OptiRoulette 0.1.0

PyTorch optimizer with random switching, LR scaling and pool swaps

OptiRoulette Optimizer

This repository accompanies the paper "OptiRoulette Optimizer: A New Stochastic Meta-Optimizer for up to 5.3x Faster Convergence".

A standalone, pip-installable PyTorch meta-optimizer that brings OptiRoulette's training logic to any project:

• random optimizer switching
• warmup -> roulette phase handling
• optimizer pool with active/backup swapping
• compatibility-aware replacement
• learning-rate scaling rules when switching
• momentum/state transfer on swap

The default behavior is loaded from the bundled optimized.yaml profile (same optimizer pool logic used in this project).

Research Highlights

Based on the current paper draft, OptiRoulette is a stochastic meta-optimizer that combines:

• warmup optimizer locking
• randomized sampling from an active optimizer pool
• compatibility-aware LR scaling during optimizer transitions
• failure-aware pool replacement

Reported mean test accuracy vs a single-optimizer AdamW baseline:

Dataset	AdamW	OptiRoulette	Delta
CIFAR-100	0.6734	0.7656	+9.22 pp
CIFAR-100-C	0.2904	0.3355	+4.52 pp
SVHN	0.9667	0.9756	+0.89 pp
Tiny ImageNet	0.5669	0.6642	+9.73 pp
Caltech-256	0.5946	0.6920	+9.74 pp

Additional paper-reported highlights:

• Target-hit reliability: in the reported 10-seed suites, OptiRoulette reaches key validation targets in 10/10 runs, while the AdamW baseline reaches none of those targets within budget.
• Faster time-to-target on shared milestones (example: Caltech-256 @ 0.59, 25.7 vs 77.0 epochs), with budget-capped lower-bound speedups up to 5.3x for non-attained baseline targets.
• Paired-seed analysis is positive across datasets, except CIFAR-100-C test ROC-AUC, which is not statistically significant in the current 10-seed study.

Install

pip install OptiRoulette

Examples

• CIFAR-100 demo notebook
• Tiny-ImageNet demo notebook

Quick Use

import torch
from optiroulette import OptiRoulette

model = torch.nn.Linear(128, 10)
optimizer = OptiRoulette(model.parameters())

for epoch in range(5):
    optimizer.on_epoch_start(epoch)

    for batch_idx in range(100):
        optimizer.on_batch_start(batch_idx)
        optimizer.zero_grad()
        x = torch.randn(32, 128)
        y = torch.randint(0, 10, (32,))
        loss = torch.nn.functional.cross_entropy(model(x), y)
        loss.backward()
        optimizer.step()

    # pass validation accuracy for warmup plateau logic (optional)
    optimizer.on_epoch_end(val_acc=0.6)

API

from optiroulette import (
    OptiRoulette,
    OptiRouletteOptimizer,
    PoolConfig,
    get_default_config,
    get_default_seed,
    get_default_optimizer_specs,
    get_default_pool_setup,
    get_default_roulette_config,
)

Configuration Reference

For a full settings guide (constructor arguments, optimizer_specs, pool_config, warmup/roulette options, and defaults precedence), see:

• docs/configuration.md

For package maintainers (release/publish steps), see:

• docs/release.md

Defaults behavior

OptiRoulette(model.parameters()) uses:

• default optimizer specs from bundled optimized.yaml
• default roulette settings from bundled optimized.yaml
• default pool config + active/backup names from bundled optimized.yaml
• default LR scaling rules from bundled optimized.yaml
• default optimizer RNG seed from bundled optimized.yaml (system.seed, fallback 42)

If you provide manual optimizer/pool settings, those are used instead of defaults:

optimizer = OptiRoulette(
    model.parameters(),
    optimizer_specs={"adam": {"lr": 1e-3}},
)

Manual custom pool example (only your chosen optimizers are used):

optimizer = OptiRoulette(
    model.parameters(),
    optimizer_specs={
        "adam": {"lr": 1e-3},
        "adamw": {"lr": 8e-4, "weight_decay": 0.01},
        "lion": {"lr": 1e-4, "betas": (0.9, 0.99)},
    },
    active_names=["adam", "adamw"],
    backup_names=["lion"],
)

Optional: override pool behavior too:

optimizer = OptiRoulette(
    model.parameters(),
    optimizer_specs={
        "adam": {"lr": 1e-3},
        "adamw": {"lr": 8e-4, "weight_decay": 0.01},
        "lion": {"lr": 1e-4, "betas": (0.9, 0.99)},
    },
    pool_config={
        "num_active": 2,
        "num_backup": 1,
        "failure_threshold": -0.2,
        "consecutive_failure_limit": 3,
    },
    active_names=["adam", "adamw"],
    backup_names=["lion"],
)

Third-Party Dependencies

This package depends on pytorch-optimizer for additional optimizer implementations. See THIRD_PARTY_LICENSES.md for a short third-party license notice.

Disclaimer

The OptiRoulette name refers exclusively to a machine-learning optimizer and has no affiliation, sponsorship, or technical relation to roulette manufacturers, casinos, or any physical/software gambling products or services.