Nemo Mbridge Resiliency

Resiliency

Stable docs: @docs/training/resiliency.md, @docs/training/checkpointing.md Card: @skills/nemo-mbridge-resiliency/card.yaml

Enablement

Fault tolerance (Slurm only)

Option 1: NeMo Run plugin (recommended)

from megatron.bridge.recipes.run_plugins import FaultTolerancePluginimport nemo_run as run
task = run.Script(...)run_plugins = [    FaultTolerancePlugin(        enable_ft_package=True,        calc_ft_timeouts=True,        num_in_job_restarts=3,        num_job_retries_on_failure=2,        initial_rank_heartbeat_timeout=1800,        rank_heartbeat_timeout=300,    )]run.run(task, plugins=run_plugins, executor=executor)

Option 2: Direct config + ft_launcher

from megatron.bridge.training.config import FaultToleranceConfig
cfg.ft = FaultToleranceConfig(    enable_ft_package=True,    calc_ft_timeouts=True,    simulate_fault=False,    simulated_fault_type="random",)

Launch with ft_launcher (not torchrun):

export GROUP_RANK=0  # required for non-Slurmft_launcher \    --rdzv_backend=c10d --rdzv_endpoint=${MASTER_ADDR}:${MASTER_PORT} \    --nnodes=${NUM_NODES} --nproc-per-node=${NUM_GPUS_PER_NODE} \    --ft-rank_section_timeouts=setup:600,step:180,checkpointing:420 \    --ft-rank_out_of_section_timeout=300 \    your_training_script.py

Section-based timeout monitoring covers setup, training steps, checkpointing, and out-of-section time independently. Timeouts are saved to ft_state.json for subsequent runs when calc_ft_timeouts=True.

NVRx straggler detection

from megatron.bridge.training.config import NVRxStragglerDetectionConfig
cfg.nvrx_straggler = NVRxStragglerDetectionConfig(    enabled=True,    report_time_interval=300.0,    calc_relative_gpu_perf=True,    calc_individual_gpu_perf=True,    num_gpu_perf_scores_to_print=5,    gpu_relative_perf_threshold=0.7,    gpu_individual_perf_threshold=0.7,    stop_if_detected=False,    enable_logging=True,)

Preemption

Plugin (Slurm)

from megatron.bridge.recipes.run_plugins import PreemptionPlugin
plugins = [    PreemptionPlugin(        preempt_time=60,        enable_exit_handler=True,        enable_exit_handler_for_data_loader=False,    )]

Direct config

import signalcfg.train.exit_signal_handler = Truecfg.train.exit_signal = signal.SIGTERMcfg.train.exit_signal_handler_for_dataloader = False

Re-run state machine (experimental)

from megatron.bridge.training.config import RerunStateMachineConfig
cfg.rerun_state_machine = RerunStateMachineConfig(    rerun_mode="validate_results",    check_for_nan_in_loss=True,    check_for_spiky_loss=False,    spiky_loss_factor=10.0,)

Exit codes: 16 = resume to disambiguate, 17 = failed validation.

In-process restart (experimental)

from megatron.bridge.training.config import InProcessRestartConfig
cfg.inprocess_restart = InProcessRestartConfig(    enabled=True,    granularity="node",    soft_timeout=60.0,    hard_timeout=90.0,)

Required environment variables:

export TORCH_CPP_LOG_LEVEL=errorexport TORCH_NCCL_RETHROW_CUDA_ERRORS=0export NCCL_NVLS_ENABLE=0

The PyTorch NCCL watchdog timeout must exceed hard_timeout. NeMo-Run's Slurm Executor is not supported; launch directly with srun --kill-on-bad-exit=0.

Async checkpoint save

cfg.checkpoint.async_save = Truecfg.checkpoint.ckpt_format = "torch_dist"

Local checkpointing (NVRx)

cfg.checkpoint.non_persistent_local_ckpt_dir = "/local/scratch/ckpt"cfg.checkpoint.non_persistent_local_ckpt_algo = "fully_parallel"

Code Anchors

Fault tolerance

• Config: src/megatron/bridge/training/config.py — FaultToleranceConfig
• Runtime: src/megatron/bridge/training/fault_tolerance.py
• Plugin: src/megatron/bridge/recipes/run_plugins.py — FaultTolerancePlugin
• Perf plugin: scripts/performance/nemo-mbridge-resiliency_plugins.py
• Tests: tests/unit_tests/training/test_fault_tolerance.py
• Example: examples/training_features/nemo-mbridge-resiliency/fault_tolerance/

Straggler detection

• Config: src/megatron/bridge/training/config.py — NVRxStragglerDetectionConfig
• Runtime: src/megatron/bridge/training/nvrx_straggler.py
• Train loop: src/megatron/bridge/training/train.py — check_nvrx_straggler_detection
• Tests: tests/unit_tests/training/test_nvrx_straggler.py, tests/functional_tests/training/test_nvrx_straggler.py
• Example: examples/training_features/nemo-mbridge-resiliency/straggler_detection/

In-process restart

• Config: src/megatron/bridge/training/config.py — InProcessRestartConfig
• Runtime: src/megatron/bridge/training/inprocess_restart.py
• Entry point: src/megatron/bridge/training/pretrain.py — maybe_wrap_for_inprocess_restart
• Tests: tests/unit_tests/training/test_inprocess_restart.py, tests/functional_tests/training/test_inprocess_restart.py

Preemption

• Plugin: src/megatron/bridge/recipes/run_plugins.py — PreemptionPlugin
• Signal handler: src/megatron/bridge/training/utils/sig_utils.py
• Tests: tests/unit_tests/recipes/test_run_plugins.py

Re-run state machine

• Config: src/megatron/bridge/training/config.py — RerunStateMachineConfig
• Init: src/megatron/bridge/training/initialize.py — init_rerun_state

Checkpointing

• Async save: src/megatron/bridge/training/checkpointing.py — schedule_async_save
• Local ckpt: src/megatron/bridge/training/checkpointing.py — LocalCheckpointManager
• Tests: tests/functional_tests/training/test_local_checkpointing.py

Pitfalls

1. ft_launcher, not torchrun: Direct FaultToleranceConfig requires ft_launcher. Using torchrun silently disables FT. For non-Slurm, set GROUP_RANK=0.

2. Async save requires torch_dist: async_save=True only works with ckpt_format="torch_dist". Other formats silently fail or error.

3. IPR + NeMo-Run: In-process restart is not compatible with NeMo-Run or Slurm preemption plugins. Requires specific PyTorch/NCCL versions and env vars.

4. NVRx vs legacy straggler: Two detectors exist. Use NVRx (nvrx_straggler); do not enable both.

5. stop_if_detected default: NVRx logs but does not stop training by default. Set stop_if_detected=True for automatic termination.

6. NCCL watchdog vs hard_timeout: For IPR, NCCL watchdog timeout must exceed hard_timeout or PyTorch kills the process before recovery.

7. Rerun state machine is alpha: Use check_for_nan_in_loss=True for NaN detection, but don't rely on full rerun workflows yet.

Verification

Fault tolerance

./examples/training_features/nemo-mbridge-resiliency/fault_tolerance/run_fault_tolerance.sh./examples/training_features/nemo-mbridge-resiliency/fault_tolerance/run_fault_tolerance.sh --simulate-fault

Look for [FaultTolerance] / [RankMonitorServer] log lines with section timeouts. Simulated fault should trigger restart from checkpoint.

Straggler detection

uv run python -m torch.distributed.run --nproc_per_node=2 \    examples/training_features/nemo-mbridge-resiliency/straggler_detection/straggler_detection_example.py

Look for GPU relative performance and GPU individual performance reports with per-rank scores.

Async checkpoint

Look for Scheduling async checkpoint save in logs. Training iterations should continue while checkpoint files are being written.

In-process restart

pytest tests/functional_tests/training/test_inprocess_restart.py -v

Requires compatible PyTorch/NCCL versions.