Documentation Index
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from onyxengine.modeling import TrainingConfig
config = TrainingConfig(
training_iters: int = 3000,
train_batch_size: int = 32,
train_val_split_ratio: float = 0.9,
test_dataset_size: int = 500,
checkpoint_type: Literal['single_step', 'multi_step'] = 'single_step',
optimizer: Union[AdamWConfig, SGDConfig] = AdamWConfig(),
lr_scheduler: Union[None, CosineDecayWithWarmupConfig, CosineAnnealingWarmRestartsConfig] = None
)
Parameters
Total number of training iterations. Range: 1-100,000.
Number of samples per training batch. Minimum: 1.
Fraction of data for training (rest is validation). Range: 0.0-1.0.
Number of samples reserved for test visualization. Minimum: 1.
checkpoint_type
Literal
default:"single_step"
What to optimize for:
'single_step': Best one-step-ahead prediction'multi_step': Best trajectory simulation
optimizer
Union[AdamWConfig, SGDConfig]
default:"AdamWConfig()"
Optimizer configuration.
lr_scheduler
Union[None, CosineDecayWithWarmupConfig, CosineAnnealingWarmRestartsConfig]
default:"None"
Learning rate scheduler configuration. None for constant learning rate.
Example
Basic Configuration
from onyxengine.modeling import TrainingConfig
config = TrainingConfig(
training_iters=2000,
train_batch_size=256,
checkpoint_type='single_step'
)
With Optimizer and Scheduler
from onyxengine.modeling import (
TrainingConfig,
AdamWConfig,
CosineDecayWithWarmupConfig
)
config = TrainingConfig(
training_iters=5000,
train_batch_size=512,
train_val_split_ratio=0.9,
test_dataset_size=500,
checkpoint_type='multi_step',
optimizer=AdamWConfig(lr=3e-4, weight_decay=1e-2),
lr_scheduler=CosineDecayWithWarmupConfig(
max_lr=3e-4,
min_lr=3e-5,
warmup_iters=200,
decay_iters=3000
)
)
For Production
config = TrainingConfig(
training_iters=10000,
train_batch_size=1024,
checkpoint_type='multi_step',
optimizer=AdamWConfig(lr=1e-3, weight_decay=1e-2),
lr_scheduler=CosineDecayWithWarmupConfig(
max_lr=1e-3,
min_lr=1e-5,
warmup_iters=500,
decay_iters=8000
)
)
Checkpoint Types
| Type | Optimizes For | Use Case |
|---|
'single_step' | Next-step prediction | Debugging, quick iteration |
'multi_step' | Trajectory simulation | Final models, deployment |
Data Splits
| Split | Purpose | Size |
|---|
| Training | Weight updates | train_val_split_ratio of data |
| Validation | Overfitting detection | 1 - train_val_split_ratio of data |
| Test | Visualization | test_dataset_size samples |
