Compute

With flexible compute, you can define compute resources from Kubernetes or Databricks clusters that you can then assign to run your generator training and synthetic data generation workloads. You can use compute resources to add GPU-capable compute that can significantly speed up the time to train new generators and generate synthetic datasets.

Add a new Kubernetes compute

You can define any of your existing Kubernetes compute resources as a new compute in MOSTLY AI.

Prerequisites

• Before you define a new compute, check the available compute resources in your Kubernetes cluster.
• You need to be a super admin to add a new compute.

Steps

1. From the profile menu, select Compute.
2. Click + New compute.
3. Select Kubernetes.
4. Define the compute configuration.

   Kubernetes compute parameter	Description
   Name	A unique name for the compute.
   CPUs	The number of CPU cores.
   Memory	The amount of memory.
   GPUs	The number of GPUs.
   GPU memory	The amount of GPU memory.
   Toleration	A toleration that matches the taint defined for your cluster. ⚠️ Leave empty if in the values.yaml you need to set the mostly_coordinator.deployment.core_job.tolerationOperator to Exists.
   Order Index	The order in which the compute appears in the Compute drop-down. The default compute has order_index=1.

   
5. Click Save.

Result

The next time users configure a new generator training or synthetic dataset, they can select the new compute from the Compute drop-down list.

In addition, the new compute remains available in the Computes list.

Add a new Databricks compute

You can add Databricks computes for use in MOSTLY AI.

Steps

1. From the profile menu, select Compute.
2. Click + New compute.
3. Select Databricks.
4. Define the Databricks compute configuration.

   Databricks compute parameter	Description
   Name	Set a name for the new Databricks compute.
   Instance URL	Set your Databricks instance URL.
   Cluster ID	Set your Databricks cluster ID.
   Access token	Set your Databricks access token.
   Order index	Set the order in which the Databricks compute appears in the Compute drop-down.

   
5. Click Save.

Result

The Databricks compute now appears in the list of available computes.

What’s next

You can now assign the Databricks compute to a generator training or synthetic dataset.

Edit a compute

You can make changes to exising computes.

Steps

1. Open the Compute kebab menu and select Edit.
2. Change the compute configuration.
3. Click Save.

Result

The compute configuration is now updated.

Delete a compute

You can delete an existing compute from its kebab menu.

Steps

1. Open the Compute kebab menu and select Delete.
2. Click Yes, delete compute in the confirmation pop-up.