Download link for necessary files: Tensorflow files.
In this example we will show you the following: How to create a deployment that uses a built tensorflow model to make predictions on the fuel efficiency of late-1970s and early 1980s automobiles or MPG (miles per gallon).
The deployment is configured as follows:
|Function||A deployment that uses a built tensorflow model to make predictions on the fuel efficiency of late-1970s and early 1980s automobiles or MPG (mile per gallon).|
|Input field:||name: data, data_type: Blob (file)|
|Output field:||name: prediction, data_type: Blob (file)|
How does it work?¶
Step 1: Login to your UbiOps account at https://app.ubiops.com/ and create an API token with project editor rights. To do so, click on Permissions in the navigation panel, and then click on API tokens. Click on [+]Add token to create a new token.
Give your new token a name, save the token in safe place and assign the following role to the token: project editor. This role can be assigned on project level.
Step 2: Download the tensorflow-ubiops-example folder and open
tensorflow_template.ipynb. In the notebook you will find a space
to enter your API token and the name of your project in UbiOps. Paste the saved API token in the notebook in the indicated spot and enter the name of the project in your UbiOps environment. This project name can be found in the top of your screen in the WebApp.
Step 3: Run the Jupyter notebook tensorflow_template and everything will be automatically deployed to your UbiOps environment! Afterwards you can explore the code in the notebook or explore the application in the WebApp.