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https://github.com/torrvision/crayon
A language-agnostic interface to TensorBoard
https://github.com/torrvision/crayon
crayon data-visualization deep-learning docker pytorch tensorboard tensorflow torch7
Last synced: 3 months ago
JSON representation
A language-agnostic interface to TensorBoard
- Host: GitHub
- URL: https://github.com/torrvision/crayon
- Owner: torrvision
- License: mit
- Archived: true
- Created: 2016-12-10T22:47:02.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-11-09T21:10:00.000Z (about 7 years ago)
- Last Synced: 2024-09-24T02:29:06.795Z (4 months ago)
- Topics: crayon, data-visualization, deep-learning, docker, pytorch, tensorboard, tensorflow, torch7
- Language: Python
- Homepage:
- Size: 87.9 KB
- Stars: 779
- Watchers: 29
- Forks: 59
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-list-docker - crayon - agnostic interface to TensorBoard (tensorflow)
README
# Crayon [](https://travis-ci.org/torrvision/crayon) [](https://pypi.python.org/pypi/pycrayon/)
Crayon is a framework that gives you access to the visualisation power
of
[TensorBoard](https://github.com/tensorflow/tensorboard) with
**any language**. Currently it provides a Python and a Lua interface, however
you can easily implement a wrapper around the
provided [RESTful API](doc/specs.md).
---
This system is composed of two parts:
* A server running on a given machine that will be used to display tensorboard
and store all the data.
* A client embedded inside your code that will send the datas to the server.
Note that the server and the client *do not* have to be on the same machine.
## Install
### Server machine
The machine that will host the server needs to
have [docker](https://www.docker.com/) installed. The server is completely
packaged inside a docker container. To get it, run:
```bash
$ docker pull alband/crayon
```
### Client machine
The client machine only need to install the client for the required language.
Detailed instructions can be read by nagivating to
their [respective directories](client/).
TL;DR:
* Lua / Torch - `$ luarocks install crayon`
* Python 2 - `$ pip install pycrayon`
* Python 3 - `$ pip3 install pycrayon`
## Usage
### Server machine
To start the server, run the following:
```bash
$ docker run -d -p 8888:8888 -p 8889:8889 --name crayon alband/crayon
```
Tensorboard is now accessible on a browser at `server_machine_address:8888`. The
client should send the data at `server_machine_address:8889`.
### Client
See the documentation for the required language:
* [Lua](client/lua/README.md#usage-example)
* [Python](client/python/README.md#usage-example)