Nemo Mbridge Resiliency
NVIDIAOfficialPublished by NVIDIA
- Skill
- Tool integration
- Software development
- Apache-2.0
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides configuration and implementation guidance for resiliency features within the Megatron Bridge framework. It covers mechanisms to maintain training stability in large-scale distributed environments, including automated fault tolerance, straggler detection, preemption handling, and experimental in-process restart capabilities. The documentation outlines how to integrate these features via NeMo Run plugins or direct configuration, along with specific requirements for environment variables and launcher utilities.
What it does
- Enables fault tolerance with automated heartbeat monitoring and job restarts.
- Detects performance degradation across GPUs using NVRx straggler detection.
- Configures preemption handling for Slurm-based environments.
- Supports experimental in-process restart and re-run state machine logic.
- Optimizes checkpointing through asynchronous saving and local storage options.
When to use it
- When setting up training jobs that require high fault tolerance in distributed clusters.
- When investigating training hangs or performance bottlenecks caused by GPU stragglers.
- When configuring training to handle preemption signals gracefully.
- When implementing advanced recovery strategies like in-process restarts for long-running jobs.
Advantages
- Reduces manual intervention for common training failures.
- Provides granular control over performance monitoring and timeout thresholds.
- Supports both plugin-based and direct configuration workflows.
- Includes built-in fault simulation for testing resiliency configurations.
Usage examples
- “How do I enable fault tolerance in my NeMo Run training script?”
- You can import the FaultTolerancePlugin from megatron.bridge.recipes.run_plugins and add it to your run.run() call, specifying parameters like num_in_job_restarts and heartbeat timeouts.
- “How can I detect slow GPUs during training?”
- Configure the NVRxStragglerDetectionConfig in your training setup, setting enabled=True and adjusting the performance thresholds to monitor and report GPU degradation.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/nvidia/nemo-mbridge-resiliency/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nemo-mbridge-resiliency.zip -d .agents/skills
Documentation by NVIDIA
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 FaultTolerancePlugin
import 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)| Plugin parameter | Default | Description |
|---|---|---|
num_in_job_restarts | 3 | Max restarts within same job |
num_job_retries_on_failure | 2 | Max new job launches on failure |
initial_rank_heartbeat_timeout | 1800 | First heartbeat timeout (seconds) |
rank_heartbeat_timeout | 300 | Subsequent heartbeat timeout (seconds) |
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-Slurm
ft_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| Config parameter | Default | Description |
|---|---|---|
enable_ft_package | False | Enable fault tolerance |
calc_ft_timeouts | False | Auto-compute optimal timeouts |
simulate_fault | False | Enable fault simulation for testing |
simulated_fault_type | "random" | "rank_hung", "rank_killed", or "random" |
simulated_fault_rank | None | Specific rank to fault (random if None) |
simulated_fault_base_delay | 0 | Base delay before simulating fault |
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,
)| Parameter | Default | Description |
|---|---|---|
enabled | False | Enable straggler detection |
report_time_interval | 300.0 | Seconds between straggler checks |
calc_relative_gpu_perf | True | Compare ranks against each other |
calc_individual_gpu_perf | True | Track per-rank degradation over time |
gpu_relative_perf_threshold | 0.7 | Threshold for relative performance (0-1) |
gpu_individual_perf_threshold | 0.7 | Threshold for individual performance (0-1) |
stop_if_detected | False | Terminate training on straggler |
num_gpu_perf_scores_to_print | 5 | Number of best/worst scores to print |
profiling_interval | 1 | Profiling interval for detector |
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,
)
]| Plugin parameter | Default | Description |
|---|---|---|
preempt_time | 60 | Seconds before job limit to send signal |
enable_exit_handler | True | Enable signal handler in training |
enable_exit_handler_for_data_loader | False | Enable for dataloader workers |
Direct config
import signal
cfg.train.exit_signal_handler = True
cfg.train.exit_signal = signal.SIGTERM
cfg.train.exit_signal_handler_for_dataloader = FalseRe-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,
)| Parameter | Default | Description |
|---|---|---|
rerun_mode | "disabled" | "disabled", "validate_results", "report_determinism_stats" |
check_for_nan_in_loss | True | Check for NaN in loss |
check_for_spiky_loss | False | Check for unexpectedly large loss |
spiky_loss_factor | 10.0 | Loss flagged if > factor * max observed (increase for large models) |
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,
)| Parameter | Default | Description |
|---|---|---|
enabled | False | Enable in-process restart |
active_world_size | None | Ranks executing workload (rest are warm reserves) |
granularity | "node" | "node" or "rank" restart granularity |
max_iterations | None | Max restart attempts (None = unlimited) |
soft_timeout | 60.0 | Detect GIL-released hangs (seconds) |
hard_timeout | 90.0 | Force-terminate hung ranks (seconds) |
heartbeat_interval | 30.0 | Heartbeat interval (seconds) |
heartbeat_timeout | 60.0 | Missing heartbeat timeout (seconds) |
barrier_timeout | 120.0 | Distributed barrier timeout (seconds) |
completion_timeout | 120.0 | Completion barrier timeout (seconds) |
empty_cuda_cache | True | Clear CUDA cache during restart |
max_rank_faults | None | Max rank faults before terminating |
monitor_process_logdir | None | Directory for monitor logs |
Required environment variables:
export TORCH_CPP_LOG_LEVEL=error
export TORCH_NCCL_RETHROW_CUDA_ERRORS=0
export NCCL_NVLS_ENABLE=0The 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 = True
cfg.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—FaultToleranceConfigRuntime:
src/megatron/bridge/training/fault_tolerance.pyPlugin:
src/megatron/bridge/recipes/run_plugins.py—FaultTolerancePluginPerf plugin:
scripts/performance/nemo-mbridge-resiliency_plugins.pyTests:
tests/unit_tests/training/test_fault_tolerance.pyExample:
examples/training_features/nemo-mbridge-resiliency/fault_tolerance/
Straggler detection
Config:
src/megatron/bridge/training/config.py—NVRxStragglerDetectionConfigRuntime:
src/megatron/bridge/training/nvrx_straggler.pyTrain loop:
src/megatron/bridge/training/train.py—check_nvrx_straggler_detectionTests:
tests/unit_tests/training/test_nvrx_straggler.py,tests/functional_tests/training/test_nvrx_straggler.pyExample:
examples/training_features/nemo-mbridge-resiliency/straggler_detection/
In-process restart
Config:
src/megatron/bridge/training/config.py—InProcessRestartConfigRuntime:
src/megatron/bridge/training/inprocess_restart.pyEntry point:
src/megatron/bridge/training/pretrain.py—maybe_wrap_for_inprocess_restartTests:
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—PreemptionPluginSignal handler:
src/megatron/bridge/training/utils/sig_utils.pyTests:
tests/unit_tests/recipes/test_run_plugins.py
Re-run state machine
Config:
src/megatron/bridge/training/config.py—RerunStateMachineConfigInit:
src/megatron/bridge/training/initialize.py—init_rerun_state
Checkpointing
Async save:
src/megatron/bridge/training/checkpointing.py—schedule_async_saveLocal ckpt:
src/megatron/bridge/training/checkpointing.py—LocalCheckpointManagerTests:
tests/functional_tests/training/test_local_checkpointing.py
Pitfalls
ft_launcher, not torchrun: Direct
FaultToleranceConfigrequiresft_launcher. Usingtorchrunsilently disables FT. For non-Slurm, setGROUP_RANK=0.Async save requires torch_dist:
async_save=Trueonly works withckpt_format="torch_dist". Other formats silently fail or error.IPR + NeMo-Run: In-process restart is not compatible with NeMo-Run or Slurm preemption plugins. Requires specific PyTorch/NCCL versions and env vars.
NVRx vs legacy straggler: Two detectors exist. Use NVRx (
nvrx_straggler); do not enable both.stop_if_detected default: NVRx logs but does not stop training by default. Set
stop_if_detected=Truefor automatic termination.NCCL watchdog vs hard_timeout: For IPR, NCCL watchdog timeout must exceed
hard_timeoutor PyTorch kills the process before recovery.Rerun state machine is alpha: Use
check_for_nan_in_loss=Truefor 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-faultLook 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.pyLook 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 -vRequires compatible PyTorch/NCCL versions.
Files
- ATTRIBUTION.mdAdded by AutoGineer845 B
- BENCHMARK.md4.2 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md11.1 KiB
- card.yaml4.8 KiB
- evals/evals.json1.3 KiB
- skill-card.md3.8 KiB
- skill.oms.sig4.7 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs Python, Needs Node.js
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Attribution and licence
- Published by
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- NVIDIA (Official)
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- NVIDIA/skills · skills/nemo-mbridge-resiliency · commit fce3cca
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- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
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