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This is step 2 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-sql -> udf-benchmark). Use this skill when you have a CPU UDF with a unit test and need to convert it to SQL for GPU acceleration.\nlicense: CC-BY-4.0 AND Apache-2.0\nmetadata:\n  spdx-file-copyright-text: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.\nmodel: inherit\n---\n\n# Convert UDF to Spark SQL\n\n## Workflow\n\n- [ ] Step 1: Implement the SQL expression\n- [ ] Step 2: Fill in the comparison test and iterate\n- [ ] Step 3: Run judge subagent if requested\n- [ ] Step 4: Review conversion\n\n**Before making any edits, create a visible TODO checklist for every workflow step in this skill and keep it updated.** Do not produce a final answer until every required checklist item is marked complete.\n\n## Prerequisites\n\n- Project directory from Step 1 (udf-gen-test) with passing unit test\n\nDerive `\u003CCamelName>` and `\u003Csnake_name>` from the UDF class name.\n\n> **Note:** Commands require access to `\u002Ftmp` (Spark temp storage) and `\u002Fdev` (GPU device). If commands fail due to sandbox restrictions, re-run them unsandboxed.\n\n## Step 1: Implement the SQL Expression\n\nImplement the SQL expression in a file at `src\u002Fmain\u002Fresources\u002F\u003Csnake_name>.sql`.\n\n**Read `examples\u002F` for example UDF-to-SQL conversions for the target language.**\n\n### Guidelines\n\n- Focus on correctness FIRST, then GPU compatibility — the test will report which operators are not GPU-compatible\n- Avoid expensive joins; prefer window functions, CTEs, and built-in array\u002Fmap functions over explode-and-aggregate patterns\n\n**Do NOT hardcode test sample values or outputs.** The SQL expression must work correctly for ANY potential input.\n\n## Step 2: Fill in test and iterate\n\nUpdate `src\u002Ftest\u002Fscala\u002Fcom\u002Fudf\u002FSqlComparisonTest.scala`:\n- Update the SQL file path to point to your `src\u002Fmain\u002Fresources\u002F\u003Csnake_name>.sql` file\n- Replace placeholders with the actual camel\u002Fsnake UDF name\n\nThen run the test:\n```bash\nmvn test -Dsuites=com.udf.SqlComparisonTest\n```\n\nIf the test fails, analyze the error and iterate on the SQL expression.\n\n### Difficult Test Failures\n\nTreat the unit test as the CPU behavior specification. Do not weaken or remove test cases silently.\n\n- Tests that check for CPU errors may not be directly applicable to SQL operators: Spark RAPIDS typically evaluates a whole column\u002Fbatch and may produce nulls for invalid rows instead of throwing one row-level exception. Make an explicit judgment call about the UDF contract. Add a clear comment in the test and a `TODO\u002FNOTE` in the SQL statement explaining the mismatch.\n- In rare cases, the Spark RAPIDS Plugin has known discrepancies in certain SQL operators. If a test case does not pass because of these discrepancies, notify the user and comment out the conflicting assertion\u002Ftest only after documenting how you tried to make the behavior match and why those attempts failed.\n- If the behavior is important, common, or part of the documented input domain, **always prefer fixing the SQL expression** over commenting out the test case. The exception is a performance-vs-correctness tradeoff that the user explicitly approves.\n\n## Step 3: Run Judge Subagent If Requested\n\nIf the user explicitly asked for the judge, a judge subagent, or a review agent, treat that as an explicit request for delegation: you **MUST** launch a separate subagent with `model: inherit` and instruct it to use the **udf-judge-conversion** skill. Ask it to review the `UnitTest`, `SqlComparisonTest`, and SQL expression.\n\nIf the user did not request a judge\u002Freview agent, mark this step as skipped and continue to Step 4. If a required judge subagent is blocked by tool policy, stop and tell the user that explicit permission\u002Finstruction is needed.\n\nIf you run the judge, wait for it to complete and review its report. If the judge finds any issues, 1) fix the issues, 2) re-run the tests, and 3) re-run the judge subagent.\n\n## Step 4: Review Conversion\n\nReview your own work to ensure:\n- The test runs on the GPU and directly compares CPU-SQL outputs\n- The implementation does not overfit to test cases\n\n## Output\n\nUpon successful completion:\n- SQL file at `src\u002Fmain\u002Fresources\u002F\u003Csnake_name>.sql`\n- Comparison test passes\n\nThese outputs are required for **Step 3: Benchmark**.\n",{"data":38,"body":42},{"name":4,"description":6,"license":26,"metadata":39,"model":41},{"spdx-file-copyright-text":40},"Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. 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Use this skill when you have a CPU UDF and need to create a unit test for the UDF before converting it into a GPU-compatible implementation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[561,562,563,564],{"name":532,"slug":533,"type":13},{"name":9,"slug":8,"type":13},{"name":18,"slug":19,"type":13},{"name":565,"slug":566,"type":13},"Testing","testing","2026-07-14T05:30:55.30163",{"slug":408,"name":408,"fn":569,"description":570,"org":571,"tags":572,"stars":23,"repoUrl":24,"updatedAt":581},"review UDF tests and GPU implementations","Reviews generated UDF tests and GPU\u002FSQL implementations for robustness, anti-cheating, and GPU execution integrity. Use when the user requests a judge\u002Freview-agent pass, or when manually reviewing a completed conversion.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[573,576,577,578],{"name":574,"slug":575,"type":13},"Code Review","code-review",{"name":15,"slug":16,"type":13},{"name":9,"slug":8,"type":13},{"name":579,"slug":580,"type":13},"QA","qa","2026-07-14T05:30:59.022808",{"slug":583,"name":583,"fn":584,"description":585,"org":586,"tags":587,"stars":23,"repoUrl":24,"updatedAt":592},"udf-optimize-cudf","optimize cuDF RapidsUDF performance","Iteratively optimizes a cuDF RapidsUDF implementation for GPU performance. Use after testing and benchmarking with udf-benchmark. Runs a loop of profiling, optimizing, testing, and benchmarking until performance converges or the iteration budget is exhausted.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[588,589,590,591],{"name":516,"slug":517,"type":13},{"name":15,"slug":16,"type":13},{"name":9,"slug":8,"type":13},{"name":520,"slug":521,"type":13},"2026-07-14T05:30:52.785634",7,{"items":595,"total":748},[596,614,632,643,655,669,682,696,705,716,728,737],{"slug":597,"name":597,"fn":598,"description":599,"org":600,"tags":601,"stars":611,"repoUrl":612,"updatedAt":613},"nemoclaw-user-guide","retrieve NemoClaw documentation and configuration","Guides human users' AI agents to the NemoClaw docs MCP server and canonical Fern documentation in Markdown form. Use when users ask how to install, configure, operate, troubleshoot, secure, or learn NemoClaw with an AI coding assistant. Trigger keywords - nemoclaw docs, use nemoclaw with ai agent, nemoclaw mcp docs, nemoclaw install help, nemoclaw quickstart, nemoclaw markdown docs, llms.txt, agent skills.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[602,605,608],{"name":603,"slug":604,"type":13},"Documentation","documentation",{"name":606,"slug":607,"type":13},"MCP","mcp",{"name":609,"slug":610,"type":13},"Search","search",21777,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FNemoClaw","2026-07-20T06:00:01.461044",{"slug":615,"name":615,"fn":616,"description":617,"org":618,"tags":619,"stars":629,"repoUrl":630,"updatedAt":631},"mcore-build-and-dependency","manage Megatron-LM development environments","Container-based dev environment setup and dependency management for Megatron-LM. Covers acquiring and launching the CI container, uv package management, and updating uv.lock.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[620,623,626],{"name":621,"slug":622,"type":13},"Containers","containers",{"name":624,"slug":625,"type":13},"Deployment","deployment",{"name":627,"slug":628,"type":13},"Python","python",17049,"https:\u002F\u002Fgithub.com\u002FNVIDIA\u002FMegatron-LM","2026-07-27T06:06:11.249662",{"slug":633,"name":633,"fn":634,"description":635,"org":636,"tags":637,"stars":629,"repoUrl":630,"updatedAt":642},"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},[638,641],{"name":639,"slug":640,"type":13},"CI\u002FCD","ci-cd",{"name":624,"slug":625,"type":13},"2026-07-14T05:25:59.97109",{"slug":644,"name":644,"fn":645,"description":646,"org":647,"tags":648,"stars":629,"repoUrl":630,"updatedAt":654},"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},[649,650,651],{"name":639,"slug":640,"type":13},{"name":624,"slug":625,"type":13},{"name":652,"slug":653,"type":13},"GitHub","github","2026-07-27T06:06:12.278222",{"slug":656,"name":656,"fn":657,"description":658,"org":659,"tags":660,"stars":629,"repoUrl":630,"updatedAt":668},"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},[661,664,665],{"name":662,"slug":663,"type":13},"Debugging","debugging",{"name":652,"slug":653,"type":13},{"name":666,"slug":667,"type":13},"Triage","triage","2026-07-14T05:25:57.442089",{"slug":670,"name":670,"fn":671,"description":672,"org":673,"tags":674,"stars":629,"repoUrl":630,"updatedAt":681},"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},[675,678],{"name":676,"slug":677,"type":13},"Best Practices","best-practices",{"name":679,"slug":680,"type":13},"Code Analysis","code-analysis","2026-07-14T05:25:56.18433",{"slug":683,"name":683,"fn":684,"description":685,"org":686,"tags":687,"stars":629,"repoUrl":630,"updatedAt":695},"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},[688,691,694],{"name":689,"slug":690,"type":13},"Machine Learning","machine-learning",{"name":692,"slug":693,"type":13},"Migration","migration",{"name":9,"slug":8,"type":13},"2026-07-17T06:07:11.777011",{"slug":697,"name":697,"fn":698,"description":699,"org":700,"tags":701,"stars":629,"repoUrl":630,"updatedAt":704},"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},[702,703],{"name":579,"slug":580,"type":13},{"name":565,"slug":566,"type":13},"2026-07-14T05:25:53.673039",{"slug":706,"name":706,"fn":707,"description":708,"org":709,"tags":710,"stars":629,"repoUrl":630,"updatedAt":715},"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},[711,712],{"name":624,"slug":625,"type":13},{"name":713,"slug":714,"type":13},"Infrastructure","infrastructure","2026-07-14T05:25:49.362534",{"slug":717,"name":717,"fn":718,"description":719,"org":720,"tags":721,"stars":629,"repoUrl":630,"updatedAt":727},"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},[722,723,724],{"name":574,"slug":575,"type":13},{"name":652,"slug":653,"type":13},{"name":725,"slug":726,"type":13},"Pull Requests","pull-requests","2026-07-14T05:26:01.226578",{"slug":729,"name":729,"fn":730,"description":731,"org":732,"tags":733,"stars":629,"repoUrl":630,"updatedAt":736},"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},[734,735],{"name":579,"slug":580,"type":13},{"name":565,"slug":566,"type":13},"2026-07-14T05:25:54.928983",{"slug":738,"name":738,"fn":739,"description":740,"org":741,"tags":742,"stars":629,"repoUrl":630,"updatedAt":747},"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},[743,746],{"name":744,"slug":745,"type":13},"Automation","automation",{"name":639,"slug":640,"type":13},"2026-07-30T05:29:03.275638",496]