skypilot-workflows

To submit and monitor SkyPilot workflows on Kubernetes, use a unified command interface that handles job lifecycle management across VM and Kubernetes controllers. This approach ensures consistent execution and tracking of distributed tasks throughout the infrastructure.

Secret management and environment isolation for deterministic cloud job execution are handled by bootstrapping isolated virtual environments and securely injecting credentials. This pattern ensures consistent workflow runner execution without exposing sensitive data.

Yes, you can use SkyPilot to run large-scale Isaac Lab reinforcement learning jobs. The orchestration patterns provide the necessary steps to submit the workflow, manage secrets, and handle controller cleanup safely for distributed training tasks.

The best way to debug distributed training tasks across cloud infrastructure is to use an orchestration interface that standardizes the authoring and execution of SkyPilot pipelines. This allows you to monitor and troubleshoot workloads running on Nebius.

Bootstrapping an isolated virtual environment before running a workflow runner is required to ensure consistent and deterministic job execution. Environment management automates this setup to prevent dependency conflicts during distributed training or simulation tasks.

Yes, SkyPilot orchestration supports both Kubernetes and VM controllers for cloud workloads. It provides a unified command surface to launch and monitor distributed jobs across the available infrastructure within the Nebius Physical AI ecosystem.