review-topic-local-vs-remote-execution

name

review-topic-local-vs-remote-execution

description

Load when a PR touches how a step/model runs in-process vs. over the network: Workflow-block execution-mode dispatch (self._step_execution_mode, StepExecutionMode.LOCAL/REMOTE, run_locally/run_remotely), a block get_restrictions()/get_air_gapped_availability(), backend/runtime routing env (USE_INFERENCE_MODELS, DISABLED_INFERENCE_MODELS_BACKENDS, WORKFLOWS_REMOTE_API_TARGET, inference_models_adapters.py), CUDA/Jetson/ONNX gating, or a new block with network/LAN/same-process assumptions.

Review topic: Local vs remote / hosted execution compatibility

When this applies

Load when a diff does any of:

• Adds/edits a run() dispatch on self._step_execution_mode (LOCAL vs REMOTE) or a run_locally / run_remotely pair.
• Adds/edits a block's get_restrictions() or get_air_gapped_availability() where the block reaches the network or needs same-process / LAN access (PTZ/ONVIF camera, local filesystem, GPU-resident handles).
• Touches runtime routing env: USE_INFERENCE_MODELS, DISABLED_INFERENCE_MODELS_BACKENDS, WORKFLOWS_STEP_EXECUTION_MODE, WORKFLOWS_REMOTE_API_TARGET, LOCAL_INFERENCE_API_URL, _PLATFORM_SPECIFIC_USE_INFERENCE_MODELS_DEFAULT, or backend selection in inference/core/models/inference_models_adapters.py / inference/models/utils.py.
• Gates behavior on hardware/deps: CUDA version, Jetson (jetson, l4t, JP5/JP6), ONNX execution providers, TRT — where one runtime path is fixed/tested but siblings are not.
• Changes hosted/serverless/dedicated cold-start / processing-time headers or WebRTC/modal worker plumbing.

This is a behavior/content trigger, not a single directory — the paths above are hints.

Cross-refs (owned elsewhere, not reviewed here):

• Serverless auth/usage fail-open behavior → review-topic-auth-and-tenant-security.
• Stateful-block restriction contract (in-process trackers/counters/cooldown/append-log needing a SOFT restriction) → review-topic-workflow-state-management.

Review checklist

• BLOCK — run() dispatches on StepExecutionMode with an explicit else: raise ValueError for unknown modes (missing else swallows new modes silently).
• BLOCK — a runtime-routing change (USE_INFERENCE_MODELS, DISABLED_INFERENCE_MODELS_BACKENDS, platform default) leaves BOTH the on and off value working and tested; the legacy ONNX registry entries are still present. Breaking the DEFAULT runtime with no working fallback is a production regression.
• BLOCK — a block that raises / hard-fails on a runtime the platform will still offer carries a matching RuntimeRestriction (right severity, applies_to_step_execution_modes, applies_to_runtimes); metadata that lies makes the UI offer a path that fails at runtime.
• FLAG — run_locally and run_remotely have matching signatures and forward every manifest param; a param wired into only one path is a divergence users hit only on the other runtime.
• FLAG — a new network/LAN/same-process block overrides get_air_gapped_availability() if it needs internet, and adds a HARD restriction excluding hosted/dedicated (LAN) or REMOTE step execution (same-process).
• FLAG — a platform-specific default (_PLATFORM_SPECIFIC_USE_INFERENCE_MODELS_DEFAULT, Windows/Jetson forks) keeps the non-default OS working; the diff must not assume one platform.
• FLAG — CUDA/Jetson/ONNX-provider introspection changes are correct on every runtime, not just the author's box (one-runtime introspection bugs mis-route model loading).
• NIT — restriction present but mis-scoped or wrong severity (HARD where SOFT fits, missing applies_to_runtimes).

Not blocking

• Do NOT demand a hosted-platform test for a change with no observable hosted/serverless behavior difference (pure local refactor, docstrings, typing). Hosted coverage under tests/inference/hosted_platform_tests/ is required only when the diff changes what a request returns on lambda / serverless / dedicated.
• Do NOT demand a restriction for a block that is genuinely runtime-agnostic (stateless, no network, no LAN, no GPU-handle assumption).
• Do NOT demand a flip-the-flag-both-ways test for env vars that only tune values (URLs, timeouts) without forking code paths.

Standards

1. Execution-mode dispatch is exhaustive. run() branches on self._step_execution_mode and terminates in else: raise ValueError(f"Unknown step execution mode: ..."). Canonical shape: inference/core/workflows/core_steps/models/roboflow/object_detection/v1.py — run() branches StepExecutionMode.LOCAL / StepExecutionMode.REMOTE, run_locally via self._model_manager, run_remotely via InferenceHTTPClient honoring WORKFLOWS_REMOTE_API_TARGET / LOCAL_INFERENCE_API_URL. (StepExecutionMode is defined in inference/core/workflows/prototypes/block.py and re-exported from core_steps/common/entities.py.)

2. Local and remote paths stay semantically equivalent. run_locally and run_remotely accept the same params and return the same shape; every manifest field is forwarded on both paths (one-path param wiring is a silent divergence).

3. Metadata must match behavior. A block's get_restrictions() / get_air_gapped_availability() declarations are consumed by the describe endpoint and the platform UI to decide where a block may run. The declaration and the actual run() guard must agree — a code path that raises for a runtime needs a RuntimeRestriction for it, and vice-versa. LAN/same-process shape: onvif_movement/v1.py (HARD + StepExecutionMode.REMOTE, plus HARD + Runtime.HOSTED_SERVERLESS/Runtime.DEDICATED_DEPLOYMENT because hosted can't reach a customer LAN).

4. Runtime routing has no orphaned path. A new backend, env flag, or hardware gate leaves BOTH the on and off value working and tested; the legacy path (ONNX registry entries) stays intact. Backend routing lives in inference/core/models/inference_models_adapters.py + inference/core/env.py (USE_INFERENCE_MODELS, _PLATFORM_SPECIFIC_USE_INFERENCE_MODELS_DEFAULT, DISABLED_INFERENCE_MODELS_BACKENDS). Rollout history: #2096 added per-deployment DISABLED_INFERENCE_MODELS_BACKENDS; #2128 added missing registry entries so RFDETR-NAS works under inference-models; #2136 reverted making inference-models the default backend (the default flip is contentious — review carefully).

5. Per-platform introspection is correct on every runtime. CUDA-version / Jetson / ONNX-provider detection that gates model loading must hold across platforms, not just the author's (CUDA acquisition fixed in #2515; Jetson environment detection in runtime introspection + dockerfiles fixed in #1796).

6. Hosted-only code paths get hosted coverage. Behavior that differs on lambda / serverless / dedicated is exercised in tests/inference/hosted_platform_tests/ (parametrized over PlatformEnvironment via conftest.py; see test_workflows.py, test_roboflow_models.py). Hosted-only plumbing regresses silently under local-only tests (remote cold-start header aggregation reverted in #2222; ROBOFLOW_ENVIRONMENT propagation to the modal webrtc worker fixed in #2179; dedicated hosted=True/DEDICATED_DEPLOYMENT_ID usage attribution fixed in #817).

Key files & Reference PRs

Files:

• inference/core/workflows/prototypes/block.py — the contract: StepExecutionMode, Runtime, Severity, RuntimeRestriction, presets (STATEFUL_VIDEO_HTTP_SOFT_RESTRICTION, COOLDOWN_HTTP_SOFT_RESTRICTION), and get_restrictions / get_air_gapped_availability / get_execution_engine_compatibility hooks. Read first.
• inference/core/workflows/core_steps/models/roboflow/object_detection/v1.py — canonical LOCAL/REMOTE dispatch.
• inference/core/workflows/core_steps/sinks/onvif_movement/v1.py — same-process + LAN block done right (two HARD restrictions).
• inference/core/models/inference_models_adapters.py + inference/core/env.py — backend routing with per-platform default and per-deployment disable.
• tests/inference/hosted_platform_tests/conftest.py (+ test_workflows.py, test_roboflow_models.py) — hosted/serverless/localhost parametrization.

Reference PRs: #2096, #2128, #2136 (backend rollout / default flip), #2515, #1796 (per-platform introspection), #2222, #2179, #817 (hosted-only plumbing).