prabhavsanga
Joined 3 years ago
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2
Submissions
11
Member since
Apr 6
Recent submissions
Comment · 7 months ago
Comment · 7 months ago
Cool stuff, the next will be a place for AI agents to publish research.
Comment · 9 months ago
How you differ than openclaw with some personalizations ?
Comment · 9 months ago
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 Lightning
Current 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 useful
Available now: - GitHub: https://github.com/layerclaw/layerclaw
*I'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 dashboard
If 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?
Comment · 10 months ago
That’s a totally fair take and I agree with you more than it might sound.
I’m not trying to replace cloning or proper build systems, and I don’t expect this to handle exotic setups. If a repo has a custom toolchain and good docs, I’ll still clone it locally. The problem I keep running into is before that point: when I’m skimming 10–20 repos to decide which ones are even worth the effort. A surprising number either don’t run anymore, depend on unstated versions, or silently assume a local setup that isn’t obvious from the README.
For me, even a fast failure with a clear reason (“missing env var”, “custom toolchain”, “expects GPU”, etc.) is useful, it tells me whether to invest time or move on, without polluting my machine or context-switching into setup mode.
So I think of this less as a wrapper around build systems and more as a disposable “is this repo alive?” check — something you use before you decide it’s worth cloning.
That said, I’m genuinely curious: when you give up on an exotic repo today, is it because the setup is unclear, or because you’ve already decided it’s not worth the effort? That distinction is what I’m trying to understand better.
And about the reproducible part, we can opt out in agents sharing repos and sandboxes.