tensorboardX
TensorBoardX lets you watch Tensors Flow without Tensorflow
Decision gist · record as of 2026-08-14
Yes. Actively maintained, low install friction, no security vulnerabilities, and MIT-licensed. Use it if you want TensorBoard visualization without TensorFlow, or need to log experiments from multiple frameworks. The recent protobuf version bump and NumPy 1.25+ fixes show the maintainers keep pace with ecosystem changes. Stable choice for experiment tracking in non-TensorFlow workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later.
- Low friction installation with stable maintenance.
- Active development (last commit 2026-07-14) and recent release (2026-04-03).
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal attribution requirements.
last release 2026-04-03 (133 days) · last repo commit 2026-07-14 · 7,996 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 5,972,785 downloads/mo, #1,998 on PyPI
Alternatives
Verify before relying
pip install tensorboardx
from tensorboardX import SummaryWriter
writer = SummaryWriter()
writer.add_scalar('loss', 0.5, global_step=1)
writer.close()- Whether the package works with all frameworks it claims to support (JAX, PaddlePaddle, Caffe2, mxnet, Chainer) in current version 2.6.5.
- Performance characteristics and memory overhead when logging large-scale experiments with many metrics.
- Compatibility with recent TensorBoard UI versions and whether all event types render correctly.
What it is and what it does
TensorBoardX is a standalone library that writes TensorBoard event files without requiring TensorFlow as a dependency. It provides a SummaryWriter interface for logging scalars, histograms, images, audio, embeddings, graphs, and other experiment artifacts to disk or cloud storage (S3, GCS). The library acts as a bridge between training code and TensorBoard visualization, letting you monitor experiments in TensorBoard regardless of which deep learning framework you use.
The package supports PyTorch, JAX, PaddlePaddle, and other tensor libraries through a framework-agnostic API. It handles writing to local disk, Amazon S3, and Google Cloud Storage, with optional optimizations like crc32c acceleration. Recent versions maintain compatibility with modern NumPy and protobuf releases, and the codebase remains actively maintained with regular bug fixes and dependency updates.
Use it for
- Log training metrics (loss, accuracy) and visualize them in TensorBoard during model training without TensorFlow.
- Track hyperparameter experiments and compare multiple runs side-by-side in TensorBoard's hparams plugin.
- Store experiment artifacts (model graphs, embeddings, sample images) to cloud storage for distributed team access.
- Monitor non-PyTorch deep learning frameworks (JAX, PaddlePaddle) using TensorBoard's visualization interface.
- Archive training logs to S3 or GCS for long-term experiment reproducibility and audit trails.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Actively maintained, low install friction, no security vulnerabilities, and MIT-licensed. Use it if you want TensorBoard visualization without TensorFlow, or need to log experiments from multiple frameworks. The recent protobuf version bump and NumPy 1.25+ fixes show the maintainers keep pace with ecosystem changes. Stable choice for experiment tracking in non-TensorFlow workflows.
Install
tensorboardx on PyPI
Before you install
Low friction installation with stable maintenance. Active development (last commit 2026-07-14) and recent release (2026-04-03). Depends on three common packages (numpy, packaging, protobuf) with no compiled dependencies. Protobuf version constraint recently bumped to >=5.29.6 in latest release.
Requires Python 3.9 or later.
License in practice
MIT license permits unrestricted use, modification, and distribution in both open-source and commercial projects with minimal attribution requirements.
Quickstart
pip install tensorboardx
from tensorboardX import SummaryWriter
writer = SummaryWriter()
writer.add_scalar('loss', 0.5, global_step=1)
writer.close()Verify before relying
- Whether the package works with all frameworks it claims to support (JAX, PaddlePaddle, Caffe2, mxnet, Chainer) in current version 2.6.5.
- Performance characteristics and memory overhead when logging large-scale experiments with many metrics.
- Compatibility with recent TensorBoard UI versions and whether all event types render correctly.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesnumpypackagingprotobuf |
| Maintenance | Actively maintained 133 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 5,972,785 / month, #1,998 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersNatural Language :: EnglishProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.9 |
Evidence: tensorboardx-2.6.5-py3-none-any.whl
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See also tbparse · tensorboard · tb-nightly · tensorboard-data-server · pygexf · tensorboard-plugin-profile · tensorflow-graphics · comet-ml · torch-tb-profiler · tensordict