Managing Models

View the full CLI/SDK Docs for Models here https://paperspace.github.io/gradient-cli/gradient.cli.html#gradient-models

Create a Model

There are two ways to create a Model in Gradient, and both can be done via the web UI or CLI:

1. Run an Experiment that generates a Model

You can do this via the UI or via the CLI by using one of the sample Experiment commands; be sure to set both --modelPath and --modelType according to those instructions. This will place your Model in your Project's Model Repository.

View an example using TensorFlow.

2. Upload a Model

To upload a Model via the Web UI, first navigate to the Models page.

From there, click Upload a Model +

This will open up a modal to Upload a Model, where you can drag 'n drop a Model file from your local machine (or click to find it locally), as well as select the model Type and provide a Name, custom Summary, and any Additional Notes as metadata. Additionally, you can click "Or Upload a directory" to select a local folder.

Then click Upload Model. This will upload and register the Model in Gradient.

You can upload a Model via the CLI with the gradient models upload subcommand:

gradient models upload downloads/squeezenet1.1.onnx --name squeezenet --modelType ONNX

Whether you use the Web UI or CLI, you've now successfully uploaded a Model into Gradient!

Note: Uploaded Models will not be associated with an Experiment.

Now that you have a Model, whether uploaded or generated by running an Experiment, read on to learn how you can use it to create a Deployment.

View Models in Your Model Repository

You can view your team's Models in your Model Repository via the Web UI or CLI, as seen below.

Navigate to Models in the side nav to see your list of trained Models:

As you can see, the Web UI view shows your Model ID, when the model was created, the S3 bucket location of your model, your metrics summary data, the Experiment ID, the model type, and whether it is currently deployed on Paperspace.

You can click Deploy Model to Create a Deployment with your Model. And you can click Open Detail to see a more detailed view of the Model's performance metrics. This will also show a list of all of the checkpoint files (artifacts) generated by the Experiment, as well as the final Model at hand, and you can download any of those files.

Alternately, you can view your Models (currently with less detailed info) via the CLI by running gradient models list.

$ gradient models list

+------+-----------------+------------+------------+----------------+
| Name | ID              | Model Type | Project ID | Experiment ID  |
+------+-----------------+------------+------------+----------------+
| None | moilact08jpaok  | Tensorflow | prcl68pnk  | eshq20m4egwl8i |
| None | mos2uhkg4yvga0p | Tensorflow | prcl68pnk  | ejuxcxp2zbv0a  |
| None | moc7i8v6bsrhzk  | Custom     | prddziv0z  | e5rxj0aqtgt2   |