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https://github.com/keplr-io/hera
Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.
https://github.com/keplr-io/hera
dashboard deep-learning visualization
Last synced: about 2 months ago
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Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.
- Host: GitHub
- URL: https://github.com/keplr-io/hera
- Owner: keplr-io
- License: mit
- Created: 2016-07-10T16:55:41.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-05-24T22:26:10.000Z (over 7 years ago)
- Last Synced: 2024-08-01T18:28:51.494Z (5 months ago)
- Topics: dashboard, deep-learning, visualization
- Language: JavaScript
- Homepage:
- Size: 152 KB
- Stars: 487
- Watchers: 19
- Forks: 47
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-datascience - Hera
README
# Hera
Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser.
## Setting up
### Step 1. Plant the spy
**Install the package**
```
pip install heraspy
```
**Add the callback**
```python
herasCallback = HeraCallback(
'model-key',
'localhost',
4000
)
model.fit(X_train, Y_train, callbacks=[herasCallback])
```
### Step 2. Start the server
Git clone this repository, then run
```
cd server
npm install
gulp # optional, for now the build file is kept track in git
node build/server
```
### Step 3. Start the dashboard
```
cd client
npm install
npm start
```
## Using RabbitMQ
By default hera uses socket.io for messaging - both from keras callback to server, and from server to dashboard. This is to minimize the number of things one needs to install before getting up and running with hera.
However, in production socket.io is outperformed by a number of alternatives, also it is good in general to decouple the server-client communication from the inter-process communitation (python -> node) so that each can be managed and optimized independently.
To demonstrate how this works Hera ships with the option to use rabbitMQ for interprocess communication. Here's how to use it.
**In your model file**
```python
from heraspy.callback import HeraCallback
from heraspy.dispatchers.rabbitmq import get_rabbitmq_dispatcher
herasCallback = HeraCallback(
'model-key', 'localhost', 4000,
dispatch=get_rabbitmq_dispatcher(
queue='[my-queue]',
amqps_url='amqps://[user]:[pass]@my-amqp-address'
)
)
```
**In server/src/server.js**
Replace the only line in the file with
```js
getServer({
dispatcher: 'rabbitmq',
dispatcherConfig: {
amqpUrl: 'amqps://[user]:[pass]@my-amqp-address',
amqpQueue: '[my-queue]'
}
}).start();
```
That's it! Now communication from the python process and the node webserver process goes through rabbitmq.
## Credits
Aside from the obvious ones:
- The dashboard is built on [React-Redux starter kit](https://github.com/davezuko/react-redux-starter-kit)