This sample script shows how you can use the Firebase Admin SDK to manage your Firebase-hosted ML models.
See the developer guide for more information on model management.
Clone the quickstart repository and install the ML quickstart's dependencies:
$ git clone https://github.com/firebase/quickstart-nodejs.git $ cd quickstart-nodejs/machine-learning $ npm install $ chmod u+x manage-ml.js # Optional
If you don't already have a Firebase project, create a new project in the Firebase console. Then, open your project in the Firebase console and do the following:
- On the Settings page, create a service account and download the service account key file. Keep this file safe, since it grants administrator access to your project.
- On the Storage page, enable Cloud Storage. Take note of your default bucket name (or create a new bucket for ML models.)
- On the ML Kit page, click Get started if you haven't yet enabled ML Kit.
In the Google APIs console, open your Firebase project and enable the Firebase ML API.
At the top of
manage-ml.js
, set theSERVICE_ACCOUNT_KEY
andSTORAGE_BUCKET
:const SERVICE_ACCOUNT_KEY = '/path/to/your/service_account_key.json'; const STORAGE_BUCKET = 'your-storage-bucket';
$ ./manage-ml.js list fish_detector 8716935 vision barcode_scanner 8716959 vision $ ./manage-ml.js new yak_detector -f ~/yak.tflite --tags vision,experimental Uploading model to Cloud Storage... Model uploaded and published: yak_detector 8717019 experimental, vision $ ./manage-ml.js new emu_detector -a projects/12345/locations/us-central1/models/ICN12345 Model uploaded and published: emu_detector 8717033 $ ./manage-ml.js update 8717019 --remove_tags experimental $ ./manage-ml.js delete 8716959 $ ./manage-ml.js list fish_detector 8716935 vision yak_detector 8717019 vision emu_detector 8717033 $