PyTorch CosineAnnealingLR Scheduler Integration
Integrates the CosineAnnealingLR learning rate scheduler into the existing training pipeline configuration, allowing dynamic learning rate adjustment based on cosine annealing strategy.
npx skills add ECNU-ICALK/AutoSkill --skill pytorch-cosineannealinglr-scheduler-integration --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# PyTorch CosineAnnealingLR Scheduler Integration Integrates the CosineAnnealingLR learning rate scheduler into the existing training pipeline configuration, allowing dynamic learning rate adjustment based on cosine annealing strategy. ## Prompt # Role & Objective You are a PyTorch training utility expert. Your task is to modify the `get_optimizer_scheduler` function in `lib/train/base_functions.py` to support the `CosineAnnealingLR` learning rate scheduler. # Operational Rules & Constraints 1. **Import Requirement**: You must import `CosineAnnealingLR` from `torch.optim.lr_scheduler`. 2. **Configuration Mapping**: The function reads scheduler settings from `cfg.TRAIN.SCHEDULER`. - `cfg.TRAIN.SCHEDULER.TYPE`: Determines the scheduler type (e.g., 'step', 'Mstep', 'CosineAnnealingLR'). - `cfg.TRAIN.SCHEDULER.T_MAX`: The maximum number of iterations for CosineAnnealingLR. - `cfg.TRAIN.SCHEDULER.ETA_MIN`: The minimum learning rate for CosineAnnealingLR. 3. **Existing Logic**: Preserve the existing logic for 'step' and 'Mstep' schedulers. 4. **New Logic**: Add an `elif` branch for `CosineAnnealingLR` to instantiate `torch.optim.lr_scheduler.CosineAnnealingLR`. 5. **Error Handling**: Kee
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What does the PyTorch CosineAnnealingLR Scheduler Integration skill do?
Integrates the CosineAnnealingLR learning rate scheduler into the existing training pipeline configuration, allowing dynamic learning rate adjustment based on cosine annealing strategy.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill pytorch-cosineannealinglr-scheduler-integration --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.