https://github.com/rerun-io/rerun-loader-python-example-tfrecord
Example Tensorboard log (TFRecord of Events) external data loader for Rerun
https://github.com/rerun-io/rerun-loader-python-example-tfrecord
machine-learning tensorflow visualization
Last synced: 6 months ago
JSON representation
Example Tensorboard log (TFRecord of Events) external data loader for Rerun
- Host: GitHub
- URL: https://github.com/rerun-io/rerun-loader-python-example-tfrecord
- Owner: rerun-io
- License: apache-2.0
- Created: 2024-01-09T08:49:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-31T06:26:16.000Z (about 2 years ago)
- Last Synced: 2025-03-25T05:59:58.176Z (over 1 year ago)
- Topics: machine-learning, tensorflow, visualization
- Language: Python
- Homepage: https://www.rerun.io/blog/data-loaders
- Size: 2.58 MB
- Stars: 1
- Watchers: 8
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# tfrecord -> Rerun plugin
This is an example data-loader plugin that lets you view a TFRecord of Events (i.e., Tensorboard log files) in the [Rerun](https://github.com/rerun-io/rerun/) Viewer.
It uses the [external data loader mechanism](https://www.rerun.io/docs/howto/open-any-file#external-dataloaders) to add this capability to the viewer without modifying the viewer itself.
https://github.com/rerun-io/rerun-loader-python-example-tfrecord/assets/9785832/912641d2-b9d8-4039-b7c9-03d3beafd2b9
External data loaders are executables that are available to the Rerun Viewer via the `PATH` variable, with a name that starts with `rerun-loader-`.
This example is written in Python, and uses [TensorFlow](https://www.tensorflow.org/) to read the files. The events are then logged to Rerun.
> NOTE: Not all events are supported yet. Scalars, images, text, and tensors should work. Unsupported events are skipped.
## Installing the Rerun Viewer
The simplest option is just:
```bash
pip install rerun-sdk
```
Read [this guide](https://www.rerun.io/docs/getting-started/installing-viewer) for more options.
## Installing the plugin
### Installing pipx
The most robust way to install the plugin to your `PATH` is using [pipx](https://pipx.pypa.io/stable/).
If you don't have `pipx` installed on your system, you can follow the official instructions [here](https://pipx.pypa.io/stable/installation/).
### Installing the plugin with pipx
Now you can install the plugin to your `PATH` using
```bash
pipx install git+https://github.com/rerun-io/rerun-loader-python-example-tfrecord.git
pipx ensurepath
```
Note: you can use the `--python` argument to specify the Python interpreter to use with pipx.
On unix-like systems `--python $(which python)` will use the currently active Python.
Make sure it's installed by running it from your terminal, which should output an error and usage description:
```bash
rerun-loader-tfrecord
usage: rerun-loader-tfrecord [-h] [--recording-id RECORDING_ID] filepath
rerun-loader-tfrecord: error: the following arguments are required: filepath
```
## Try it out
### Download an example `xxx.tfevents.xxx` file
```bash
curl -OL https://github.com/rerun-io/rerun-loader-python-example-tfrecord/raw/main/events.tfevents.example
```
### Open in the Rerun Viewer
You can either first open the viewer, and then open the file from there using drag-and-drop or the menu>open… dialog,
or you can open it directly from the terminal like:
```bash
rerun events.tfevents.example
```