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magpie-kernel-evaluator
Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map
개요
Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.
전체 설명 읽기
소스 문서이며 이 웹사이트의 실행 지침이 아닙니다. 명령 실행 전에 권한을 확인하세요.
Magpie
Use Magpie for three connected jobs:
- Benchmark an inference workload and collect throughput, latency, and traces.
- Analyze or compare GPU kernels for correctness and performance.
- Drive an optimization loop from a benchmark bottleneck to source, candidate kernels, and end-to-end validation.
Describe only capabilities supported by the checked-out Magpie version. Do not infer support for an unverified ROCm, GPU, framework, or experimental integration.
Choose the workflow
| User goal | Workflow |
|---|---|
| Evaluate one implementation | analyze |
| Rank two or more implementations | compare |
| Measure model-serving performance | benchmark |
| Find expensive kernels in existing traces | standalone gap analysis |
| Explain a profiled inference workload | benchmark → TraceLens post-processing → stage/roofline review |
| Optimize an end-to-end workload | benchmark → TraceLens/gap analysis → source mapping → analyze/compare → re-benchmark |
Use a YAML config for reproducible or multi-step work. Use inline CLI arguments for small exploratory runs.
Preflight
-
Locate the Magpie repository or installed package.
-
Check the local interface before constructing commands:
magpie --help magpie analyze --help magpie compare --help magpie benchmark --help magpie --gpu-info -
Check required tools, model access, GPU visibility, writable output space, and container or Ray access as applicable.
-
Read the repository compatibility matrix before making version claims. Treat ROCm or hardware not listed there as unverified until tested.
-
Record the exact config, model revision, image, environment variables, GPU allocation, and Magpie commit for benchmark comparisons.
Run from the Magpie repository root, install with pip install -e ., or use python -m Magpie when the magpie entry point is unavailable.
Analyze a kernel
Prefer a config when correctness or profiler settings matter:
magpie analyze --kernel-config path/to/kernel.yaml
For a quick single-kernel run:
magpie analyze path/to/kernel.hip --type hip --testcase "./run_test.sh"
Supported public kernel types are hip, cuda, pytorch, and triton. Use --no-perf only when the user wants correctness or execution validation without profiling.
Do not equate successful execution with numerical correctness. Supply a representative testcase whenever an optimized result will be accepted or rejected.
Compare kernel variants
Compare at least two implementations and identify the baseline explicitly:
magpie compare --kernel-config path/to/compare.yaml
Keep inputs, tolerances, warmup, iteration count, GPU allocation, and profiler settings identical across candidates. Reject candidates that fail correctness before considering performance rankings.
For PyTorch without a testcase, Magpie's built-in check only verifies that each result is finite; it does not prove numerical equivalence between variants. Require a testcase for numerical validation.
Benchmark inference
Prefer a checked-in benchmark config:
magpie benchmark --benchmark-config path/to/benchmark.yaml
The stable public CLI supports vllm, sglang, and atom. It supports direct docker and local run modes; use YAML configuration and the repository's Ray examples for distributed execution. Do not advertise integrations that exist only in internal enums or partial code paths as stable.
Enable profiling deliberately: profiler runs perturb latency and should not replace a clean baseline. Compare throughput, completed requests, TTFT, TPOT, ITL, and end-to-end latency using equivalent workloads.
Post-process traces with TraceLens
Enable TraceLens in the profiled benchmark YAML; torch traces are its required input:
benchmark:
profiler:
torch_profiler:
enabled: true
tracelens:
enabled: true
analysis_mode: inference
analysis_stages: all
export_format: csv
Use analysis_mode: inference for vLLM/SGLang. It splits the rank-0 trace into prefilldecode, decode, and prefill stages when available, runs TraceLens post-processing, and writes full stage reports plus compact *_kernel_roofline_simple.csv files under the benchmark workspace's tracelens/ directory. For direct PyTorch trace reporting, use analysis_mode: pytorch.
Open the compact roofline CSVs first. Rank rows by kernel_time_ms_sum or time_pct; then use roofline_bound, arithmetic intensity, achieved TFLOP/s or TB/s, and pct_roofline_mean to form an optimization hypothesis. Confirm benchmark_report.json.tracelens_analysis has outputs and no error before treating post-processing as successful. Use analysis_mode: pytorch when the task specifically needs the legacy direct single-rank or multi-rank collective reports.
Magpie's integrated TraceLens stage produces CSV/Excel analysis artifacts, not an agent-written analysis.md. If the user requests a prioritized agentic report, pass the captured trace to the separate tracelens-analysis-orchestrator skill when installed; keep that result distinct from Magpie's benchmark report.
Analyze existing traces and find source
Run standalone gap analysis with --trace-dir directly on benchmark:
magpie benchmark \
--trace-dir path/to/torch_trace \
--top-k 20 \
--find-kernel-sources \
--kernel-source-repos path/to/repository
Do not insert a gap-analysis positional token; it is not a CLI subcommand. Inspect the generated aggregate and per-rank CSVs, and preserve source-mapping confidence rather than assuming every normalized kernel name maps uniquely.
Drive the optimization loop
- Run an unprofiled baseline benchmark and save its config and report.
- Repeat with torch profiling and TraceLens inference post-processing enabled.
- Review stage-level TraceLens roofline summaries to classify dominant operations and likely compute, memory, or communication limits.
- Run gap analysis over the representative steady-state window to rank concrete kernels.
- Select bottlenecks by total contribution, not only single-dispatch duration.
- Map the selected kernel to source and an executable testcase.
- Generate isolated candidate implementations; preserve the baseline.
- Use
analyzefor iteration, thencomparewith correctness gates to rank candidates. - Re-run the original unprofiled benchmark with the winning candidate and the same workload. Report both kernel-level and end-to-end changes, including regressions.
Stop before claiming success if correctness is unproven, the benchmark inputs changed, the source mapping is uncertain, or the end-to-end improvement is within run-to-run noise.
Use MCP tools when available
Prefer Magpie MCP tools for structured agent workflows such as hardware inspection, kernel discovery, config generation, analyze/compare, optimization suggestions, result lookup, report comparison, Ray job management, and benchmark batches.
Do not pass a CLI analyze_report.json wrapper directly to an MCP tool that expects one result object's performance_state and performance_result. Do not assume every CLI option exists in MCP; kernel-source enrichment is currently exposed by the CLI gap-analysis path.
Additional resources
- Full CLI reference: reference.md
- Copy-paste command examples: examples.md
파일 메타데이터
name: magpie-kernel-evaluator description: Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.
원문 보기
---
name: magpie-kernel-evaluator
description: Benchmarks LLM inference and drives GPU kernel optimization with Magpie. Use when the user wants to benchmark vLLM, SGLang, or Atom; capture torch traces; post-process inference traces with TraceLens into prefill/decode and roofline reports; identify top bottleneck kernels or map profiler names to source; analyze or compare HIP, CUDA, PyTorch, or Triton kernels; validate and rank optimized variants; run local, container, or Ray workloads; or mentions Magpie, TraceLens, gap analysis, TTFT, TPOT, kernel evaluation, or AMD GPU optimization.
---
# Magpie
Use Magpie for three connected jobs:
1. Benchmark an inference workload and collect throughput, latency, and traces.
2. Analyze or compare GPU kernels for correctness and performance.
3. Drive an optimization loop from a benchmark bottleneck to source, candidate kernels, and end-to-end validation.
Describe only capabilities supported by the checked-out Magpie version. Do not infer support for an unverified ROCm, GPU, framework, or experimental integration.
## Choose the workflow
| User goal | Workflow |
|---|---|
| Evaluate one implementation | `analyze` |
| Rank two or more implementations | `compare` |
| Measure model-serving performance | `benchmark` |
| Find expensive kernels in existing traces | standalone gap analysis |
| Explain a profiled inference workload | benchmark → TraceLens post-processing → stage/roofline review |
| Optimize an end-to-end workload | benchmark → TraceLens/gap analysis → source mapping → analyze/compare → re-benchmark |
Use a YAML config for reproducible or multi-step work. Use inline CLI arguments for small exploratory runs.
## Preflight
1. Locate the Magpie repository or installed package.
2. Check the local interface before constructing commands:
```bash
magpie --help
magpie analyze --help
magpie compare --help
magpie benchmark --help
magpie --gpu-info
```
3. Check required tools, model access, GPU visibility, writable output space, and container or Ray access as applicable.
4. Read the repository compatibility matrix before making version claims. Treat ROCm or hardware not listed there as unverified until tested.
5. Record the exact config, model revision, image, environment variables, GPU allocation, and Magpie commit for benchmark comparisons.
Run from the Magpie repository root, install with `pip install -e .`, or use `python -m Magpie` when the `magpie` entry point is unavailable.
## Analyze a kernel
Prefer a config when correctness or profiler settings matter:
```bash
magpie analyze --kernel-config path/to/kernel.yaml
```
For a quick single-kernel run:
```bash
magpie analyze path/to/kernel.hip --type hip --testcase "./run_test.sh"
```
Supported public kernel types are `hip`, `cuda`, `pytorch`, and `triton`. Use `--no-perf` only when the user wants correctness or execution validation without profiling.
Do not equate successful execution with numerical correctness. Supply a representative testcase whenever an optimized result will be accepted or rejected.
## Compare kernel variants
Compare at least two implementations and identify the baseline explicitly:
```bash
magpie compare --kernel-config path/to/compare.yaml
```
Keep inputs, tolerances, warmup, iteration count, GPU allocation, and profiler settings identical across candidates. Reject candidates that fail correctness before considering performance rankings.
For PyTorch without a testcase, Magpie's built-in check only verifies that each result is finite; it does not prove numerical equivalence between variants. Require a testcase for numerical validation.
## Benchmark inference
Prefer a checked-in benchmark config:
```bash
magpie benchmark --benchmark-config path/to/benchmark.yaml
```
The stable public CLI supports `vllm`, `sglang`, and `atom`. It supports direct `docker` and `local` run modes; use YAML configuration and the repository's Ray examples for distributed execution. Do not advertise integrations that exist only in internal enums or partial code paths as stable.
Enable profiling deliberately: profiler runs perturb latency and should not replace a clean baseline. Compare throughput, completed requests, TTFT, TPOT, ITL, and end-to-end latency using equivalent workloads.
## Post-process traces with TraceLens
Enable TraceLens in the profiled benchmark YAML; torch traces are its required input:
```yaml
benchmark:
profiler:
torch_profiler:
enabled: true
tracelens:
enabled: true
analysis_mode: inference
analysis_stages: all
export_format: csv
```
Use `analysis_mode: inference` for vLLM/SGLang. It splits the rank-0 trace into `prefilldecode`, `decode`, and `prefill` stages when available, runs TraceLens post-processing, and writes full stage reports plus compact `*_kernel_roofline_simple.csv` files under the benchmark workspace's `tracelens/` directory. For direct PyTorch trace reporting, use `analysis_mode: pytorch`.
Open the compact roofline CSVs first. Rank rows by `kernel_time_ms_sum` or `time_pct`; then use `roofline_bound`, arithmetic intensity, achieved TFLOP/s or TB/s, and `pct_roofline_mean` to form an optimization hypothesis. Confirm `benchmark_report.json.tracelens_analysis` has outputs and no error before treating post-processing as successful. Use `analysis_mode: pytorch` when the task specifically needs the legacy direct single-rank or multi-rank collective reports.
Magpie's integrated TraceLens stage produces CSV/Excel analysis artifacts, not an agent-written `analysis.md`. If the user requests a prioritized agentic report, pass the captured trace to the separate `tracelens-analysis-orchestrator` skill when installed; keep that result distinct from Magpie's benchmark report.
## Analyze existing traces and find source
Run standalone gap analysis with `--trace-dir` directly on `benchmark`:
```bash
magpie benchmark \
--trace-dir path/to/torch_trace \
--top-k 20 \
--find-kernel-sources \
--kernel-source-repos path/to/repository
```
Do not insert a `gap-analysis` positional token; it is not a CLI subcommand. Inspect the generated aggregate and per-rank CSVs, and preserve source-mapping confidence rather than assuming every normalized kernel name maps uniquely.
## Drive the optimization loop
1. Run an unprofiled baseline benchmark and save its config and report.
2. Repeat with torch profiling and TraceLens inference post-processing enabled.
3. Review stage-level TraceLens roofline summaries to classify dominant operations and likely compute, memory, or communication limits.
4. Run gap analysis over the representative steady-state window to rank concrete kernels.
5. Select bottlenecks by total contribution, not only single-dispatch duration.
6. Map the selected kernel to source and an executable testcase.
7. Generate isolated candidate implementations; preserve the baseline.
8. Use `analyze` for iteration, then `compare` with correctness gates to rank candidates.
9. Re-run the original unprofiled benchmark with the winning candidate and the same workload. Report both kernel-level and end-to-end changes, including regressions.
Stop before claiming success if correctness is unproven, the benchmark inputs changed, the source mapping is uncertain, or the end-to-end improvement is within run-to-run noise.
## Use MCP tools when available
Prefer Magpie MCP tools for structured agent workflows such as hardware inspection, kernel discovery, config generation, analyze/compare, optimization suggestions, result lookup, report comparison, Ray job management, and benchmark batches.
Do not pass a CLI `analyze_report.json` wrapper directly to an MCP tool that expects one result object's `performance_state` and `performance_result`. Do not assume every CLI option exists in MCP; kernel-source enrichment is currently exposed by the CLI gap-analysis path.
## Additional resources
- Full CLI reference: [reference.md](reference.md)
- Copy-paste command examples: [examples.md](examples.md)
소스 확인
가격 및 실행 비용
- Skill 받기
- 가격 미확인
- 실행
- 실행 요구 사항이 확인되지 않았습니다. 제공처에서 Agent, API 및 서비스 요금을 확인하세요.
- 라이선스
- MIT
- 가격 미확인
- 가격을 아직 확인하지 못했습니다. 기존 소스 및 설치 링크는 계속 이용할 수 있습니다.
무료 다운로드가 무료 실행을 뜻하지 않습니다. 가격은 안전 등급이 아닙니다. 가격 정보 제출 →
소스 재검토 필요
소스가 변경되었거나 동기화에 실패했습니다. 설치 전에 현재 소스를 확인하세요.
설치 전 검토: 자동 설치 피하기
라이선스: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Stars/forks activity: 332 stars, 30 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
도구 목록은 메타데이터이며 테스트된 호환성이 아닙니다. 프롬프트는 제안입니다.
작은 작업부터 시작
- 1소스를 읽고 입력, 출력, 의존성 및 권한을 확인하세요.
- 2Agent에게 계획을 요청하고 설정과 비용을 승인한 뒤 격리 환경에서 테스트하세요.
- 3출력과 변경 파일을 확인하고 실제 실행 결과만 보고하세요. 재현을 위해 소스 버전을 보관하세요.
소스에서 의존성, API 키 및 외부 서비스 비용을 확인하세요. 공개 저장소라고 모든 서비스가 무료는 아닙니다.
출처 및 사용 안내
등록됨
메타데이터와 검토 신호는 참고용입니다. 인기, 소스 발견, 실행 성공은 서로 다른 사실입니다.
- 소스 저장소
- amd/skills
- 라이선스
- MIT
- 버전
- 1.0.0
- 최근 GitHub 푸시
- 2026년 9월 5일
- 목록 업데이트
- 2026년 9월 13일
목록에 보고된 버전입니다. 소스 릴리스를 확인하세요.
품질
69/100
유망
신뢰
64/100
샌드박스 전용
감사
76/100
검토 필요
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Stars/forks activity: 332 stars, 30 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- 결과
- —
복사는 설치가 아닙니다. 설치 수는 성공 보고에 기반하며 전체 품질을 보장하지 않습니다.
Agent 연결
Registry API를 통해 동일한 결정, 신뢰, 감사, 사용 사례, 설치 신호를 제공하므로 Agent가 UI를 스크래핑하지 않고도 순위를 매길 수 있습니다.
추가 정보
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"Permission surface needs review: secrets or environment access, shell or command execution",
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"Dependency/runtime risk: command execution surface, credential or environment access",
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"Permission surface needs review: secrets or environment access, shell or command execution",
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"Dependency/runtime risk: command execution surface, credential or environment access"
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"label": "Promising"
},
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"scenario": "Research agents",
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},
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{
"slug": "amd-quark-torch-llm-ptq",
"name": "quark-torch-llm-ptq",
"url": "https://www.openagentskill.com/skills/amd-quark-torch-llm-ptq",
"stars": 395,
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},
{
"slug": "hermes-labs-ai-lintlang",
"name": "lintlang",
"url": "https://www.openagentskill.com/skills/hermes-labs-ai-lintlang",
"stars": 137,
"install_command": "",
"trust_score": 73,
"audit_score": 76
}
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"high-compliance environments without internal security review",
"No major risk signals from current metadata",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"Financial research output is not financial advice; require human review before any live investment decision."
],
"agent_contract": {
"task_input": "Use magpie-kernel-evaluator in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 72/100 Strong shortlist",
"Audit: 76/100 Needs review",
"Safety: 32/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "amd-magpie-kernel-evaluator (magpie-kernel-evaluator)",
"install_command": "",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "amd-magpie-kernel-evaluator",
"task": "Use magpie-kernel-evaluator in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/amd-magpie-kernel-evaluator",
"api": "https://www.openagentskill.com/api/agent/skills/amd-magpie-kernel-evaluator",
"audit": "https://www.openagentskill.com/skills/amd-magpie-kernel-evaluator/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=amd-magpie-kernel-evaluator&task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20magpie-kernel-evaluator%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/amd-magpie-kernel-evaluator/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/amd-magpie-kernel-evaluator"
}
}제작자 도구
등록 출처
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[](https://www.openagentskill.com/skills/amd-magpie-kernel-evaluator/audit)
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