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嗨，HN！我开发了 LayerClaw（https://github.com/layerclaw/layerclaw），这是一个以本地为中心的 PyTorch 训练可观察性工具。问题：在训练神经网络时，很多问题都是悄无声息地发生的。你的损失在第 47,392 步时爆炸；你的梯度在第 12 层消失；你的 GPU 内存随机飙升。等你注意到这些问题时，可能已经浪费了数小时或数天的计算资源。我厌倦了添加打印语句、手动检查 TensorBoard 文件以及事后追踪训练问题。现有的工具要么需要云账户（如 W&B、Neptune），要么对于快速实验来说太过笨重（如 MLflow、用于梯度分析的 TensorBoard）。 LayerClaw 的功能： - 在训练过程中自动跟踪梯度、指标和系统资源 - 所有数据都存储在本地（使用 SQLite + Parquet，无需云服务） - 检测异常：梯度爆炸、NaN/Inf 值、损失峰值 - 提供命令行界面（CLI）以比较运行结果：`tracer compare run1 run2 --metric loss` - 异步写入的开销极小（约 2-3%）快速示例： ```python import tracer import torch # 初始化（只需一行） tracer.init(project="my-project", track_gradients=True) # 你的正常训练循环 model = YourModel() tracer._state.tracer.attach_hooks(model) for batch in dataloader: loss = train_step(model, batch) tracer.log({"loss": loss.item()}) tracer.step() tracer.finish() ``` 然后分析：`tracer anomalies my-run --auto` 与其他工具的不同之处： 1. 本地优先：无需注册，无数据离开你的机器，无供应商锁定 2. 专为调试设计：内置深度梯度跟踪和异常检测（不是事后添加的功能） 3. 轻量级：在你的训练循环中只需添加两行代码，开销极小 4. 兼容所有框架：原生 PyTorch、HuggingFace Transformers、PyTorch Lightning 当前限制（v0.1.0）： - 仅支持 CLI（计划在 v0.2 中推出 Web UI） - 单机训练（分布式支持即将推出） - 处于早期阶段，欢迎对最有用的功能提供反馈现在可用： - GitHub: https://github.com/layerclaw/layerclaw *我在寻找贡献者！* 我已经创建了几个“适合新手的问题”，欢迎任何有兴趣贡献的人。需要帮助的领域： - 可视化的 Web UI - 分布式训练支持 - 更多框架集成 - 实时监控仪表板如果你之前在机器学习训练中遇到过问题，我非常希望听到你认为最有价值的意见。欢迎提交 PR，或者如果你觉得这个项目有趣，请给仓库加星！什么功能会让这个工具成为你工作流程中不可或缺的一部分？

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Hi HN! I built LayerClaw (https://github.com/layerclaw/layerclaw), a local-first observability tool for PyTorch training. The problem: When training neural networks, things go wrong silently. Your loss explodes at step 47,392. Your gradients vanish in layer 12. Your GPU memory spikes randomly. By the time you notice, you've wasted hours or days of compute.I got tired of adding print statements, manually checking TensorBoard files, and tracking down training issues after the fact. Existing tools either require cloud accounts (W&B, Neptune) or are too heavyweight for quick experiments (MLflow, TensorBoard for gradient analysis).What LayerClaw does:- Automatically tracks gradients, metrics, and system resources during training - Stores everything locally (SQLite + Parquet, no cloud required) - Detects anomalies: gradient explosions, NaN/Inf values, loss spikes - Provides a CLI to compare runs: `tracer compare run1 run2 --metric loss` - Minimal overhead with async writes (~2-3%)Quick example:```python import tracer import torch# Initialize (one line) tracer.init(project="my-project", track_gradients=True)# Your normal training loop model = YourModel() tracer._state.tracer.attach_hooks(model)for batch in dataloader: loss = train_step(model, batch) tracer.log({"loss": loss.item()}) tracer.step()tracer.finish() ```Then analyze: `tracer anomalies my-run --auto`What makes it different:1. Local-first: No sign-ups, no data leaving your machine, no vendor lock-in 2. Designed for debugging: Deep gradient tracking and anomaly detection built-in (not an afterthought) 3. Lightweight: Add 2 lines to your training loop, minimal overhead 4. Works with everything: Vanilla PyTorch, HuggingFace Transformers, PyTorch LightningCurrent limitations (v0.1.0):- CLI-only (web UI planned for v0.2) - Single-machine training (distributed support coming) - Early stage - would love feedback on what's most usefulAvailable now: - GitHub: https://github.com/layerclaw/layerclawI'm looking for contributors! I've created several "good first issues" for anyone interested in contributing. Areas where I need help: - Web UI for visualizations - Distributed training support - More framework integrations - Real-time monitoring dashboardIf you've struggled with ML training issues before, I'd love your input on what would be most valuable. PRs welcome, or just star the repo if you find it interesting!What features would make this indispensable for your workflow?

展示HN：LayerClaw – 用于PyTorch训练的可观察性工具