Quantized version of TensorFlow that has reduced compatibility for models but supports more hardware. Uploaded models must adhere to the model requirements.
A full framework that was built primarily for research. Because of this, it is much faster to do iterative development with (model doesn’t have to be predefined) but it is not as “production ready” as TensorFlow. It is the most common framework for OSS models because it is the go-to framework for ML researchers.
Note
For some models of the ML model service, like the Triton ML model service for Jetson boards, you can configure the service to use either the available CPU or a dedicated GPU.
Click Select model and select a model from your organization or the registry.
Save your config.
Models available to deploy on the ML Model service
You can also use these publicly available machine learning models with an ML model service:
Model
Type
Framework
Description
Deploy a specific version of an ML model
When you add a model to the ML model service in the app interface, it automatically uses the latest version.
In the ML model service panel, you can change the version in the version dropdown.
Save your config to use your specified version of the ML model.
How the ML model service works
The service works with models trained inside and outside the Viam app:
You can upload externally trained models from a model file on the MODELS tab in the DATA section of the Viam app.
You can use a model trained outside the Viam platform whose files are on your machine. See the documentation of the model of ML model service you’re using (pick one that supports your model framework) for instructions on this.
On its own the ML model service only runs the model.
After deploying your model, you need to configure an additional service to use the deployed model.
For example, you can configure an mlmodel vision service to visualize the inferences your model makes.
Follow our docs to run inference to add an mlmodel vision service and see inferences.
ML models must be designed in particular shapes to work with the mlmodelclassification or detection model of Viam’s vision service.
See ML Model Design to design a modular ML model service with models that work with vision.