MLtraq vs TensorBoard
MLtraq
mltraq.comTensorBoard
tensorflow.orgSide by side
| Figure | MLtraq | TensorBoard |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 0 | 1 |
| Spec score · Experiment Tracking | 7.1 (#15) | 7.6 (#7) |
| Spec score · ML Experiment Tracking Software | 7.0 (#28) | 7.6 (#16) |
| License | The project is licensed under the BSD 3-Clause License. | The TensorBoard GitHub repository identifies its license as Apache-2.0. |
| Metrics | Experiments can stream metrics and track native Python data types and structures, as well as NumPy, Pandas, and PyArrow objects. | It tracks and visualizes metrics such as loss and accuracy. |
| Purpose | MLtraq is an open-source Python library for ML and AI developers to design, execute, track, and share experiments. | TensorBoard is a suite of visualization tools for understanding, debugging, and optimizing TensorFlow programs during machine learning experimentation. |
| Support | The project directs users to GitHub Discussions to ask questions and share ideas. | The TensorBoard README directs general usage questions to Stack Overflow and bug reports to GitHub Issues. |
Plans
In their own words
TensorBoard
TensorBoard is a free suite of visualization tools for understanding, debugging and optimizing TensorFlow programs during machine-learning experiments. It tracks metrics such as loss and accuracy, and its Graph Explorer displays model graphs for inspection. Dashboards show changes in tensor distributions through histograms and percentile summaries. The tools can also display logged images, audio and text, project high-dimensional embeddings and profile TensorFlow programs. In Keras workflows, a TensorFlow callback records metrics, training graph visualizations, weight histograms and sampled profiling data. TensorBoard can be launched from the command line or in notebooks using the `%tensorboard` line magic. It is designed for offline use, including local environments, corporate firewalls and datacenters. Version 2.0 and later defaults to `--host localhost`; `--bind_all` serves it on the public network over IPv4 and IPv6. Without a TensorFlow installation, it does not support log directories on Google Cloud Storage. The README identifies Chrome and Firefox as supported browsers and warns that others may have bugs or performance issues.