Updating software specifications from the UI

1. From the deployment space, click the model (make sure it does not have any active deployments.)
2. Click the i symbol to check model details.
3. Use the dropdown list to update the software specification.

Refer to the example image:

Updating software specifications by using the API

You can update a software specification by using the API Patch command:

For software_spec field, type /software_spec. For value field, use either the ID or the name of the new software specification.

Refer to this example:

curl -X PATCH '<deployment endpoint url>/ml/v4/models/6f01d512-fe0f-41cd-9a52-1e200c525c84?space_id=f2ddb8ce-7b10-4846-9ab0-62454a449802&project_id=<project_id>&version=<YYYY-MM-DD>' \n--data-raw '[
   {
      "op":"replace",
      "path":"/software_spec",
      "value":{
         "id":"6f01d512-fe0f-41cd-9a52-1e200c525c84"   // or "name":"tensorflow_rt22.1-py3.9"
      }
   }
]'

For more information, see Updating an asset by using the Patch API command.

Option 1: Save the model with a compatible framework

1. Download the model by using either the watsonx.ai Runtime Data and AI Common Core API or the watsonx.ai Python client library.

   The following example shows how to download your model:

   client.repository.download(<model-id>, filename="xyz.tar.gz")
   

2. Edit model metadata with the model type and version that is supported in the current release. For more information, see Software specifications and hardware specifications for deployments.

   The following example shows how to edit model metadata:

   model_metadata = {
      client.repository.ModelMetaNames.NAME: "example model",
      client.repository.ModelMetaNames.DESCRIPTION: "example description",
      client.repository.ModelMetaNames.TYPE: "<new model type>",
      client.repository.ModelMetaNames.SOFTWARE_SPEC_UID:
      client.software_specifications.get_uid_by_name("<new software specification name>")
   }
   

3. Save the model to the watsonx.ai Runtime repository. The following example shows how to save the model to the repository:

   model_details = client.repository.store_model(model="xyz.tar.gz", meta_props=model_metadata)
   

4. Deploy the model.

5. Score the model to generate predictions.

If deployment or scoring fails, the model is not compatible with the new version that was used for saving the model. In this case, use Option 2.

Option 2: Retrain the model with a compatible framework

1. Retrain the model with a model type and version that is supported in the current version.
2. Save the model with the supported model type and version.
3. Deploy and score the model.

It is also possible to update a model by using the API. For more information, see Updating an asset by using the Patch API command.

Option 1: Save the Python function with a compatible runtime or software specification

1. Download the Python function by using either the watsonx.ai Runtime REST API or the watsonx.ai Python client library.
2. Save the Python function with a supported runtime or software specification version. For more information, see Software specifications and hardware specifications for deployments.
3. Deploy the Python function.
4. Score the Python function to generate predictions.

If your Python function fails during scoring, the function is not compatible with the new runtime or software specification version that was used for saving the Python function. In this case, use Option 2.

Option 2: Modify the function code and save it with a compatible runtime or software specification

1. Modify the Python function code to make it compatible with the new runtime or software specification version. In some cases, you must update dependent libraries that are installed within the Python function code.
2. Save the Python function with the new runtime or software specification version.
3. Deploy and score the Python function.

It is also possible to update a function by using the API. For more information, see Updating an asset by using the Patch API command.