 from megatron.bridge.training.tokenizers.tokenizer import build_tokenizer
 from megatron.bridge.training.utils.log_utils import safe_serialize
 from megatron.bridge.training.utils.sig_utils import DistributedSignalHandler
-from megatron.bridge.utils.common_utils import get_rank_safe, get_world_size_safe, warn_rank_0
+from megatron.bridge.utils.common_utils import get_rank_safe, get_world_size_safe
 
 
 @dataclass
@@ -444,5 +444,5 @@ def _timers_write_to_mlflow(
         try:
             logger.log_metrics(metrics, step=iteration)
         except Exception:
-            # continue training
-            warn_rank_0("Failed to log timer metrics to MLFlow; continuing without timer metrics.")
+            import warnings
+            warnings.warn("Failed to log timer metrics to MLFlow; continuing without timer metrics.")
\ No newline at end of file
diff --git a/src/megatron/bridge/training/utils/mlflow_utils.py b/src/megatron/bridge/training/utils/mlflow_utils.py
index b05eb32406..9d6a678eb5 100644
--- a/src/megatron/bridge/training/utils/mlflow_utils.py
+++ b/src/megatron/bridge/training/utils/mlflow_utils.py
@@ -15,6 +15,7 @@
 from pathlib import Path
 from typing import Any, Optional
 
+from megatron.bridge.training.utils.checkpoint_utils import get_checkpoint_name
 from megatron.bridge.utils.common_utils import print_rank_last
 
 
@@ -42,7 +43,8 @@ def on_save_checkpoint_success(
     try:
         checkpoint_path = str(Path(checkpoint_path).resolve())
         base_name = Path(save_dir).name or "checkpoints"
-        artifact_subdir = f"{base_name}/iter_{iteration:07d}"
+        expected_ckpt_path = get_checkpoint_name(save_dir, iteration)
+        artifact_subdir = f"{base_name}/{Path(expected_ckpt_path).name}"
         mlflow_logger.log_artifacts(checkpoint_path, artifact_path=artifact_subdir)
     except Exception as exc:
         # continue training

From b51de4062af316f4ca6f24d2e8037b772fa57f2c Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Tue, 9 Dec 2025 16:39:37 +0000
Subject: [PATCH 12/13] ruff formatting

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 src/megatron/bridge/training/state.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/megatron/bridge/training/state.py b/src/megatron/bridge/training/state.py
index 9fc4191e87..44178d3a3d 100644
--- a/src/megatron/bridge/training/state.py
+++ b/src/megatron/bridge/training/state.py
@@ -445,4 +445,5 @@ def _timers_write_to_mlflow(
             logger.log_metrics(metrics, step=iteration)
         except Exception:
             import warnings
-            warnings.warn("Failed to log timer metrics to MLFlow; continuing without timer metrics.")
\ No newline at end of file
+
+            warnings.warn("Failed to log timer metrics to MLFlow; continuing without timer metrics.")

From e07db35ef8141ed90073e7323ce045129612391b Mon Sep 17 00:00:00 2001
From: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
Date: Sun, 18 Jan 2026 14:13:31 +0530
Subject: [PATCH 13/13] added max-attention-logit param

Signed-off-by: Naveenraj Kamalakannan <therealnaveenkamal@gmail.com>
---
 src/megatron/bridge/training/utils/train_utils.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/megatron/bridge/training/utils/train_utils.py b/src/megatron/bridge/training/utils/train_utils.py
index aff67f071f..9155a7fbef 100644
--- a/src/megatron/bridge/training/utils/train_utils.py
+++ b/src/megatron/bridge/training/utils/train_utils.py
@@ -586,6 +586,8 @@ def training_log(
             )
             if wandb_writer:
                 wandb_writer.log({"max-attention-logit": log_max_attention_logit}, iteration)
+            if mlflow_logger:
+                mlflow_logger.log_metrics({"max-attention-logit": log_max_attention_logit}, step=iteration)
 
     if config.model.num_moe_experts is not None:
         moe_loss_scale = 1 / get_num_microbatches()