[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-nvidia-jetson-print-device-info":3,"mdc--3pbcdh-key":31,"related-repo-nvidia-jetson-print-device-info":1080,"related-org-nvidia-jetson-print-device-info":1186},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":20,"repoUrl":21,"updatedAt":22,"license":23,"forks":24,"topics":25,"repo":26,"sourceUrl":29,"mdContent":30},"jetson-print-device-info","print Jetson device information","Use when you need to print Jetson device info (module model, L4T version, kernel, OS version, current power mode) from a running Jetson target. 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This is an example skill.\nversion: 0.0.1\nlicense: \"Apache-2.0\"\nmetadata:\n  author: \"Jetson Team\"\n  tags: [jetson, device, info]\n  languages: [bash]\n  data-classification: public\n---\n\n# jetson-print-device-info\n\nPrints a concise summary of the Jetson device this skill runs on.\n\nThis skill is intended as a reference example for the `jetson-device-skills` repo and the NVIDIA-wide skills CI. It runs on the Jetson target (not the host PC) and performs read-only inspection — useful as a baseline capture before running performance tests.\n\n## Purpose\n\nCapture a baseline snapshot of a Jetson target's software stack and power mode before running performance, regression, or compatibility tests.\n\n## When to use\n\n- Starting a performance run and you want a captured baseline of the device's software stack and power mode.\n- Verifying that a freshly flashed Jetson matches an expected L4T \u002F JetPack version.\n\n## Prerequisites\n\n- Running on a Jetson target (not the host PC).\n- Standard CLIs available: `tr`, `cat`, `uname`, `uptime`, `lsb_release` (or `\u002Fetc\u002Fos-release`).\n- `nvpmodel` available for power mode (optional — skill falls back gracefully).\n\n## Inputs\n\nNone. The skill reads only from the local Jetson filesystem and standard CLIs.\n\n## Instructions\n\nRun each step in order and print the captured values into the report shown under [Output format](#output-format).\n\n1. **Capture** the module model and **validate** it is a Jetson target — exit early otherwise:\n   ```bash\n   # Device-tree strings are null-terminated, so strip NULs before printing.\n   model=$(tr -d '\\0' \u003C \u002Fproc\u002Fdevice-tree\u002Fmodel 2>\u002Fdev\u002Fnull)\n   case \"$model\" in\n     *Jetson*) ;;\n     *) echo \"Not running on a Jetson target (model: '${model:-unknown}')\"; exit 1 ;;\n   esac\n   echo \"$model\"\n   ```\n2. **Extract** the L4T release header line — skip the rest of the file (long list of library SHAs):\n   ```bash\n   head -1 \u002Fetc\u002Fnv_tegra_release 2>\u002Fdev\u002Fnull || echo \"L4T release info not found\"\n   # Equivalent: grep -m1 '^# R' \u002Fetc\u002Fnv_tegra_release\n   ```\n3. **Run** `nvpmodel -q` and **join** its two output lines (`NV Power Mode: \u003Cname>` and the mode number) onto one line. `paste -sd` only uses the first char of its delimiter, so use `awk` to insert the literal ` \u002F ` separator:\n   ```bash\n   nvpmodel -q 2>\u002Fdev\u002Fnull | awk 'NR==1{a=$0; next} {print a\" \u002F \"$0}' \\\n     || echo \"nvpmodel not available\"\n   ```\n4. **Print** the kernel version and uptime:\n   ```bash\n   uname -r\n   uptime -p\n   ```\n5. **Print** the OS version (prefer `lsb_release`, fall back to `\u002Fetc\u002Fos-release`):\n   ```bash\n   lsb_release -ds 2>\u002Fdev\u002Fnull || (. \u002Fetc\u002Fos-release && echo \"$PRETTY_NAME\") || echo \"OS version not found\"\n   ```\n\n## Output format\n\nPrint a short report with these sections, one line each where possible:\n\n```text\nModel:           \u003Cdevice-tree model string>\nL4T release:     \u003Crelease header line>\nPower mode:      \u003Cnvpmodel name> \u002F \u003Cmode number>\nKernel:          \u003Cuname -r>\nUptime:          \u003Cuptime -p>\nOS version:      \u003Clsb_release \u002F \u002Fetc\u002Fos-release output>\n```\n\n## Examples\n\nExample output on an Orin AGX dev kit (L4T R36 \u002F Ubuntu 22.04):\n\n```text\nModel:           NVIDIA Jetson AGX Orin Developer Kit\nL4T release:     # R36 (release), REVISION: 3.0\nPower mode:      NV Power Mode: MAXN \u002F 0\nKernel:          5.15.136-tegra\nUptime:          up 2 hours, 14 minutes\nOS version:      Ubuntu 22.04.4 LTS\n```\n\nExample output on an AGX Thor (L4T R39 \u002F Ubuntu 24.04):\n\n```text\nModel:           NVIDIA Jetson AGX Thor Developer Kit\nL4T release:     # R39 (release), REVISION: 0.0\nPower mode:      NV Power Mode: 120W \u002F 1\nKernel:          6.8.0-tegra\nUptime:          up 12 minutes\nOS version:      Ubuntu 24.04 LTS\n```\n\n## Error handling\n\nEach command falls back to a clearly labeled `\"... not found\"` \u002F `\"... not available\"` string if the underlying file or binary is missing — the skill never errors out mid-report. If `\u002Fproc\u002Fdevice-tree\u002Fmodel` is missing or does not contain a Jetson string, exit early with a clear \"not running on a Jetson target\" message.\n\n## Limitations\n\n- Read-only inspection only — does not detect GPU\u002FCPU clocks, thermal state, or per-rail power.\n- `nvpmodel` output format varies between L4T versions; the skill prints it verbatim rather than parsing.\n\n## Troubleshooting\n\n- **Error:** `\u002Fproc\u002Fdevice-tree\u002Fmodel: No such file or directory`\n  **Cause:** Running on a host PC, not a Jetson target.\n  **Solution:** Run the skill on the Jetson device directly (e.g. via SSH).\n\n- **Error:** `nvpmodel: command not found`\n  **Cause:** L4T BSP not installed, or running in a minimal container without `nvpmodel`.\n  **Solution:** Expected on non-Jetson or stripped environments — the skill prints `\"nvpmodel not available\"` and continues.\n\n## Notes\n\n- Read-only. 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Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1212,1215,1216],{"name":1213,"slug":1214,"type":15},"Containers","containers",{"name":1106,"slug":1107,"type":15},{"name":1217,"slug":1218,"type":15},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":1223,"name":1223,"fn":1224,"description":1225,"org":1226,"tags":1227,"stars":1219,"repoUrl":1220,"updatedAt":1232},"mcore-bump-base-image","update NVIDIA PyTorch base images","Bump the NVIDIA PyTorch base image (`nvcr.io\u002Fnvidia\u002Fpytorch:YY.MM-py3`) used by Megatron-LM CI. Covers the two pin sites (GitHub CI in `docker\u002F.ngc_version.dev` and GitLab CI in `.gitlab\u002Fstages\u002F01.build.yml`), the post-bump CI loop (re-run functional tests, refresh golden values, mark broken tests), and the gotchas that bit PRs",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1228,1231],{"name":1229,"slug":1230,"type":15},"CI\u002FCD","ci-cd",{"name":1106,"slug":1107,"type":15},"2026-07-14T05:25:59.97109",{"slug":1234,"name":1234,"fn":1235,"description":1236,"org":1237,"tags":1238,"stars":1219,"repoUrl":1220,"updatedAt":1244},"mcore-cicd","manage CI\u002FCD pipelines for Megatron-LM","CI\u002FCD reference for Megatron-LM. Covers CI pipeline structure, PR scope labels, triggering internal GitLab CI (which force-pushes the current branch to a pull-request\u002FBRANCH ref — always dry-run and verify the destination first; never run against shared or protected branches), and CI failure investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1239,1240,1241],{"name":1229,"slug":1230,"type":15},{"name":1106,"slug":1107,"type":15},{"name":1242,"slug":1243,"type":15},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":1246,"name":1246,"fn":1247,"description":1248,"org":1249,"tags":1250,"stars":1219,"repoUrl":1220,"updatedAt":1258},"mcore-create-issue","investigate CI failures and create issues","Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1251,1254,1255],{"name":1252,"slug":1253,"type":15},"Debugging","debugging",{"name":1242,"slug":1243,"type":15},{"name":1256,"slug":1257,"type":15},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":1260,"name":1260,"fn":1261,"description":1262,"org":1263,"tags":1264,"stars":1219,"repoUrl":1220,"updatedAt":1271},"mcore-linting-and-formatting","lint and format Megatron-LM code","Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1265,1268],{"name":1266,"slug":1267,"type":15},"Best Practices","best-practices",{"name":1269,"slug":1270,"type":15},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":1273,"name":1273,"fn":1274,"description":1275,"org":1276,"tags":1277,"stars":1219,"repoUrl":1220,"updatedAt":1285},"mcore-migrate-gpt-to-hybrid","migrate Megatron-LM models to HybridModel","Migration guide for moving Megatron Core GPTModel checkpoints, model providers, training commands, and layer mappings to HybridModel.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1278,1281,1284],{"name":1279,"slug":1280,"type":15},"Machine Learning","machine-learning",{"name":1282,"slug":1283,"type":15},"Migration","migration",{"name":9,"slug":8,"type":15},"2026-07-17T06:07:11.777011",{"slug":1287,"name":1287,"fn":1288,"description":1289,"org":1290,"tags":1291,"stars":1219,"repoUrl":1220,"updatedAt":1296},"mcore-onboard-gb200-1node-tests","onboard functional tests for GB200","Onboard 1-node GitHub MR functional tests for GB200 from existing mr-scoped 2-node tests.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1292,1295],{"name":1293,"slug":1294,"type":15},"QA","qa",{"name":1136,"slug":1137,"type":15},"2026-07-14T05:25:53.673039",{"slug":1298,"name":1298,"fn":1299,"description":1300,"org":1301,"tags":1302,"stars":1219,"repoUrl":1220,"updatedAt":1305},"mcore-run-on-slurm","launch distributed training jobs on SLURM","How to launch distributed Megatron-LM training jobs on a SLURM cluster. Covers a minimal sbatch skeleton, environment-variable setup for torch.distributed.run, CUDA_DEVICE_MAX_CONNECTIONS rules across hardware and parallelism modes, container conventions, monitoring, and per-rank failure diagnosis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1303,1304],{"name":1106,"slug":1107,"type":15},{"name":1109,"slug":1110,"type":15},"2026-07-14T05:25:49.362534",{"slug":1307,"name":1307,"fn":1308,"description":1309,"org":1310,"tags":1311,"stars":1219,"repoUrl":1220,"updatedAt":1319},"mcore-split-pr","split pull requests to reduce review load","Split a PR into multiple PRs to reduce the number of required CODEOWNERS reviewer groups.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1312,1315,1316],{"name":1313,"slug":1314,"type":15},"Code Review","code-review",{"name":1242,"slug":1243,"type":15},{"name":1317,"slug":1318,"type":15},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":1321,"name":1321,"fn":1322,"description":1323,"org":1324,"tags":1325,"stars":1219,"repoUrl":1220,"updatedAt":1328},"mcore-testing","run and manage Megatron-LM tests","Test system for Megatron-LM. Covers test layout, recipe YAML structure, adding and running unit and functional tests, golden values, marker filters, and CI parity.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1326,1327],{"name":1293,"slug":1294,"type":15},{"name":1136,"slug":1137,"type":15},"2026-07-14T05:25:54.928983",{"slug":1330,"name":1330,"fn":1331,"description":1332,"org":1333,"tags":1334,"stars":1219,"repoUrl":1220,"updatedAt":1337},"nightly-sync","manage nightly main-to-dev sync workflows","Domain knowledge for the nightly main-to-dev sync workflow. Covers merge strategy, CI architecture, failure investigation, and known issues.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1335,1336],{"name":1146,"slug":1147,"type":15},{"name":1229,"slug":1230,"type":15},"2026-07-30T05:29:03.275638",496]