TensorBoard

An open-source ML visualization tool that runs offline on your machine or server to compare TensorFlow experiments and inspect model behavior.

TensorBoard helps ML developers and researchers inspect TensorFlow experiments through a browser. It shows how training progresses and lets you compare runs when evaluating changes to a model or its hyperparameters.

Training metrics: Plot recorded loss, accuracy, learning rate, and other scalar measurements over time, with multiple runs on the same charts.
Model inspection: View the model's operations and layers as a graph, and examine histograms of weights, biases, or other tensors as they change during training.
Embedding views: Project embeddings into fewer dimensions to examine their structure visually.
Recorded media: Display images, text, and audio from an experiment alongside its numerical measurements.
Performance profiling: Profile TensorFlow programs to investigate execution performance.

TensorBoard runs on your own machine or server and works entirely offline, including behind a firewall or in a datacenter. It reads recorded experiment data from disk, so you can inspect runs within your own environment without internet access. The dashboards depend on the metrics and other data your TensorFlow program records. TensorBoard is open source under the Apache 2.0 license.

Experiment Tracking and MLOps Model Inspection and Visualization macOS Web Works offline

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TensorBoard

An open-source ML visualization tool that runs offline on your machine or server to compare TensorFlow experiments and inspect model behavior.

Nobody has claimed TensorBoard yet

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