Skip to content

Tensorflow to ONNX

Download link for necessary files: Tensorflow to ONNX files.

In this example we will show you the following: How to convert a Tensorflow based image classification algorithm to ONNX and run it on UbiOps using the ONNX runtime.

Overview of the Deployments

The resulting deployment is made up of the following:

Deployment Function
tf A deployment that uses a trained Tensorflow model
onnx The same model but now running on the ONNX runtime

How does it work?

Step 1: Login to your UbiOps account at and create an API token with project editor admin 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.

Creating an API token

Give your new token a name, save the token in safe place and assign the following roles to the token: project editor and blob admin. These roles can be assigned on project level.

Download link for necessary files: Tensorflow to ONNX files 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 tf-to-onnx-mnist 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.