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Documentation Index

Fetch the complete documentation index at: https://docs.onyx-robotics.com/llms.txt

Use this file to discover all available pages before exploring further.

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Optimizers

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AdamWConfig

Adam optimizer with decoupled weight decay. 
from onyxengine.modeling import AdamWConfig

optimizer = AdamWConfig(
    lr: float = 3e-4,
    weight_decay: float = 1e-2
)
ParameterDefaultDescription
lr3e-4Learning rate
weight_decay1e-2L2 regularization strength
Example: 
optimizer = AdamWConfig(lr=3e-4, weight_decay=1e-2)

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SGDConfig

Stochastic Gradient Descent with momentum. 
from onyxengine.modeling import SGDConfig

optimizer = SGDConfig(
    lr: float = 1e-3,
    weight_decay: float = 1e-4,
    momentum: float = 0.9
)
ParameterDefaultDescription
lr1e-3Learning rate
weight_decay1e-4L2 regularization strength
momentum0.9Momentum factor
Example: 
optimizer = SGDConfig(lr=1e-3, weight_decay=1e-4, momentum=0.95)

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Learning Rate Schedulers

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CosineDecayWithWarmupConfig

Linear warmup followed by cosine decay. 
from onyxengine.modeling import CosineDecayWithWarmupConfig

scheduler = CosineDecayWithWarmupConfig(
    max_lr: float = 3e-4,
    min_lr: float = 3e-5,
    warmup_iters: int = 200,
    decay_iters: int = 1000
)
ParameterDefaultDescription
max_lr3e-4Peak learning rate (after warmup)
min_lr3e-5Final learning rate (after decay)
warmup_iters200Iterations to ramp up to max_lr
decay_iters1000Iterations for cosine decay
Example: 
scheduler = CosineDecayWithWarmupConfig(
    max_lr=1e-3,
    min_lr=1e-5,
    warmup_iters=500,
    decay_iters=5000
)

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CosineAnnealingWarmRestartsConfig

Cosine annealing with periodic restarts. 
from onyxengine.modeling import CosineAnnealingWarmRestartsConfig

scheduler = CosineAnnealingWarmRestartsConfig(
    T_0: int = 500,
    T_mult: int = 1,
    eta_min: float = 1e-5
)
ParameterDefaultDescription
T_0500Initial cycle length
T_mult1Cycle length multiplier (1 = same length)
eta_min1e-5Minimum learning rate
Example: 
scheduler = CosineAnnealingWarmRestartsConfig(
    T_0=1000,
    T_mult=2,  # Each cycle is 2x longer
    eta_min=1e-6
)

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Optimization Configs

For hyperparameter search, use the OptConfig variants:

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AdamWOptConfig

from onyxengine.modeling import AdamWOptConfig

adamw_opt = AdamWOptConfig(
    lr={"select": [1e-5, 1e-4, 3e-4, 1e-3]},
    weight_decay={"select": [1e-4, 1e-3, 1e-2]}
)

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SGDOptConfig

from onyxengine.modeling import SGDOptConfig

sgd_opt = SGDOptConfig(
    lr={"select": [1e-4, 1e-3, 1e-2]},
    weight_decay={"select": [1e-4, 1e-3]},
    momentum={"select": [0.9, 0.95, 0.99]}
)

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CosineDecayWithWarmupOptConfig

from onyxengine.modeling import CosineDecayWithWarmupOptConfig

lr_opt = CosineDecayWithWarmupOptConfig(
    max_lr={"select": [3e-4, 1e-3, 3e-3]},
    min_lr={"select": [1e-6, 1e-5, 1e-4]},
    warmup_iters={"select": [100, 200, 400]},
    decay_iters={"select": [1000, 2000, 5000]}
)

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CosineAnnealingWarmRestartsOptConfig

from onyxengine.modeling import CosineAnnealingWarmRestartsOptConfig

lr_opt = CosineAnnealingWarmRestartsOptConfig(
    T_0={"select": [500, 1000, 2000]},
    T_mult={"select": [1, 2]},
    eta_min={"select": [1e-6, 1e-5, 1e-4]}
)

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Typical Configurations

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Quick Experimentation

training_config = TrainingConfig(
    training_iters=2000,
    train_batch_size=256,
    optimizer=AdamWConfig(lr=3e-4),
    lr_scheduler=None
)

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Production Training

training_config = TrainingConfig(
    training_iters=10000,
    train_batch_size=1024,
    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
    )
)