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--- name: alpamayo2-super description: Package, preflight, run, validate, or troubleshoot NVIDIA Alpamayo 2 Super 34B VLA inference in NPA, including its OpenMDW model terms, separately gated PhysicalAI-AV dataset, public runtime-fetch image, B200/RTX PRO 6000 routing, workflow artifacts, and real-GPU evidence. --- # Alpamayo 2 Super Run NVIDIA's real VLM plus 2.3B diffusion expert. Never replace inference with an import, fabricated trajectory, or manifest-only smoke. ## Legal gates Keep the boundaries separate: - Source: `NVlabs/alpamayo2@beb2977d9a7e9d66837d4a3ad5144ff59de37519`, Apache-2.0, baked with its license and the marked dataset-revision patch. - Model: `nvidia/Alpamayo2-Super@00554695e729a6ff0b6281fd2c81b18d06e33dbe`, OpenMDW-1.1. Acceptance is by exercising granted rights. Fetch at runtime. - Dataset: `nvidia/PhysicalAI-Autonomous-Vehicles@b719eea7f0a63619ef51ec7f54178af0937ef050`, gated by NVIDIA's AV Dataset License. It is non-transferable; accept it interactively on Hugging Face and fetch it only into the operator cache. - Outputs: OpenMDW-1.1 imposes no output restriction, but Alpamayo is not an automotive-grade driving stack. Preserve model-card safety provenance. Before provisioning, run: ```bash npa workbench health preflight npa workbench health access --capability alpamayo2-super ``` A missing dataset entitlement is terminal. Open the exact URL printed by the access command; Hugging Face acceptance cannot be automated by NPA. ## Build and scan Build only from the checked-in Dockerfile. Never pass `HF_TOKEN` as a build arg or populate `/workspace/.cache/huggingface` during a build. ```bash bash npa/docker/workbench/alpamayo2-super/build.sh npa/.venv/bin/python npa/scripts/scan_image_alpamayo2_payload.py \ <exact-local-image> ``` Require a complete clean scan over every layer for checkpoints, PhysicalAI-AV payload, caches, and credentials. Inspect SBOM/provenance separately; the byte scanner is not a license review. ## Run the workflow Use B200 first. The workload is headless and does not need RT cores; its measured H100 peak is 72,115 MiB, so one B200 has ample memory. Validate RTX PRO 6000 separately because B200 `sm_100` does not prove RTX `sm_120`. ```bash npa workbench workflow validate-spec \ npa/workflows/workbench/npa-workflows/alpamayo2-super-inference.yaml npa workbench workflow submit \ npa/workflows/workbench/npa-workflows/alpamayo2-super-inference.yaml \ --infra <configured-infra-target> --var bucket=<operator-bucket> \ --secret-env HF_TOKEN --secret-env AWS_ACCESS_KEY_ID \ --secret-env AWS_SECRET_ACCESS_KEY ``` The run must publish non-empty `trajectory.json`, `trajectory.png`, and `result.json`. Verify exact model/dataset revisions, the runtime image digest, `weights_baked=false`, `dataset_baked=false`, and the node-local ephemeral cache tier. Treat success without all three artifacts as failure. ## Diagnose - 401/403 before GPU allocation: accept the dataset agreement with the same HF account or replace the rejected token; do not add an NPA bypass boolean. - CUDA OOM: confirm one trajectory sample, ten diffusion steps, and no competing process; do not silently reduce cameras or frames. - `no kernel image`: inspect Torch arch flags and flash-attn build targets. B200 requires `sm_100`; RTX PRO 6000 requires `sm_120`. - Missing camera projection: retain fail-closed `--require-camera-projection`. - Pending job: inspect scheduling and image-pull evidence. Change GPU products only for scheduler/capacity evidence and record a separate architecture result. Cancel the exact run before teardown. Never delete shared operator resources merely because a validation job finished. ## Accepted release baseline `0.1.0-cu128` is the accepted runtime-fetch baseline. Its OCI index digest is `sha256:2164450f8baf57d8798f64063ea27bf11611f5b695c467de0c2e319e3134ebd5`. On 2026-08-18 the exact digest completed the real upstream workflow on B200 (`sm_100`) and, independently, RTX PRO 6000 (`sm_120`). The 26-layer payload scan was clean. RTX peak allocation was 71,447 MiB; the B200 run completed but was not sampled for peak memory, so retain NVIDIA's 72,115 MiB H100 measurement as the conservative documented reference rather than inventing a B200 number. For RTX qualification, preserve the committed B200 default and override the submitted accelerator with `NPA_WORKFLOW_GPU_ACCELERATOR=RTXPRO6000:1`. Require the same three artifacts and provenance checks; do not infer RTX support from a B200 result. ## Verify changes ```bash npa/.venv/bin/python ~/.codex/skills/.system/skill-creator/scripts/quick_validate.py \ skills/tools/alpamayo2-super npa/.venv/bin/python -m pytest \ npa/tests/workbench/test_alpamayo2_super.py \ npa/tests/guardrails/test_skills_index.py \ npa/tests/orchestration/npa_workflow/test_catalog_doc_sync.py -q ```