Controlling model tuning availability

Due to the large resource demands of tuning foundation models, you might want to disable a tuning method that is available in Tuning Studio to free up resources. You can wait to enable a full fine tuning method when you're ready to tune a foundation model.

Before you begin

You must be a cluster administrator.
Before you enable full fine tuning, be sure you have the necessary resources available to support tuning in the Tuning Studio.
- For details about the overall resources that are required for the service, see Hardware requirements.
- There must be enough GPUs for the foundation model that you plan to tune. For details about GPU requirements per foundation model, see System requirements for foundation models in IBM watsonx.ai.
- For details about the system requirements for various tuning methods, see Planning for foundation model tuning in IBM watsonx.ai.

Procedure

You can control full fine tuning availability by enabling or disabling the settings.

Set the wml-cr to maintenance mode with the following command:

oc patch wmlbase wml-cr \
--namespace=${PROJECT_CPD_INST_OPERANDS} \
--type=merge \
--patch='{"spec":{"ignoreForMaintenance": true}}'

Update the training configuration with the following command to disable full fine tuning:

oc patch cm wmltrainingconfigmap \
--namespace=${PROJECT_CPD_INST_OPERANDS} \
--type=merge \
--patch='{"service":{"fine_tuning": {"full.enabled": false}}}'

Note the names of training pods after running the following command:
```
oc get pods | grep wmltraining
```
Restart the training pods by using the pod names :
```
oc delete pod <training-pod-name>
```
Note the names of Watson Studio pods after running the following command:
```
oc get pods | grep portal-ml-dl
```
Restart the Watson Studio pods by using the pod names:
```
oc delete pod <studio-pod-name>
```

What to do next

To get started with tuning foundation models, see Tuning Studio.