--- name: jetson-video-setup license: "Apache-2.0" description: >- Use when installing, repairing, reusing, inspecting, or verifying readiness of the native NVIDIA Video Codec SDK or PyNvVideoCodec on Jetson, including the one-frame encode/decode smoke test with official samples, and when interpreting what those readiness results, including CPU-buffer and device-memory sample modes, do and do not establish. metadata: author: "Vinit Bansal " tags: [jetson, video-codec-sdk, pynvvideocodec, setup, nvenc, nvdec] languages: [markdown] data-classification: public --- # Jetson Video Setup ## Purpose Inspect, install, and verify the native NVIDIA Video Codec SDK and PyNvVideoCodec on a live Jetson. Setup owns product installation and readiness. Use the sibling video skills for codec support, encoder configuration, performance measurement, and application pipelines. Use the standard package manager, Python environment tools, and installed NVIDIA samples directly, then return a concise readiness result. ## Read before acting - Read [setup-workflow.md](references/setup-workflow.md) for inspection and readiness checks. - Read [setup-install.md](references/setup-install.md) completely once before an APT, venv, or pip change. - Read [setup-output-contract.md](references/setup-output-contract.md) for the concise human-readable result to return. - Read [video-content.md](references/video-content.md) only when a request also supplies or asks to retrieve media. ## Select the product Resolve the requested product before touching the target: - “Video Codec SDK”, “VC SDK”, “native SDK”, or `nvidia-video-codec-sdk` selects the native product. - “PyNvVideoCodec”, “PyNv”, “PySDK”, “Python SDK”, or an explicitly Python interface selects PyNvVideoCodec. - Select both only when the user asks for both. A genuinely bare “video SDK” setup or readiness request is ambiguous. Ask whether the user wants native Video Codec SDK, PyNvVideoCodec, or both, then stop. The word “report” does not resolve that ambiguity. Keep native and Python work independent. A failure on one surface must not erase a successful result from the other. ## Workflow 1. Confirm commands would run on a Jetson. Inspect `/etc/nv_tegra_release`, `/etc/os-release`, and the requested GPU ordinal. On another host, provide guidance only and make no readiness claim. 2. Inspect only the selected product with the direct commands in [setup-workflow.md](references/setup-workflow.md). For native, identify the installed package, package-owned Samples tree, CUDA toolkit, and build tools. For Python, use the exact interpreter supplied by the user or created during this request and inspect its installed distribution and loaded module. Never scan the filesystem for virtual environments. 3. If inspection is all the user requested, report what is installed and stop. Package or import presence is `installed`, not `ready`. 4. If the selected product is missing and the user asked to install or repair it, follow that install reference. Show the exact package or pip commands before mutation. Install and verify system prerequisites before creating a final Python environment path. Change only the selected product and its missing prerequisites. 5. After installation, repeat the direct inspection. Then run the installed release's official one-frame encode followed by independent decode as described in [setup-workflow.md](references/setup-workflow.md). 6. Report each selected product separately. Use `ready` only after its official encode and independent decode pass all observable checks. Otherwise report `installed`, `blocked`, or `failed`, name the failing command or missing prerequisite, and give one concrete next action. When another video skill asks only for readiness, perform steps 1 and 2 and return the exact package/Samples root or Python interpreter/package path. Do not run the setup smoke test if that consumer will immediately run its own authenticated operation. ## PyNvVideoCodec environment selection Use this interpreter precedence: an explicit user path, an exact path already established in the current conversation, then the conventional profile path. The conventional smoke interpreter is `$HOME/.venvs/nvcodec/bin/python`; the `full-samples` interpreter is `$HOME/.venvs/nvcodec-full/bin/python`. Checking one of these exact paths is not a filesystem scan. Never select a venv by directory order or fall back to system Python. For a new environment, use the applicable conventional path when it is absent, or an explicit new absolute path in a durable user-owned location. If the conventional path exists, inspect it first. Reuse it when valid; otherwise report its exact defect, leave it untouched, and ask for a different new path. Return the selected interpreter path so downstream skills can use it directly. Use `pynvc-smoke` for the setup smoke proof, decode-performance work, and a conventional interface-availability check. That availability check inspects only the exact smoke path and reports `not_ready` when it is absent or invalid. Any consumer encode operation, including a capability availability proof, and work using advanced raw decode, segmentation, or encode-performance samples selects the separate `full-samples` environment and dependencies described in the install guide. A media-free capability inventory may use either conventional profile: inspect smoke first, then `full-samples` when smoke is absent, and report the exact interpreter that answered. Apply that either-profile allowance only when the entire request is a media-free inventory. When a request also seeks an operation or a capability availability proof, use the profile that request binds; if that profile is not ready, report `not_ready` and do not query the other conventional profile. The interface-availability check above remains bound to the smoke path. ## Readiness proof Both products use one generated 640×360 8-bit NV12 frame (345,600 bytes), H.264 encode, and an independent decode of the fresh bitstream. | Product | Required evidence | |---|---| | Native | Package-owned `AppEncCuda` reports one encoded frame; package-owned `AppDec` consumes that exact nonempty bitstream, reports one decoded frame, and writes a 345,600-byte NV12 output. | | PyNv `full-samples` | Wheel-owned `basic/encode.py` reports one encoded CPU-buffer frame; wheel-owned `advanced/decode.py` consumes the exact bitstream, reports one frame, and writes a 345,600-byte output. | | PyNv `pynvc-smoke` | The same encoder proof; wheel-owned `advanced/decode_perf.py` reports one requested decoded frame and a total of one, with no worker error, warning, or traceback. It does not claim a raw decoded file. | Exit zero or file creation alone is insufficient. Require the expected marker and count, a newly created nonempty bitstream, and the independent consumer. Do not require decoded bytes to equal the input because H.264 is lossy. CPU-buffer and device-memory sample modes establish only the exact readiness operation. They do not establish external buffer sharing, zero copy, or a synchronization primitive for another process or pipeline stage. For a request to confirm an in-process or cross-stage buffer contract, invoke `jetson-video-pipeline`; require an authenticated operation of the actual downstream stage that proves the sharing handle, format and layout, ownership and lifetime, signal and wait behavior, and safe buffer reuse. Do not answer that request from setup evidence alone. ## Compose requested sibling work For a request that also asks about support, encoder configuration, throughput, or an application workflow, invoke only the matching public skill: - `jetson-video-capability` - `jetson-video-recipe` - `jetson-video-benchmark` - `jetson-video-pipeline` Pass the selected product and exact local paths as data. Do not read or import a sibling skill's private files. If a required sibling is unavailable, preserve completed setup results and name the missing skill. ## Safety - For a report-only request, perform direct read-only inspection only; never build samples, create a workspace or venv, launch a codec operation, or mutate packages or an existing Python environment. - Use only already configured, signature-authenticated package repositories. NVIDIA SDK and CUDA packages must come from the public Jetson repository for the installed release. Never add or edit a source, key, or trust bypass. - Before APT installation, inspect the candidate and origin, run the exact `apt-get -s` simulation, and reject removals, downgrades, or unexpected packages. Apply the reviewed command with noninteractive `sudo -n`; if that authorization is unavailable, stop rather than using a password prompt, `su`, or another escalation path. - Run `dpkg --audit` after an APT mutation and stop if it is nonempty or fails. - Keep native and Python acquisition separate. A Python-only request must not install `nvidia-video-codec-sdk`; a native-only request must not create a venv. - Keep credentials out of commands, logs, and reports. Reject symlinked or unexpected install targets and use fresh build/output paths. - Local installation and smoke results do not establish product support or that the release is the newest compatible release. Use current official NVIDIA documentation for those claims. For PSNR, SSIM, DRM playback, capture, inference, or display work, state that setup does not own that workflow and route only an explicitly requested video codec portion.