菜单
Weights & Biases — ML experiment tracking and visualization. Log metrics, hyperparameters, model checkpoints, and artifacts. Collaborative dashboards, sweep hyperparameter search, and model registry.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mkurman/zorai --skill wandb命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use when provisioning Prime Intellect GPU compute, managing pods/disks/sandboxes, running hosted RL training via prime lab, installing or publishing RL environments, or exposing local services via Prime Tunnel. Covers the `prime` CLI (PyPI: prime) for all Prime Intellect platform operations.
Shared Python package for Science Skills, currently containing http_client -- a unified HTTP client with rate limiting, retries, and exponential backoff. Not a standalone agent skill. Do not invoke directly.
Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
基于 SOC 职业分类
正在显示 SKILL.md
| name | wandb |
| description | Weights & Biases — ML experiment tracking and visualization. Log metrics, hyperparameters, model checkpoints, and artifacts. Collaborative dashboards, sweep hyperparameter search, and model registry. |
| tags | ["experiment-tracking","hyperparameter-sweeps","model-observability","training-visualization","wandb"] |
Weights & Biases (wandb) tracks ML experiments with rich visualizations, hyperparameter sweeps, dataset versioning, model registry, and collaborative dashboards. Industry standard for experiment tracking across ML teams.
uv pip install wandb
wandb login # authenticate with API key
import wandb
wandb.init(project="my_project", config={
"learning_rate": 0.001,
"batch_size": 32,
"architecture": "transformer",
})
for epoch in range(10):
loss = train_one_epoch()
wandb.log({"train_loss": loss, "val_loss": val_loss, "epoch": epoch})
wandb.finish()
sweep_config = {
"method": "bayes",
"metric": {"name": "val_loss", "goal": "minimize"},
"parameters": {"lr": {"min": 1e-5, "max": 1e-2}},
}
sweep_id = wandb.sweep(sweep_config, project="my_project")
wandb.agent(sweep_id, function=train_function, count=20)