skills.rest
i-bebsi
i-bebsiCOMMUNITY
Explore all skills by this author →
GitHub

weights-and-biases

Track ML experiments, hyperparameter sweeps, and model artifacts with Weights & Biases.

Updated Jun 17, 2026
Search Tags:#mlops#machine learning#pytorch#hyperparameter tuning#experiment tracking#wandb#model registry
One-click install
npx skills add https://github.com/i-bebsi/hermes-agent --skill weights-and-biases-i-bebsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/i-bebsi/hermes-agent/tree/main/hermes-config/skills/mlops/evaluation/weights-and-biases
Command: npx skills add https://github.com/i-bebsi/hermes-agent --skill weights-and-biases-i-bebsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires wandb, and includes references (resource) components.

What problem does it solve? Machine learning teams lose track of experiments, hyperparameters, and model versions when training runs are scattered across notebooks and terminals. This Skill provides complete guidance for instrumenting training code with Weights & Biases so every run, metric, and artifact is logged, comparable, and reproducible. ## Core Features & Use Cases - Experiment Tracking: Log metrics, configs, media, and system stats from PyTorch, TensorFlow, Keras, HuggingFace, and PyTorch Lightning training loops. - Hyperparameter Sweeps: Run grid, random, or Bayesian optimization searches with early termination and parallel agents across GPUs. - Artifacts & Model Registry: Version datasets and models with lineage tracking, aliases, and promotion workflows from staging to production. - Use Case: A data scientist fine-tuning a ResNet50 classifier can initialize a W&B run, launch a 50-trial Bayesian sweep over learning rate and batch size, then register the best model artifact to a production registry with full lineage back to the training dataset. ## Quick Start Ask the agent to instrument my PyTorch training script with W&B experiment tracking and set up a Bayesian hyperparameter sweep for learning rate and batch size.

Frequently Asked Questions about weights-and-biases

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track ML experiments with Weights & Biases?▼

Install wandb, call wandb.init with your project name and config, then call wandb.log with metrics inside your training loop. Finish with wandb.finish to close the run and view results in the W&B dashboard.

How to run hyperparameter sweeps with wandb?▼

Define a sweep config with a search method (grid, random, or bayes), a target metric, and parameter distributions. Create the sweep with wandb.sweep, then launch agents with wandb.agent pointing to your training function.

Does W&B integrate with HuggingFace Transformers?▼

Yes, set report_to="wandb" in TrainingArguments and the Trainer automatically logs metrics, losses, and evaluation results to W&B. You can also add custom WandbCallback subclasses for additional logging.

Can I use wandb offline without internet?▼

Yes, set the WANDB_MODE environment variable to "offline" before initializing runs. Metrics are stored locally and can be uploaded later using the wandb sync command on the run directory.

What is the difference between wandb.save and artifacts?▼

wandb.save uploads files associated with a single run, while artifacts create versioned, deduplicated objects with lineage tracking across runs. Artifacts are recommended for datasets and models that need versioning and registry promotion.

Which sweep method should I use for hyperparameter tuning?▼

Bayesian optimization is recommended for expensive training runs since it learns from previous trials. Use grid search for few discrete parameters and random search for quick exploration across many parameters.

Related Skills

Explore More Data & Analytics Skills→
CHENyiru3CHENyiru3

datasets

Load and preprocess datasets from Hugging Face Hub or local sources.

Community
Advanced
KentoShimizuKentoShimizu
7

mlops-monitoring-drift

Detect data drift, concept drift, and quality degradation in production ML pipelines.

Community
Advanced
nishide-devnishide-dev

ml-profile

Profile PyTorch Lightning training to diagnose data loading, computation, and GPU memory bottlenecks.

Community
Intermediate
granietgraniet
247

weights-and-biases

Log ML experiment metrics, hyperparameters, and artifacts to W&B.

Community
Intermediate