Experiment Reporting

EOLE supports multiple reporting backends during training:

• stdout (always enabled)
• TensorBoard (optional)
• Trackio (optional)

Stdout Reporting

Stdout reporting writes periodic training metrics directly to the process logs and is always enabled.

Use report_every to control the step interval for these logs.

report_every: 100

TensorBoard Reporting

TensorBoard is a local dashboard for visualizing scalar metrics over time.

Enable TensorBoard in your train config with:

tensorboard: true
tensorboard_log_dir: runs/eole

When enabled, EOLE creates a dated run directory under tensorboard_log_dir and logs training/validation metrics.

To visualize logs:

tensorboard --logdir runs/eole

Trackio Reporting

Trackio is an experiment tracking backend for local-first logging and optional remote dashboards.

Trackio is an optional dependency:

pip install -e ".[trackio]"

Enable Trackio in your train config:

trackio: true
trackio_project: eole
trackio_run_name: my-run-name  # optional
trackio_space_id: username/space_id  # optional
trackio_bucket_id: my-bucket-id  # optional

Notes:

• trackio_run_name: optional run name (auto-generated if omitted)
• trackio_space_id: optional Hugging Face Trackio Space target
• trackio_bucket_id: optional Hugging Face bucket for persistence

If available, EOLE also logs model and training configuration to Trackio's run config.

At runtime, the following failure behavior happens:

• If Trackio is not installed, EOLE logs a warning and continues training.
• If Trackio initialization fails, EOLE logs a warning and continues training.
• If Trackio raises during runtime logging, EOLE logs a warning once and disables Trackio for the rest of the run.

Distributed behavior

In distributed training, TensorBoard and Trackio are initialized on rank 0 (gpu_rank <= 0) only, to avoid duplicate logging.

Config snippet example

report_every: 100

tensorboard: true
tensorboard_log_dir: runs/eole

trackio: true
trackio_project: eole
trackio_run_name: exp-001

Extending with a Custom Report Backend

For advanced integrations, implement your own manager by subclassing ReportMgrBase from eole.utils.report_manager and then compose it with CompositeReportMgr.

At minimum, implement:

• _report_training(...) for step-level periodic logging
• _report_step(...) for end-of-step train/valid summaries
• close() if your backend owns external resources

If you instantiate a manager directly, call start() before the first report_training(...) call so start_time is initialized.

Example sketch:

from eole.utils.report_manager import CompositeReportMgr, ReportMgrBase, StdoutReportMgr


class MyBackendReportMgr(ReportMgrBase):
    def _report_training(self, step, num_steps, learning_rate, patience, report_stats):
        # send training metrics to your backend
        pass

    def _report_step(self, lr, patience, step, valid_stats=None, train_stats=None):
        # send step summary to your backend
        pass


report_mgr = CompositeReportMgr(
    report_every=100,
    managers=[
        StdoutReportMgr(report_every=100),
        MyBackendReportMgr(report_every=100),
    ],
)