pytorch-training

01 前言 Front Matter

name

pytorch-training

metadata

{ "author": "researchclaw", "version": "1.0", "category": "tooling", "priority": "3", "references": "PyTorch Performance Tuning Guide, pytorch.org", "code-template": "import torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\n# Reproducibility\ntorch.manual_seed(seed)\ntorch.cuda.manual_seed_all(seed)\ntorch.backends.cudnn.deterministic = True\n\n# Training loop\nmodel.train()\nfor epoch in range(num_epochs):\n for batch in train_loader:\n optimizer.zero_grad(set_to_none=True)\n loss = criterion(model(batch['input']), batch['target'])\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()", "trigger-keywords": "training,pytorch,torch,deep learning,neural network,model", "applicable-stages": "10,12" }

description

Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.

03 SKILL.md 正文