Does this skill support both local and cloud monitoring?

Yes, it provides setup scripts and templates for TensorBoard (local) and Weights & Biases (cloud), including a unified logger for both platforms.

How do I launch the dashboards?

The skill includes a 'launch-monitoring.sh' script that simplifies the process of starting the local TensorBoard server or connecting to your WandB cloud account.

Does it support hyperparameter tuning?

Yes, it includes specific templates for configuring and visualizing WandB hyperparameter sweeps to help you find the best model configuration efficiently.

Can I track custom metrics like images or audio?

Yes, the skill provides advanced logging examples for scalars, histograms, images, text, model graphs, embeddings, and audio files.

Can I use this with PyTorch or TensorFlow?

Absolutely. The skill includes integration patterns and templates that are compatible with major deep learning frameworks like PyTorch and TensorFlow.

ML Training Monitoring Dashboard

Name: ML Training Monitoring Dashboard
Author: vanman2024

byvanman2024

0•

데이터 과학 및 ML

Sets up comprehensive monitoring dashboards using TensorBoard and Weights & Biases to track machine learning experiments in real-time.

The Monitoring Dashboard skill streamlines the integration of experiment tracking tools into your machine learning workflow. It provides pre-configured templates and scripts for both TensorBoard (local) and Weights & Biases (cloud), enabling developers to visualize loss curves, gradients, model architectures, and hyperparameter sweeps. Whether you need offline monitoring for local development or collaborative tracking for team projects, this skill ensures unified logging and professional-grade observability for your ML pipelines through a standardized, easy-to-implement framework.

주요 기능

01Real-time visualization of metrics, gradients, and model artifacts

02Pre-configured hyperparameter sweep and visualization patterns

030 GitHub stars

04Automated setup scripts for TensorBoard and Weights & Biases (WandB)

05Customizable alert systems for training thresholds and performance updates

06Unified logging templates for simultaneous local and cloud experiment tracking

사용 사례

01Comparing multiple training runs to identify optimal model architectures and hyperparameters

02Monitoring model health and training stability in real-time to catch divergence early

03Generating collaborative experiment reports and history for team review and reproducibility

주요 기능

01Real-time visualization of metrics, gradients, and model artifacts

02Pre-configured hyperparameter sweep and visualization patterns

030 GitHub stars

04Automated setup scripts for TensorBoard and Weights & Biases (WandB)

05Customizable alert systems for training thresholds and performance updates

06Unified logging templates for simultaneous local and cloud experiment tracking

사용 사례

01Comparing multiple training runs to identify optimal model architectures and hyperparameters

02Monitoring model health and training stability in real-time to catch divergence early

03Generating collaborative experiment reports and history for team review and reproducibility