Metadata-Version: 2.1
Name: tensorboard-projects
Version: 0.0.1rc9
Summary: Tensorboard Projects: A management UI for Tensorboard runs
Home-page: https://github.com/jverre/tensorboard-projects
Author: Jacques Verré
License: MIT
Platform: UNKNOWN
Requires-Python: >=2.7,!= 3.0.*,!= 3.1.*,!= 3.2.*,!= 3.3.*,!= 3.4.*,!= 3.5.*
Description-Content-Type: text/markdown
Requires-Dist: click (>=7.0Flask)
Requires-Dist: flask-cors
Requires-Dist: gunicorn
Requires-Dist: tensorboard

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# TensorBoard Projects


**Management UI for Tensorboard Dashboards**

[![GitHub license](https://img.shields.io/github/license/jverre/tensorboard-projects)](https://img.shields.io/github/license/jverre/tensorboard-projects)
![](https://github.com/jverre/tensorboard-projects/workflows/CI/badge.svg)


</div>

---

TensorBoard is a powerful visualization tool when building machine learning models. However it can quickly become quite unwieldy as the number of model versions and runs increases.


## What is TensorBoard Projects
TensorBoard Projects provides a UI to manage Tensorboard runs and allows you to easily:
* Visualize runs for a given model
* Archive Runs
* Delete Runs
* Add metadata to individual runss
* Start TensorBoard dashboard for a subset of model runs
* Write documentation for a model

As the project is still very much in development, please report any issues or features you would like to see added !

![Demo](https://raw.githubusercontent.com/jverre/tensorboard-projects/main/docs/tensorboard_projects_demo_v2.gif)

## What TensorBoard Projects isn't
**TensorBoard projects is not an experiment tracking solution**, it simply allows you to better manage TensorBoard runs.

If you are looking for a fully fledgeed experiment tracking solution, you can look into:
* [ClearML](https://www.clear.ml/)
* [Comet](https://www.comet.ml/site/)
* [MLFlow](https://www.mlflow.org/)
* [Weights and Biases](https://wandb.ai/site)
and others

## Using TensorBoard Projects
Tensorboard Projects can be installed from PyPI using `pip install tensorboard-projects`.

Once Tensorboard Projects is installed, you can start the UI using:
```
tensorboard-projects ui --port 5000 --host 0.0.0.0
```


