tensorboard
Category: ml-inference-and-ops · License: MIT · Version: 1.0.0
Tags: MLOps, TensorBoard, Visualization, Training Metrics, Model Debugging, PyTorch, TensorFlow, Experiment Tracking, Performance Profiling
When to use it
Logs and views ML training data in TensorBoard using PyTorch SummaryWriter and TensorFlow/Keras callbacks: scalars, images, text, histograms, model graphs, embedding projector, hyperparameter tables, PR curves, and TensorFlow or PyTorch profiler traces. Use when plotting loss and accuracy curves during training, comparing multiple runs in one dashboard, inspecting weight and gradient distributions, projecting embeddings with PCA or t-SNE, tracking hyperparameter experiments, or finding performance bottlenecks in a training loop. Not for hosted experiment tracking with team collaboration; use a dedicated tracking service for that.Example requests
Ask your agent in plain language, for example:- “How do I get my PyTorch loss curve showing up in TensorBoard?”
- “I’m training a ResNet-18 on CIFAR-10 in train.py with a Keras-free PyTorch loop. Val accuracy is flat after epoch 5 and I suspect vanishing gradients in the early conv layers. I already have a SummaryWriter at runs/resnet18_lr1e-3. Add logging so I can inspect the weight and gradient distributions per layer every epoch, and also log a few misclassified validation images.”
- “I ran 12 learning-rate and batch-size combos of my transformer and want one view where I can see which settings gave the best validation loss, and see all the loss curves overlaid without a pile of separate folders to dig through.”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole ml-inference-and-ops category with /plugin install ml-inference-and-ops@research-agent-skills.