OpenClaw

wandb

Monitor and analyze Weights & Biases training runs. Use when checking training status, detecting failures, analyzing loss curves, comparing runs, or monitoring experiments. Triggers on "wandb", "training runs", "how's training", "did my run finish", "any failures", "check experiments", "loss curve", "gradient norm", "compare runs".

chrisvoncsefalvay0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install chrisvoncsefalvay/wandb-monitor

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

Weights & Biases

Monitor, analyze, and compare W&B training runs.

Setup

wandb login
# Or set WANDB_API_KEY in environment

Scripts

Characterize a Run (Full Health Analysis)

~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/characterize_run.py ENTITY/PROJECT/RUN_ID

Analyzes:

Loss curve trend (start → current, % change, direction)
Gradient norm health (exploding/vanishing detection)
Eval metrics (if present)
Stall detection (heartbeat age)
Progress & ETA estimate
Config highlights
Overall health verdict

Options: --json for machine-readable output.

Watch All Running Jobs

~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/watch_runs.py ENTITY [--projects p1,p2]

Quick health summary of all running jobs plus recent failures/completions. Ideal for morning briefings.

Options:

--projects p1,p2 — Specific projects to check
--all-projects — Check all projects
--hours N — Hours to look back for finished runs (default: 24)
--json — Machine-readable output

Compare Two Runs

~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/compare_runs.py ENTITY/PROJECT/RUN_A ENTITY/PROJECT/RUN_B

Side-by-side comparison:

Config differences (highlights important params)
Loss curves at same steps
Gradient norm comparison
Eval metrics
Performance (tokens/sec, steps/hour)
Winner verdict

Python API Quick Reference

import wandb
api = wandb.Api()

# Get runs
runs = api.runs("entity/project", {"state": "running"})

# Run properties
run.state      # running | finished | failed | crashed | canceled
run.name       # display name
run.id         # unique identifier
run.summary    # final/current metrics
run.config     # hyperparameters
run.heartbeat_at # stall detection

# Get history
history = list(run.scan_history(keys=["train/loss", "train/grad_norm"]))

Metric Key Variations

Scripts handle these automatically:

Loss: train/loss, loss, `train_...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/chrisvoncsefalvay/wandb-monitor
Version: —
Commit: edabfeafcb308244a91641d1acd4ec27336ed2a0
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Weights & Biases

Monitor, analyze, and compare W&B training runs.

## Setup

```bash
wandb login
# Or set WANDB_API_KEY in environment
```

## Scripts

### Characterize a Run (Full Health Analysis)

```bash
~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/characterize_run.py ENTITY/PROJECT/RUN_ID
```

Analyzes:
- Loss curve trend (start → current, % change, direction)
- Gradient norm health (exploding/vanishing detection)  
- Eval metrics (if present)
- Stall detection (heartbeat age)
- Progress & ETA estimate
- Config highlights
- Overall health verdict

Options: `--json` for machine-readable output.

### Watch All Running Jobs

```bash
~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/watch_runs.py ENTITY [--projects p1,p2]
```

Quick health summary of all running jobs plus recent failures/completions. Ideal for morning briefings.

Options:
- `--projects p1,p2` — Specific projects to check
- `--all-projects` — Check all projects
- `--hours N` — Hours to look back for finished runs (default: 24)
- `--json` — Machine-readable output

### Compare Two Runs

```bash
~/clawd/venv/bin/python3 ~/clawd/skills/wandb/scripts/compare_runs.py ENTITY/PROJECT/RUN_A ENTITY/PROJECT/RUN_B
```

Side-by-side comparison:
- Config differences (highlights important params)
- Loss curves at same steps
- Gradient norm comparison
- Eval metrics
- Performance (tokens/sec, steps/hour)
- Winner verdict

## Python API Quick Reference

```python
import wandb
api = wandb.Api()

# Get runs
runs = api.runs("entity/project", {"state": "running"})

# Run properties
run.state      # running | finished | failed | crashed | canceled
run.name       # display name
run.id         # unique identifier
run.summary    # final/current metrics
run.config     # hyperparameters
run.heartbeat_at # stall detection

# Get history
history = list(run.scan_history(keys=["train/loss", "train/grad_norm"]))
```

## Metric Key Variations

Scripts handle these automatically:
- Loss: `train/loss`, `loss`, `train_...

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