开发编程
#ai
pytorch-training
Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code.
DeepseekModel
官方收录技能
质量 优秀 · 90
v1.0.0
获取
https://deepseekmodel.com/api/download.php?id=aiming-lab-autoresearchclaw-researchclaw-skills-builtin-tooling-pytorch-training-skill-md&format=skill
下载 .skill
标准格式，含 system_prompt 与 model_config，导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name pytorch-training description Best practices for building robust PyTorch training loops. Use when generating or reviewing ML training code. metadata {"category":"tooling","trigger-keywords":"training,pytorch,torch,deep learning,neural network,model","applicable-stages":"10,12","priority":"3","version":"1.0","author":"researchclaw","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()\n"} PyTorch Training Best Practice Use torch.manual_seed() for reproducibility (set for torch, numpy, random) Use DataLoader with num_workers>0 and pin_memory=True for GPU Enable cudnn.benchmark=True for fixed input sizes Use learning rate schedulers (CosineAnnealingLR or OneCycleLR) Implement early stopping based on validation metric Log metrics every epoch, save best model checkpoint Use torch.no_grad() for evaluation Clear gradients with optimizer.zero_grad(set_to_none=True) for efficiency
Agent 识别该技能的关键词，点击任意一个即可复制。
该技能未提供触发词。
下载的 .skill 包内含以下字段。
| 字段 | 说明 |
|---|---|
| format | 格式标识（skill/v1） |
| skill_id | 技能唯一 ID |
| name | 技能名称 |
| version | 版本号 |
| description | 技能描述 |
| category | 所属分类（数组） |
| trigger_words | 触发词列表 |
| tags | 标签列表 |
| source | 来源标识 |
| source_url | 来源链接（本页地址） |
| exported_at | 导出时间（每次下载生成） |
| system_prompt | 系统提示词正文 |
| model_config | 模型参数：provider / model / temperature / max_tokens / top_p |
| examples | 示例 |
| install_guide | 各平台导入说明（Coze / Dify / Claude / 自定义框架） |
