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home / skills / jeremylongshore / claude-code-plugins-plus-skills / tensorboard-visualizer

tensorboard-visualizer skill

/skills/07-ml-training/tensorboard-visualizer

This skill provides automated tensorboard visualizer guidance, generating production-ready configs and best-practice steps for ML training workflows.

npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill tensorboard-visualizer

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
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---
name: "tensorboard-visualizer"
description: |
  Generate tensorboard visualizer operations. Auto-activating skill for ML Training.
  Triggers on: tensorboard visualizer, tensorboard visualizer
  Part of the ML Training skill category. Use when working with tensorboard visualizer functionality. Trigger with phrases like "tensorboard visualizer", "tensorboard visualizer", "tensorboard".
allowed-tools: "Read, Write, Edit, Bash(python:*), Bash(pip:*)"
version: 1.0.0
license: MIT
author: "Jeremy Longshore <[email protected]>"
---

# Tensorboard Visualizer

## Overview

This skill provides automated assistance for tensorboard visualizer tasks within the ML Training domain.

## When to Use

This skill activates automatically when you:
- Mention "tensorboard visualizer" in your request
- Ask about tensorboard visualizer patterns or best practices
- Need help with machine learning training skills covering data preparation, model training, hyperparameter tuning, and experiment tracking.

## Instructions

1. Provides step-by-step guidance for tensorboard visualizer
2. Follows industry best practices and patterns
3. Generates production-ready code and configurations
4. Validates outputs against common standards

## Examples

**Example: Basic Usage**
Request: "Help me with tensorboard visualizer"
Result: Provides step-by-step guidance and generates appropriate configurations


## Prerequisites

- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml training concepts


## Output

- Generated configurations and code
- Best practice recommendations
- Validation results


## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |


## Resources

- Official documentation for related tools
- Best practices guides
- Community examples and tutorials

## Related Skills

Part of the **ML Training** skill category.
Tags: ml, training, pytorch, tensorflow, sklearn

Overview

This skill automates TensorBoard visualizer tasks to speed up experiment tracking and inspection during ML training. It generates code, configurations, and step-by-step instructions to set up and run TensorBoard for common frameworks such as TensorFlow and PyTorch. The outputs are production-ready and validated against common standards.

How this skill works

The skill inspects your training configuration and experiment layout, then generates TensorBoard log writer code, configuration files, and run commands. It suggests directory structures for scalars, histograms, images, and embeddings, and produces launch scripts or Docker commands for local and remote visualization. It also validates required dependencies and warns about common permission or path issues.

When to use it

  • You want to add TensorBoard to a new or existing training pipeline.
  • You need ready-to-run code snippets for logging scalars, histograms, images, or embeddings.
  • You need configuration for running TensorBoard locally, in containers, or on remote servers.
  • You want quick validation of TensorBoard log paths and dependency checks.
  • You need best-practice guidance for experiment organization and naming conventions.

Best practices

  • Write logs to a timestamped experiment directory to keep runs reproducible and comparable.
  • Log scalars at consistent intervals and add step/global_step for accurate plotting.
  • Record model checkpoints and link them in experiment metadata for traceability.
  • Use separate subfolders for training/validation and different experiment runs to avoid overlapping logs.
  • Validate log writer initialization and ensure TensorBoard has read permissions for the log directory.

Example use cases

  • Generate TensorBoard logging snippets for TensorFlow and PyTorch training loops.
  • Create a Dockerfile and docker-compose snippet to host TensorBoard for a team.
  • Produce a launch script that forwards remote TensorBoard via SSH or ngrok for secure remote access.
  • Validate an existing training pipeline and fix missing writer initialization or misnamed log directories.
  • Provide step-by-step instructions to add embeddings projector and attach metadata for visualization.

FAQ

What dependencies must be installed to use generated code?

Install tensorflow or torch (as applicable), tensorboard, and any environment tools like pip, virtualenv, or Docker. The skill will include a dependency checklist.

Can this configure TensorBoard for remote servers or cloud instances?

Yes. It generates SSH port-forward commands, Docker configurations, and guidance to expose TensorBoard securely on cloud instances.