v1.3.7

🚀 Added

✈️ OFFLINE_MODE — air-gapped deployments

Inference servers can now run fully air-gapped (@alexnorell, #2263). The supported flow is deliberately simple:

1. Warm a mounted cache while network access and a Roboflow API key are available.
2. Restart the same deployment with the same cache, OFFLINE_MODE=True, and no API key.
3. Cached model metadata, weights, and Workflow specifications load with no built-in Roboflow API calls, no retries, and no cache-expiration failures.

📏 YOLO26 depth estimation

Public-pretrained YOLO26 depth estimation lands end-to-end as a drop-in alternative to Depth Anything (@leeclemnet,
#2691): inference_models backends for ONNX, TorchScript, and TensorRT (Ultralytics -depth pretrains, 768×768, metric log-depth head), public aliases yolo26{n,s,m,l,x}-depth-768, and the same five variants selectable in the roboflow_core/depth_estimation@v1 Workflow block. Outputs are normalized to Depth Anything's disparity-style convention (larger = closer), so downstream tooling is interchangeable; the block's default model is unchanged.

Alongside it, depth maps got dramatically cheaper on the wire (@leeclemnet, #2693): the SDK now requests the normalized depth map as a base64 PNG16 (uint16 quantization of the normalized map) instead of a JSON list of floats — roughly 17 MB → ~1 MB for a typical single-image response, with matching decode-time wins.
Raw HTTP callers are unaffected (the server-side default response format is still json), and the SDK decoder transparently accepts responses from older servers.

🔥 New workflow blocks

• Detections Nearest Neighbor (roboflow_core/detections_nearest_neighbor@v1) — a nearest-neighbor spatial join between two detection sets: for each query detection it finds the closest target detection(s) by 2D pixel distance between configurable anchor points (bbox corners/edges/center, or a named keypoint), with tie-awareness, self-match exclusion, and an optional max_distance cutoff (@bczifra, #2698). Emits enriched query predictions (nearest_target_distance) plus index-aligned matched-query/matched-target sets ready for standard downstream blocks.
• Grid Visualization now accepts multiple image inputs directly (@leeclemnet, #2694).

🎛️ Execution & operations

• Per-run workflow sink disabling — a disable_sinks API parameter, delivered to sink blocks through the existing dependency-injection system, turns off built-in sink side effects for a single run while the rest of the workflow executes normally; all 22 built-in side-effect sinks honor it with no manifest or spec changes (@joaomarcoscrs, #2697).
• TensorRT engine builds announce themselves — with the TRT execution provider and a cold engine cache, the first inference silently compiles an engine (measured ~9.5 minutes for a single COCO object-detection model on an Orin 16 GB) while requests time out. The server now logs an explicit warning at session-configuration time, pointing at the mitigation: persist MODEL_CACHE_DIR (@sberan, #2703).
• Billable and errored usage are aggregated separately — usage records now partition by billability, request outcome, structured error_type, and bounded HTTP error_status_code (400–599), so a failed request can no longer blend into a billable aggregate; billing intent is preserved and policy stays with the Roboflow backend. Payload schema is additive-only (@hansent, #2692).
• Roboflow API calls now default to a 120-second timeout (ROBOFLOW_API_REQUEST_TIMEOUT) instead of waiting indefinitely (@sberan, #2702).

🔧 Fixed

• Instance-segmentation dense-mask post-processing memory bounded — mask upscaling now runs in fixed-size chunks and RF-DETR segmentation applies the standard max_detections cap (default 300, overridable via INFERENCE_MODELS_RFDETR_DEFAULT_MAX_DETECTIONS) before the expensive full-resolution work. Peak CUDA memory on a dense-instance workload dropped 17.1 GiB → 4.2 GiB with no wall-time regression (@bigbitbus, #2682).
• Model-access failures keep their real HTTP statuses — Execution Engine v1.12.1 propagates upstream model-access errors (401/402/403/404…) through workflow runs instead of collapsing them into generic 500s (@hansent, #2690; @dkosowski87, #2709).
• PP-OCR — authorization fixed on Serverless (@Erol444, #2646), and the route now returns a clean 404 when the inference_models stack is disabled instead of an internal error (@dkosowski87, #2707).
• Phantom keypoints eliminated — padded keypoint slots are no longer emitted as real keypoints in predictions (@kounelisagis,
  #2677).
• detections_overlaps kind round-trips — serializer and deserializer registered, so the kind survives workflow JSON output/input (@kounelisagis, #2638).
• Grounding DINO accepts both canonical and legacy BERT cache layouts, so existing warmed caches keep working after the cache-layout changes (@dkosowski87, #2716).
• SAM3 package load no longer requires sam_configuration.json (@grzegorz-roboflow, #2678).
• Local package imports no longer write .pyc bytecode during module execution, keeping mounted/read-only caches byte-stable (@dkosowski87, #2713).
• Pre-release security patches — landing-page npm dependency chain moved to patched versions and Python dependency bumps rolled up ahead of the release (@PawelPeczek-Roboflow, #2715).

🚧 Maintenance

• inference-models 0.33.0 / 0.34.1 releases and pins (@PawelPeczek-Roboflow, #2706; @dkosowski87, #2711).
• Windows build fixes (@PawelPeczek-Roboflow, #2689).
• CI — PP-OCR T4 regression tests skipped when USE_INFERENCE_MODELS=False (@grzegorz-roboflow, #2712) and the workflows integration-test job timeout raised to 25 minutes (@grzegorz-roboflow, #2719).
• README contributing-guide link fixed (@bczifra, #2704).

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📦 Side note: dual GPU build for Cosmos 3

There is still no released transformers version that ships the NVIDIA Cosmos 3 model code, so this release again publishes two GPU server builds:

• roboflow/roboflow-inference-server-gpu:1.3.7 — the standard build, with the regular dependency stack (no Cosmos 3).

• roboflow/roboflow-inference-server-gpu:1.3.7-cosmos3 — identical server, but with the custom transformers dependency set required by NVIDIA Cosmos 3 Edge:

  docker pull roboflow/roboflow-inference-server-gpu:1.3.7-cosmos3

Use the -cosmos3 tag only if you need the Cosmos 3 Edge preview self-hosted; all other images are single-build.

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Full changelog: v1.3.6...v1.3.7