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Install Holoscan SDK via the NGC Docker container.
Passed 17 of 19 test cases with the skill, and 6 of 19 without it.
and 13 more in the full report
13 fixed7 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
When executing the video_replayer example inside the Holoscan container, the application fails to locate sample video files. Which environment variable must be set, and to what directory path?
| Checked | Without skill | With skill |
|---|---|---|
| Sets HOLOSCAN_INPUT_PATH to /opt/nvidia/holoscan/data | Missed | Passed |
The instructions your model reads when the skill loads.
Pull and verify the official Holoscan SDK container from NGC (nvcr.io/nvidia/clara-holoscan/holoscan), selecting the right CUDA/arch tag for the host GPU and validating with the bundled Python and C++ examples.
nvidia-smi).docker group (or sudo).docker run --gpus all works).nvcr.io and docs.nvidia.com.nvcr.io/nvidia/clara-holoscan/holoscan.Tag = <version>-<suffix>, e.g. v4.1.0-cuda13. Get the current SDK version from the doc page above; pick the suffix from nvidia-smi (the "CUDA Version" field, top-right of the table header):
nvidia-smi CUDA Version | Suffix |
|---|---|
| 13.x+ | cuda13 |
| 12.x, Ampere/Ada dGPU | cuda12-dgpu |
| 12.x, ARM64 iGPU (nvgpu) | cuda12-igpu |
The "CUDA Forward Compatibility mode ENABLED" banner is expected — not an error — when the container ships a newer CUDA minor version than the host driver supports. The forward-compat shim lets the container's CUDA runtime work against the older host driver within the same major version.
test cases passed, vs 6 / 19 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.