Forget provisioning.
What if your training job spun up its own isolated container, ran on your selected GPUs, and tore down clean the moment it finished?

Here is the flow:

Open the Ocean Network Dashboard and pick the GPUs that match your specs. Lock the environment you want and take it straight to your IDE.

Write your training job the way you already do, point it at the data, and dispatch. It runs sealed in its own container on the hardware you selected, the data never leaves where it lives, and you get the output back.

The moment it finishes, the container tears down clean.

Learn more about IDE-native GPU deployment: https://docs.oncompute.ai/ocean-orchestrator/using-ocean-orchestrator-with-ocean-dashboard