Weights And Biases — W&B: log ML experiments, sweeps, model registry, dashboards
Weights And Biases — W&B: log ML experiments, sweeps, model registry, dashboards
Start with meaning, then move to detail.
This lesson explains Weights And Biases — W&B: log ML experiments, sweeps, model registry, dashboards as part of getting started with Hermes correctly. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, When to Use This Skill, then verify failure modes and version compatibility.
No prior experience is required; follow the steps on a safe test setup first.
A clear outcome before you read.
- Understand Weights And Biases — W&B: log ML experiments, sweeps, model registry, dashboards without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Provider
- The service that runs or provides access and authentication to a model.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
W&B: log ML experiments, sweeps, model registry, dashboards
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03When to Use This Skill
Read this after the foundation, then connect it to the previous step.
- 04Installation
Read this after the foundation, then connect it to the previous step.
- 05Quick Start
Read this after the foundation, then connect it to the previous step.
- 06Basic Experiment Tracking
Read this after the foundation, then connect it to the previous step.
- 07With PyTorch
Read this after the foundation, then connect it to the previous step.
- 08Core Concepts
Read this after the foundation, then connect it to the previous step.
- 091. Projects and Runs
Read this after the foundation, then connect it to the previous step.
- 102. Configuration Tracking
Finish here to verify the result and special cases.
Copy only after you understand the effect.
# Install W&B
pip install wandb
# Login (creates API key)
wandb login
# Or set API key programmatically
export WANDB_API_KEY=your_api_key_here### With PyTorch## Core Concepts
### 1. Projects and Runs
**Project**: Collection of related experiments
**Run**: Single execution of your training scriptRead the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.