Running on Windows (and cross-platform)

This skill runs on Windows with Docker Desktop with no host packages: every compute step runs inside the DeepStream Linux container via docker run. The host only needs docker + the NVIDIA driver. The single exception is the host-side install (copying the skill into <project>\.claude\skills\), which cannot run in a container — so the skill ships exactly one PowerShell script, **install.ps1**, the twin of install.sh. There are no .ps1 duplicates of the compute scripts.

Why this works

The ONNX export, TensorRT engine build, custom nvinfer parser compile, DeepStream run, and PDF report all execute inside nvcr.io/nvidia/deepstream:9.1-triton-multiarch (the venv build/.venv_optimum + wkhtmltopdf are installed into that container by setup.sh). The container is the portability layer. The only per-shell difference is the docker run bind-mount token.

Prerequisites (Windows)

Docker Desktop with the WSL2 backend enabled (Settings → General → Use the WSL 2 based engine) — required for GPU (--gpus all works only through the WSL2 backend).
A recent NVIDIA driver with WSL/CUDA support. No CUDA toolkit / TensorRT / DeepStream needed on the host — the container ships them.
Docker Desktop → Settings → Resources → File Sharing: share the drive holding your working dir.
docker pull nvcr.io/nvidia/deepstream:9.1-triton-multiarch.

The one thing that differs per shell: the mount token

Claude Code fills this in based on the host OS:

Shell	working-dir mount
PowerShell	`-v "${PWD}:/work"`
cmd	`-v "%cd%:/work"`
WSL2 / Linux bash	`-v "$PWD":/work`

Bootstrap + preflight (PowerShell example)

# one-time bootstrap: venv + torch/onnx/onnxruntime + wkhtmltopdf, all in-container
docker run --rm -it --gpus all --shm-size=16g -v "${PWD}:/work" -w /work `
  --entrypoint bash nvcr.io/nvidia/deepstream:9.1-triton-multiarch `
  .claude/skills/deepstream-import-vision-model/setup.sh

# preflight — GPU + venv + trtexec (container-mode auto-detects /.dockerenv)
docker run --rm --gpus all -v "${PWD}:/work" -w /work `
  --entrypoint bash nvcr.io/nvidia/deepstream:9.1-triton-multiarch `
  .claude/skills/deepstream-import-vision-model/scripts/preflight.sh

Every subsequent phase runs the same way — via docker run … -lc '<commands>' or the .claude/skills/deepstream-import-vision-model/scripts/dsrun.sh wrapper.

Notes

The skill ships a .gitattributes forcing LF on all scripts, so a Windows checkout won't CRLF-corrupt them (CRLF breaks bash-in-container).
--shm-size=16g works on the WSL2 backend.
Install: on native Windows run the bundled install.ps1 — the twin of install.sh, same sequence and flags (-Target=--target, -NoCursor=--no-cursor, -DryRun=--dry-run): .\install.ps1 -Target C:\path\to\project (copies the skill into <project>\.claude\skills\). On Linux/WSL2/Git Bash use bash install.sh --target <project>.
Prefer a WSL2 Ubuntu terminal for the exact Linux experience — inside WSL2 everything runs unchanged.

Third-party checks

No alerts27d3 checks · Risk SAFE

Gen Agent Trust Hub27d
This skill provides an automated pipeline for importing HuggingFace or NVIDIA NGC vision models into NVIDIA DeepStream. It follows industry best practices for model acquisition, TensorRT engine building, and benchmarking. All external downloads target trusted sources (HuggingFace and NVIDIA NGC) or well-known package registries, and all code traces back to the vendor (NVIDIA). No malicious patterns or data exfiltration risks were detected.
Socket27d
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Snyk27d
Risk: LOW · No issues

Capability

Other metadata

metadata: { "author": "Tushar Khinvasara <tkhinvasara@nvidia.com>", "owner": "Tushar Khinvasara <tkhinvasara@nvidia.com>", "service": "deepstream", "version": "1.5.2", "reviewed": "2026-08-04", "team": "deepstream-sdk", "tags": [ "deepstream", "tensorrt", "object-detection", "import-vision-model" ], "languages": [ "bash", "python", "cpp" ], "domain": "computer-vision" }

/deepstream-import-vision-model

referenceswindows.md