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CASE FILE / AUTOMATION PROTOTYPE

AUTOMATION PROTOTYPE / PROTOTYPE

RunPod Media Orchestrator

Prototype RunPod workflow for provisioning GPU compute, transferring prompts, running media jobs, retrieving outputs, and tearing down resources.

CASE STUDY5 TECHNOLOGIES
Technical systems artwork for RunPod Media Orchestrator

SYSTEM QUESTION

Make the temporary GPU lifecycle explicit from operator confirmation through provisioning, transfer, execution, retrieval, and teardown.

API credentials, generated prompts, remote shell access, output files, and paid compute cross several privilege and data boundaries.

DIRECT ANSWER

What does RunPod Media Orchestrator do?

Prototype RunPod workflow for provisioning GPU compute, transferring prompts, running media jobs, retrieving outputs, and tearing down resources. It addresses api credentials, generated prompts, remote shell access, output files, and paid compute cross several privilege and data boundaries.

ROLE & SCOPE

ROLE EVIDENCE

The inspected repository establishes an automation implementation but does not establish an individual contributor role.

AUDITED SCOPE

The audited slice covers channel selection, prompt assembly, operator confirmation, RunPod GraphQL calls, readiness polling, SSH and SFTP transfer, remote execution, SCP retrieval, and termination.

CONSTRAINTS

SSH host keys are accepted automatically, exceptions are handled broadly, and no cost, reliability, audience, or production claim is made.

ARCHITECTURE

  1. 01

    internal

    Channel selection

    loads local channel configuration and requires an interactive selection

    channel_manager.py
  2. 02

    internal

    Prompt assembly

    generates video, voice, and music prompt components through Gemini with local fallbacks

    prompt_generator.py
  3. 03

    external

    RunPod GraphQL lifecycle

    creates a secure-cloud GPU request, polls readiness, and terminates the pod

    runpod_auto.py
  4. 04

    boundary

    SSH and SFTP execution

    uploads prompt data and invokes the remote pipeline

    runpod_auto.py
  5. 05

    boundary

    SCP retrieval

    copies the generated media output back to the operator host

    runpod_auto.py

TRUST BOUNDARIES

  1. BOUNDARY 01

    Operator host to RunPod API

    Provisioning and termination requests use an authorization header assembled from configuration.

    runpod_auto.py
  2. BOUNDARY 02

    Operator host to temporary pod

    Prompts, commands, logs, and media files cross SSH-based channels.

    runpod_auto.py

SECURITY DECISIONS

RISKCONTROLSOURCE

Compute provisioning starts without final operator intent.

The workflow requires an explicit confirmation before creating a pod.

runpod_auto.py

A readiness timeout leaves temporary compute active.

The timeout branch invokes pod termination before returning.

runpod_auto.py

IMPLEMENTATION EVIDENCE

  1. sourcerunpod_auto.py

    Pod lifecycle

    GraphQL mutations create and terminate the pod around readiness polling and remote execution.

  2. sourcerunpod_auto.py

    Transfer and execution path

    SFTP writes prompt data, SSH starts the remote pipeline, and SCP retrieves the final media file.

  3. sourcechannel_manager.py

    Local channel selection

    The command-line manager loads JSON channel configurations and requires an interactive selection.

  4. sourceprompt_generator.py

    Prompt generation

    The generator assembles video, voice, and music prompts with explicit local fallbacks.

VERIFIED OUTCOMES

  1. Provisioning, readiness polling, remote execution, retrieval, and teardown are represented in one operator-visible workflow.

    runpod_auto.py

  2. The workflow has an explicit cancellation point before compute allocation.

    runpod_auto.py

OPEN HARDENING

  1. 01

    Replace automatic SSH host-key acceptance with pinned or pre-provisioned host verification.

    runpod_auto.py
  2. 02

    Guarantee pod termination with structured cleanup around every post-provisioning failure path.

    runpod_auto.py