Training Parameters

Scenario models use the same TrainingParams class as the foundation model, but with scenario-specific behavior. For the complete field-level reference, see Foundation Model Training Parameters.

from monad.config import TrainingParams, MetricParams, EarlyStopping

Key Differences from Foundation Model

Feature	Foundation Model	Scenario Model
Custom metrics	Not supported	Supported via `metrics` parameter
`checkpoint_dir`	Set in YAML	Commonly set in `TrainingParams`
`metric_to_monitor`	Not used	Select best model by custom metric
Typical epochs	1	3-10+

Commonly Used Parameters

training_params = TrainingParams(
    epochs=5,
    learning_rate=1e-4,
    devices=[0],
    checkpoint_dir=Path("./my_scenario_model"),
    metrics=[
        MetricParams(alias="auroc", metric_name="AUROC"),
    ],
    metric_to_monitor="auroc",
    metric_monitoring_mode="max",
    early_stopping=EarlyStopping(patience=5, min_delta=0.001),
)

module.fit(training_params)

Available Metrics by Task Type

Binary Classification

Metric Name	Description
`AUROC`	Area Under the ROC Curve.
`AveragePrecision`	Area Under the Precision-Recall Curve.
`Recall`	True positive rate.
`Precision`	Positive predictive value.
`F1Score`	Harmonic mean of precision and recall.
`Accuracy`	Overall correctness.

metrics = [
    MetricParams(alias="auroc", metric_name="AUROC"),
    MetricParams(alias="avg_precision", metric_name="AveragePrecision"),
]

Multiple Targets Classification

Custom metrics from monad.metrics for tasks with multiple valid targets:

Metric Name	Description	Kwargs
`MultipleTargetsRecall`	Recall across multiple valid targets.	—
`MultipleTargetsRecallPerClass`	Per-class recall for multiple targets.	`{"num_classes": N}`

Multiclass Classification

Metric Name	Description	Kwargs
`Accuracy`	Overall correctness.	`{"task": "multiclass", "num_classes": N}`
`F1Score`	F1 with macro averaging.	`{"task": "multiclass", "num_classes": N, "average": "macro"}`

metrics = [
    MetricParams(
        alias="accuracy",
        metric_name="Accuracy",
        kwargs={"task": "multiclass", "num_classes": 3},
    ),
]

Multilabel Classification

Metric Name	Description	Kwargs
`F1Score`	F1 with micro averaging.	`{"task": "multilabel", "num_labels": N, "average": "micro"}`
`HammingDistance`	Fraction of incorrect labels.	`{"task": "multilabel", "num_labels": N}`

Regression

RegressionTask requires num_targets (number of regression outputs) and optionally max_value (upper bound for target normalization).

from monad.ui.module import RegressionTask

task = RegressionTask(num_targets=1, max_value=1000.0)

Metric Name	Description
`MeanSquaredError`	Mean Squared Error (use for RMSE via post-processing).
`MeanAbsoluteError`	Mean Absolute Error.
`R2Score`	Coefficient of determination.

metrics = [
    MetricParams(alias="mse", metric_name="MeanSquaredError"),
    MetricParams(alias="mae", metric_name="MeanAbsoluteError"),
]

Recommendation

Two task types are available:

RecommendationTask — sketch-based recommendation for high-cardinality item catalogs.
OneHotRecommendationTask — one-hot recommendation for low-cardinality item sets.

Metric Name	Description	Kwargs
`HitRateAtK`	Hit rate at top-k.	`{"k": 10}`
`MeanAveragePrecisionAtK`	MAP at top-k.	`{"k": 10}`
`NDCGAtK`	Normalized Discounted Cumulative Gain at top-k.	`{"k": 10}`
`PrecisionAtK`	Precision at top-k.	`{"k": 10}`
`MeanReciprocalRank`	Mean reciprocal rank of the first relevant item.	`{"k": 100}` (default)

metrics = [
    MetricParams(alias="hr10", metric_name="HitRateAtK", kwargs={"k": 10}),
    MetricParams(alias="ndcg10", metric_name="NDCGAtK", kwargs={"k": 10}),
]

Tip

Metrics are resolved by searching monad.metrics first, then torchmetrics. You can use any metric from either library.

`MetricParams`

Defines a metric by name from monad.metrics or torchmetrics.

Parameter	Type	Default	Description
`alias`	`str`	required	Unique alias to identify the metric. Used in logs and for `metric_to_monitor`.
`metric_name`	`str`	required	Name of the metric class from `monad.metrics` or `torchmetrics`.
`kwargs`	`dict[str, Any]`	`{}`	Arguments passed to the metric constructor.

`CustomMetric`

Allows passing a pre-instantiated torchmetrics.Metric object directly, instead of resolving by name via MetricParams.

from monad.config import CustomMetric
from torchmetrics.classification import BinaryAUROC

metrics = [
    CustomMetric(alias="my_auroc", metric=BinaryAUROC()),
]

Parameter	Type	Default	Description
`alias`	`str`	required	Unique alias to identify the metric. Used in logs and for `metric_to_monitor`.
`metric`	`torchmetrics.Metric`	required	A pre-instantiated `torchmetrics.Metric` instance.

Multi-GPU Training

For larger datasets, use distributed training strategies:

training_params = TrainingParams(
    epochs=5,
    learning_rate=1e-4,
    devices=[0, 1, 2, 3],
    strategy="ddp",
    precision="bf16-mixed",
    checkpoint_dir=Path("./my_model"),
)

Note

"ddp" and "fsdp" strategies require multiple devices. When using "fsdp:A:B" (custom FSDP), you need at least A * B devices.