https://github.com/jolibrain/deepdetect-js
DeepDetect javascript client
https://github.com/jolibrain/deepdetect-js
browser deepdetect javascript machine-learning nodejs
Last synced: about 1 month ago
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DeepDetect javascript client
- Host: GitHub
- URL: https://github.com/jolibrain/deepdetect-js
- Owner: jolibrain
- License: mit
- Created: 2018-05-07T12:59:13.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-03-21T16:30:15.000Z (about 1 year ago)
- Last Synced: 2024-10-31T19:46:43.880Z (7 months ago)
- Topics: browser, deepdetect, javascript, machine-learning, nodejs
- Language: JavaScript
- Size: 953 KB
- Stars: 8
- Watchers: 5
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# deepdetect-js
[](#contributors-)
> DeepDetect JS client
## Files
* ```src/index.js``` - client source code
* ```src/index.test.js``` - client methods tests
* ```doc/web-example/server.js``` - simple webserver to serve web-example index.html and proxy api calls to a deepdetect server
* ```doc/web-example/index.html``` - deepdetect-js web integration demo
## Usage
### Web integration
DeepDetect-JS can be used on a webpage, you probably should run deepdetect server behind a http-proxy to avoid same-origin policy issues.
A simple webserver demo is available on ```http://localhost:3000``` when running the following command:
```sh
yarn run web-example
```
Here is the simple ```/info``` api call on a DeepDetect server.
Note the ```{path: 'api'}``` parameter when initializing ```DD``` object.
```html
...
async function fetchInfo() {
const dd = new deepdetect.DD({path: 'api'});
const info = await dd.info();
document.getElementById('infoResult').innerHTML = JSON.stringify(info);
}
fetchInfo();
...
```
### NodeJS integration
Following usage examples will use nodejs, install it with this command:
```sh
npm install --save deepdetect-js
```
### Connect to DeepDetect server, and fetch informations
Here is the simplest way to get information about a DeepDetect server:
```js
import DD from 'deepdetect-js';
async () => {
const dd = new DD()
// Get DeepDetect server info
const info = await dd.info()
console.log(info);
}
```
You can also specified the DeepDetect server host and port options:
```js
import DD from 'deepdetect-js';
async () => {
const dd = new DD('10.10.10.1', 8580)
// Get DeepDetect server info
const info = await dd.info()
console.log(info);
}
```
### Service API
Once connected to a DeepDetect server, the Service API allows to:
* create a service
* fetch informations about a service
* delete a service
```js
import DD from 'deepdetect-js';
async () => {
const dd = new DD()
// Create a service
const serviceName = 'myserv';
const serviceConfig = {
description: 'example classification service',
model: {
repository: '/home/me/models/example',
templates: '../templates/caffe'
},
mllib: 'caffe',
parameters: {
input: { connector: 'txt' },
mllib: { nclasses: 20 },
output: {},
},
};
const createService = await dd.putService(serviceName, serviceConfig)
// Fetch service information
const service = await dd.getService(serviceName);
console.log(service);
// Delete service
const deleteService = await dd.deleteService(serviceName, {clear: 'full'});
}
```
### Train API
Once connected to a DeepDetect server, the Train API allows to:
* Create a training job
* Get information on a non-blocking training job
* Kills a non-blocking training job
```js
import DD from 'deepdetect-js';
async () => {
const dd = new DD()
const serviceName = 'myserv';
// Create a training job
const train = await dd.postTrain(
serviceName,
[ '/home/me/deepdetect/examples/all/n20/news20' ],
{
test_split: 0.2,
shuffle: true,
min_count: 10,
min_word_length: 3,
count: false,
},
{
gpu: false,
solver: {
iterations: iterationsN20,
test_interval: 200,
base_lr: 0.05,
snapshot: 2000,
test_initialization: true,
},
net: {
batch_size: 100,
},
},
{ measure: ['acc', 'mcll', 'f1'] },
false
);
// Get information on a non-blocking training job
const trainingJob = await dd.getTrain(serviceName);
console.log(trainingJob);
// Kills a non-blocking training job
const deletedTrainingJob = await dd.deleteTrain(serviceName);
console.log(deletedTrainingJob);
}
```
### Predict API
Once connected to a DeepDetect server, the Predict API allows
to makes prediction from data and model
```js
import DD from 'deepdetect-js';
async () => {
const dd = new DD()
const serviceName = 'myserv';
// Predict with measures
const postData = {
service: serviceName,
data: [ '/home/me/deepdetect/examples/all/n20/news20' ],
parameters: {
input: {},
mllib: {
gpu: false,
net: {
test_batch_size: 10,
},
},
output: {
measure: ['f1']
}
}
};
const predict = await dd.postPredict(postData)
console.log(predict);
}
```
## Build and release
1. Modify version number in `package.json`
2. `npm run build`
3. `npm publish` - [documentation](https://www.freecodecamp.org/news/how-to-create-and-publish-your-first-npm-package/)
## Testing
In order to run the test, you first need to run a deepdetect server loccaly on port 8080. To do so, you can use the following docker command:
``` sh
docker run -d -p 8080:8080 docker.jolibrain.com/deepdetect_cpu
```
Then you can run the test suite:
```sh
yarn test
```
If you find and issue with your tests, please check the header parameters available in ```src/index.test.js```.
## Changelog
* 1.8.12 - 21/03/2024 - return !response.ok error from http requests
* 1.8.11 - 19/10/2023 - Add missing process lib
* 1.8.10 - 19/10/2023 - Review dependabot alerts
* 1.8.9 - 17/10/2023 - Update dependencies
* 1.8.8 - 19/10/2021 - Add option to enable/disable Accept-Encoding gzip request header
* 1.8.7 - 05/01/2020 - Replace NaN values in returned json from deepdetect server
* 1.8.4 - 16/10/2020 - [Fix conditional check of options.sameOrigin](https://github.com/jolibrain/deepdetect-js/pull/13) by [eh-dub](https://github.com/eh-dub)
## Contributors
Thanks goes to these people ([emoji key](https://github.com/kentcdodds/all-contributors#emoji-key)):
Alexandre Girard
💻
Ariel Weingarten
💻
This project follows the [all-contributors](https://github.com/kentcdodds/all-contributors) specification. Contributions of any kind welcome!
## License
MIT © [Jolibrain](http://jolibrain.com)