Setup
Get started with Embedl Hub.
Create an Embedl Hub account
To get started with Embedl Hub, you’ll first need to create a free account. After you’ve signed up, we invite you to join our Slack community. Although joining the community isn’t required, we’d love to meet you and learn what excites you about efficient edge AI.
Install the Python library
With the Embedl Hub Python library, you can quantize, compile, and profile models on real edge devices in the cloud. The library logs metrics and parameters, allowing you to analyze your results on the Embedl Hub website and to reproduce them later.
The simplest way to install the library is through pip:
pip install embedl-hubDue to conflicting dependencies between tensorflow and ai-edge-tensorflow one additional
package, tf-keras, needs to be installed separately without dependencies:
pip install tf-keras --no-depsConfigure an API key
The Embedl Hub Python library requires an API key for authentication. To start, create one under Personal API keys on your profile page.
Keep the key secret. Anyone with access to it will be able to read and write to your Embedl Hub projects.
We recommend configuring the API key using the embedl-hub CLI:
embedl-hub auth --api-key <your-key>Your key will be stored in the plaintext configuration file at ~/.config/embedl-hub/config.yaml, and any existing key in the file will be
overwritten.
For alternative ways to configure your API key, see the configuration guide.
(Optional) Set up a remote hardware cloud
Embedl Hub enables you to evaluate your models on real edge AI devices without needing physical access to the devices. Using the Embedl Hub device cloud requires no additional set up.
Follow the cloud-specific setup instructions to use remote devices from third-party device clouds: