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runpod-deploy

Quickstart
The runpod-deploy run lifecycle
Config Reference
Python API vs. CLI: when to use which
Recipes

GitHub
PyPI

Quickstart
The runpod-deploy run lifecycle
Config Reference
Python API vs. CLI: when to use which
Recipes
Recipe: local pre-flight, then runpod-deploy run
Recipe: pull artifacts, then post-process locally
Recipe: embed deploy metadata in your own artifacts
Recipe: multi-config sweep
Recipe: cost reconciliation across past runs
Recipe: predictions-only-eval
Recipe: flash_attention_2 graceful fallback
Recipe: pod-Python reproducibility
Recipe: multi-manifest forensics via the Python API
Recipe: stock-out diagnostic
Recipe: forensics, then cleanup
Recipe: weekly stale-pod audit
Recipe: reuse the staging payload via a network volume
Recipe: recycle a pod for fast iteration
Examples
API reference
runpod_deploy.config
runpod_deploy.orchestrator
runpod_deploy.provider
runpod_deploy.pricing
runpod_deploy.transport
runpod_deploy.metadata
Extending runpod-deploy
Troubleshooting
RunPod Gotchas
Release workflow
Migration notes (v0.8.2 and prompt-injection-v3)
ADR 0001: Config-First Jobs
ADR 0002: Defer Hook Execution
ADR 0003: Git Tags For Stable Consumption

GitHub
PyPI

Section Navigation

runpod_deploy.provider.PodConnection
runpod_deploy.provider.resolve_volume
runpod_deploy.provider.select_gpu_across_datacenters

API reference
runpod_deploy.provider
runpod_deploy.provider.select_gpu_across_datacenters

runpod_deploy.provider.select_gpu_across_datacenters#

.. currentmodule:: runpod_deploy.provider

.. autofunction:: select_gpu_across_datacenters

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