Monitoring and logging

This page summarizes how Primus configures application logging, experiment tracking (Weights & Biases, TensorBoard, MLflow), training metrics, profilers, ROCm memory probes, and how to capture a reproducible configuration snapshot.

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1. Primus logging system

Primus uses loguru for structured logging. Initialization wires file sinks (per log level) and a stderr sink, binds experiment and distributed context (team, user, exp, module_name, node_ip, rank, world_size), and installs an intercept handler so legacy logging output from frameworks such as Megatron is forwarded to loguru with consistent formatting.

Rank-aware behavior

• Worker processes write under {exp_root}/logs/{module_name}/rank-{rank}/ with separate rotated files for debug, info, warning, and error (subject to file_sink_level).

• The launcher master process can use logs/master/ when the master logger is configured with is_head=True.

Levels from module configuration (primus/configs/modules/module_base.yaml)

Parameter	Default	Role
sink_level	null	If set, overrides both file and stderr sink levels.
file_sink_level	DEBUG	Minimum level for file sinks when sink_level is unset.
stderr_sink_level	INFO	Minimum level for stderr when sink_level is unset.

init_worker_logger in primus/core/runtime/logging.py reads sink_level, file_sink_level, and stderr_sink_level from the merged module config. The Megatron trainer maps stderr_sink_level to Megatron’s numeric logging_level (the logging_level field in trainer_base.yaml is deprecated; this mapping supersedes it)…

Shell / runner environment (see docs/03-configuration-reference/environment-variables.md)

Variable	Purpose
PRIMUS_LOG_LEVEL	Runner verbosity: DEBUG, INFO, WARN, ERROR (default INFO).
PRIMUS_LOG_TIMESTAMP	1 enables timestamps on runner logs; 0 disables.
PRIMUS_LOG_COLOR	1 enables ANSI colors when appropriate; often 0 in non-TTY contexts.

CLI

• primus-cli --debug sets PRIMUS_LOG_LEVEL=DEBUG so launcher and shell logging are verbose (see docs/02-user-guide/cli-reference.md).

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2. Weights & Biases

Megatron

Configuration files: primus/configs/modules/megatron/trainer_base.yaml and primus_megatron_module.yaml.

Defaults in primus_megatron_module.yaml disable Weights & Biases; trainer fields in trainer_base.yaml supply names and paths when enabled.

Parameter	Default (module / trainer)	Description
disable_wandb	true (primus_megatron_module.yaml)	Master switch; when false, Primus sets paths and default project/run names from experiment metadata.
wandb_project	null	If unset when Weights & Biases is enabled, defaults to {work_group}_{user_name}.
wandb_exp_name	null	If unset, defaults to exp_name.
wandb_entity	null	Optional WandB entity/team.
wandb_save_dir	null	Deprecated in favor of {exp_root}; artifacts use {exp_root}/wandb.

Environment

• WANDB_API_KEY is required when Weights & Biases is enabled; Primus emits a warning if it is missing (primus/backends/megatron/patches/args/wandb_config_patches.py).

TorchTitan

Configuration file: primus/configs/modules/torchtitan/pre_trainer.yaml.

Parameter	Default	Description
metrics.enable_wandb	false	Enables WandB in the TorchTitan metrics stack.

When enabled, primus/backends/torchtitan/patches/wandb_patches.py can set WANDB_PROJECT and WANDB_RUN_NAME from Primus experiment metadata if unset. Use WANDB_API_KEY for authentication.

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3. TensorBoard

Megatron

Module toggles

Configuration file: primus_megatron_module.yaml

Parameter	Default	Description
disable_tensorboard	true	When false, TensorBoard output is placed under {exp_root}/tensorboard (Primus overrides deprecated tensorboard_dir with this path).

Trainer

Configuration file: trainer_base.yaml

Parameter	Default	Description
tensorboard_log_interval	1	Steps between TensorBoard writes.
tensorboard_queue_size	1000	Event file queue size.
log_timers_to_tensorboard	false	Log timer stats.
log_batch_size_to_tensorboard	false	Log batch size.
log_learning_rate_to_tensorboard	true	Log learning rate.
log_validation_ppl_to_tensorboard	false	Log validation perplexity.
log_memory_to_tensorboard	false	Log memory stats.
log_world_size_to_tensorboard	false	Log world size.
log_loss_scale_to_tensorboard	true	Log loss scale.
tensorboard_dir	null	Deprecated; Primus sets the directory under exp_root.

Note: Enabling Megatron profiling (profile: true) forces disable_tensorboard off in update_primus_config so TensorBoard is available for profile-related views.

TorchTitan

Configuration file: pre_trainer.yaml.

Parameter	Default	Description
metrics.enable_tensorboard	false	Enables TensorBoard logging.
metrics.save_tb_folder	tb	Subfolder name (typically under the job dump directory in TorchTitan layouts).

Launch TensorBoard locally

tensorboard --logdir <path-to-tensorboard-or-tb-folder>

Point <path> at the Megatron tensorboard directory under the experiment root, or at the TorchTitan metrics folder that contains the save_tb_folder subtree.

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4. MLflow

MLflow integration is Megatron-only in the paths described here.

Module

Configuration file: primus_megatron_module.yaml

Parameter	Default	Description
disable_mlflow	true	When false, MLflow run setup runs on the last global rank (world_size - 1).
mlflow_run_name	null	If unset when enabled, defaults to {work_group}_{user_name}.
mlflow_experiment_name	null	Passed to mlflow.set_experiment when set.

Startup behavior

Configuration file: primus/backends/megatron/training/global_vars.py

• Logs training args as parameters.

• Logs filtered environment variables with an env__ prefix.

• Collects git metadata, sets MLflow source tags, and writes system/git_metadata.json as a run artifact.

Environment (typical Databricks / hosted tracking)

Variable	Role
DATABRICKS_HOST	Checked by the Megatron trainer when MLflow is enabled; a warning is printed if unset.
DATABRICKS_TOKEN	Authentication for Databricks-hosted tracking (see environment reference).
MLFLOW_TRACKING_URI	Tracking server URI; optional depending on deployment.

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5. Training metrics

Megatron

Configuration file: trainer_base.yaml

Parameter	Default	Description
log_interval	100	Steps between standard training log lines.
log_throughput	false	Log throughput metrics.
log_avg_skip_iterations	2	Skip initial iterations when computing averages.
log_avg_reset_interval	10	Interval for resetting running averages.
log_params_norm	false	Log parameter norm.
log_num_zeros_in_grad	false	Log count of zero gradients.
log_progress	false	Progress-style logging.
timing_log_level	0	Timing log verbosity.
timing_log_option	minmax	Timing aggregation option.

TorchTitan

Configuration file: pre_trainer.yaml.

Parameter	Default	Description
metrics.log_freq	10	Metric logging frequency (steps).
metrics.disable_color_printing	false	Disable colored console metrics.
metrics.save_for_all_ranks	false	Save metrics from every rank vs. reduced ranks.

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6. Profiling

Megatron

Configuration files: trainer_base.yaml and primus_megatron_module.yaml

Parameter	Source	Default	Description
profile	trainer_base.yaml	false	Enables Megatron profiling path; also forces TensorBoard on when true.
use_pytorch_profiler	trainer_base.yaml	false	Use PyTorch profiler integration.
profile_ranks	trainer_base.yaml	[0]	Ranks to profile.
profile_step_start	trainer_base.yaml	10	First step to profile.
profile_step_end	trainer_base.yaml	12	Last step to profile.
record_memory_history	trainer_base.yaml	false	Record memory history.
memory_snapshot_path	trainer_base.yaml	snapshot.pickle	Memory snapshot file name.
disable_profiler_activity_cpu	primus_megatron_module.yaml	false	Disable CPU activities in the profiler.
torch_profiler_record_shapes	primus_megatron_module.yaml	true	Record tensor shapes.
torch_profiler_with_stack	primus_megatron_module.yaml	true	Capture Python stacks.
torch_profiler_use_gzip	primus_megatron_module.yaml	false	Gzip profiler traces.

TorchTitan

Configuration file: pre_trainer.yaml.

Parameter	Default	Description
profiling.enable_profiling	false	Master profiling toggle.
profiling.profile_freq	10	How often to capture traces.
profiling.enable_memory_snapshot	false	Enable memory snapshots.
profiling.save_memory_snapshot_folder	memory_snapshot	Output folder for snapshots.
profiling.save_traces_folder	profile_traces	Folder for profiler traces.

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7. ROCm memory monitoring

Configured in primus/configs/modules/megatron/primus_megatron_module.yaml and applied in the Megatron trainer when logging throughput.

Parameter	Default	Description
use_rocm_mem_info	false	When true, collect ROCm memory information via rocm-smi on every iteration that hits the throughput logging branch.
use_rocm_mem_info_iters	[1, 2]	When use_rocm_mem_info is false, rocm-smi runs only on these iteration numbers (same branch).

Collection is evaluated where log_throughput drives the extended iteration log (see primus/backends/megatron/patches/training_log/print_rank_last_patches.py): enable log_throughput in trainer_base.yaml (or overrides) when you need ROCm memory lines in the training log.

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8. Experiment snapshots

On disk (every run)

• Experiment root: {workspace}/{work_group}/{user_name}/{exp_name} is created at config load time (PrimusConfig).

• Per-rank logs: {exp_root}/logs/{module_name}/rank-{rank}/ with rotated level-specific files.

• Checkpoints: Megatron uses {exp_root}/checkpoints (trainer sets save to this path).

• TensorBoard / WandB: Under exp_root as described above when those features are enabled.

MLflow (Megatron, when enabled): Parameters, environment snapshot, and git metadata artifact provide a structured record of the run configuration and repository state.

Resolved configuration

The launcher and parser accept --export_config, but the default core training path (primus/cli/subcommands/train.py into PrimusRuntime) does not currently write a resolved YAML file. Archive the submitted experiment YAML, any referenced presets, launcher config, and runtime logs with each run. Treat resolved-config export as a legacy or future capability unless your deployment has implemented it on the core runtime path.