Train and fine-tune biomedical ML models with production-grade patterns. Use when: (1) Writing training loops with early stopping and LR scheduling, (2) Transfer learning and fine-tuning pretrained models (freeze/unfreeze, discriminative LR), (3) Mixed precision training (fp16/bf16), (4) Gradient accumulation for large effective batch sizes, (5) Checkpoint management (save/load best model, resume training).
インストール
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Train and fine-tune biomedical ML models with production-grade patterns. Use when: (1) Writing training loops with early stopping and LR scheduling, (2) Transfer learning and fine-tuning pretrained models (freeze/unfreeze, discriminative LR), (3) Mixed precision training (fp16/bf16), (4) Gradient accumulation for large effective batch sizes, (5) Checkpoint management (save/load best model, resume training).
Training
Workflow
Training a biomedical ML model involves these steps:
Choose training strategy -- from scratch, fine-tune, or frozen feature extraction
Configure the training loop -- loss, optimizer, LR scheduler, early stopping
Set up mixed precision -- fp16 or bf16 based on GPU hardware
Run training -- with checkpointing and metric logging
Evaluate -- on held-out validation set each epoch
Decision Tree
Is there a pretrained model available?
Yes, dataset is small (<1K samples) → Freeze backbone, train head only. See transfer-learning.md
Yes, dataset is medium (1K-10K) → Fine-tune with discriminative LR. See transfer-learning.md
Yes, dataset is large (>10K) → Fine-tune all layers or train from scratch
No → Train from scratch with full training loop
What LR scheduler?
Research / general use → ReduceLROnPlateau (adapts to val metric)
Fine-tuning with warm start → Warmup + Cosine decay
Short aggressive training → OneCycleLR
Mixed precision?
Ampere+ GPU (A100, RTX 3090+) → bf16 (no GradScaler needed)
Older GPU → fp16 + GradScaler
CPU only → skip AMP
ASK the user before starting:
Are they training from scratch or fine-tuning? This determines the entire strategy.
What metric to monitor for early stopping (and minimize vs maximize)?
What GPU hardware is available? This determines mixed precision strategy.
