kestrel-cloud-runpod

Runpod GPU cloud provider for Kestrel Sovereign agents. Provision Pods, run LoRA training, manage Pod lifecycle, and submit queue-based Serverless jobs without using Runpod's v1 or GraphQL infrastructure APIs.

See CHANGELOG.md for release notes.

Installation

uv pip install kestrel-cloud-runpod

The feature is auto-discovered by Kestrel Sovereign via the kestrel_sovereign.features entry point — install it alongside kestrel-sovereign and RunPodFeature registers itself at startup.

Configuration

Optional [runpod] section in kestrel.toml for default profile preferences.

What's provided

• RunPodFeature — agent-facing tools for pod search, provisioning, training, lifecycle
• Standalone API: RunPodManager for direct programmatic use
• RunpodControlPlaneClient — typed v2 catalog, Pod, Serverless endpoint, worker/log, and billing client
• RunpodServerlessClient — typed queue job run/status/cancel/retry/health client
• Durable RunPod-backed private Ollama leases with readiness and cost gates

Architecture

• Runpod v2 execution platform — the accepted control-plane, Serverless, catalog inference, and private Ollama design.

Runpod has two distinct v2 services:

Both clients set an explicit application User-Agent because the beta control plane rejects generic/default clients at its edge. Base URLs are injectable for testing but must end in /v2; there is no v1 or GraphQL production fallback.

Direct client example

from kestrel_cloud_runpod import RunpodControlPlaneClient, RunpodServerlessClient
from kestrel_cloud_runpod.models import ComputeProduct

control = RunpodControlPlaneClient(api_key="...")
offers = control.list_gpus(products=(ComputeProduct.SERVERLESS,))

jobs = RunpodServerlessClient(api_key="...")
job = jobs.run("endpoint-id", {"prompt": "hello"})
status = jobs.status("endpoint-id", job.id)

Create calls are never retried automatically. If a connection failure or 5xx makes a Pod, endpoint, or queue-job creation result ambiguous, the client raises RunPodAmbiguousResultError with reconcile_required = True. Ollama leases persist the request fingerprint and deterministic resource name before creation, then recover by listing before any replacement could be authorized.

Private Ollama callers submit a stable OllamaLeaseRequest with owner/workload IDs, the exact model, placement constraints, expected warm utilization, an idle timeout, hard deadline, and maximum spend. The service compares current Pod and Serverless catalog offers. Bursty traffic can use native load-balanced Serverless; sustained sessions can use a dedicated Pod. Queue Serverless is not selected for interactive streaming. lease.public_route_url remains None until both Runpod health and Ollama /api/tags prove the requested model is ready. An external scheduler must run RunPodManager.reconcile_ollama_leases() periodically so expiry and teardown retries survive requester crashes. The kestrel-runpod-reconcile-ollama command performs one fail-fast pass and is suitable for a timer or job runner.

Control-plane, Serverless data-plane, and Pod workload credentials are separate. This package does not ship the runtime image: RUNPOD_OLLAMA_IMAGE must identify a separately built and reviewed image that enforces Authorization: Bearer $KESTREL_OLLAMA_BEARER_TOKEN. The provider injects that value from RUNPOD_OLLAMA_BEARER_TOKEN and refuses to publish a Pod route unless an anonymous /api/tags probe receives 401 or 403. The token is never returned in lease state. AUTO mode considers only products whose scoped credential is configured.

accrued_estimated_cost is intentionally a billing-safe upper bound. Dedicated Pods accrue their continuous live catalog rate. Until the Serverless readiness API exposes active worker-seconds or billing reconciliation supplies them, Serverless uses the same wall-clock bound; it may release early, but it cannot authorize spend beyond the caller's cap. The selection estimate still compares the expected Serverless initialization, active, and idle-tail window against the expected Pod session.

Configuration migration from 0.2

Profiles no longer select a hardcoded gpu_type_id or record a cost_per_hr. Replace legacy fields with workload constraints:

[profiles.image]
name = "Large-memory image generation"
task_type = "image"
image_name = "runpod/kestrel-flux:latest"
min_vram_gb = 40
min_cuda_version = "12.8"
max_cost_per_hr = 3.00
gpu_count = 1

At Pod creation time, the direct provider queries v2 with product-specific availability, rejects offers outside the profile constraints, and records the selected GPU and offered live rate on the session. Legacy gpu_type_id, vram_gb, cost_per_hr, and template_id fields fail with migration guidance. Use registry_id for a v2 registry credential.

The old private CLI SSH helper is also gone. RunPodManager.get_logs() uses the v2 SSE Pod log endpoint. Arbitrary commands must be exposed as scoped workload HTTP operations.

OpenAPI beta pin

The reviewed v2 schema is pinned in vendor/runpod-v2-openapi.yaml with its checksum in vendor/runpod-v2-openapi.lock.json. Unit/contract CI validates the operations and shapes Kestrel consumes. A weekly/manual workflow compares the live schema and reports semantic drift without overwriting the pin.

Dependencies

• kestrel-sovereign-sdk>=0.2,<1 — base Feature, tool, ToolCategory, BackendType
• kestrel-sovereign>=0.7,<1 — kestrel.toml unified-config loader (runtime)
• httpx>=0.27,<1
• requests>=2.32,<3

Development

uv pip install -e '.[test]'
uv run pytest
python scripts/check_runpod_openapi.py --check-pin

An authenticated smoke test is opt-in, read-only, and lists the GPU catalog only:

RUNPOD_API_KEY=... uv run pytest --run-cloud tests/test_runpod_smoke.py

License

Apache-2.0