'sglang' 검색 결과

Add, rename, or review SGLang environment variables using consistent definitions, types, names, and access methods. The skill also guides migration from old names and temporary test overrides that restore the original values afterward.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new SGLang environment variable
• Migrating deprecated environment variable names
• Temporarily overriding settings in tests

Integrate or upgrade cache-dit in SGLang diffusion, the image generation system. Follow guidance for resolving code conflicts, adding cache settings, testing feature combinations, and comparing generated images using quality measurements and logs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Upgrading cache-dit in SGLang diffusion
• Resolving conflicts when porting upstream changes
• Adding per-request cache settings

Develop lightweight CUDA operations for SGLang using JIT, which compiles code on first use. A tensor-scaling example explains implementation conventions, input validation, memory access helpers, and when to choose this approach.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add a lightweight CUDA operation to SGLang
• Choose between JIT and ahead-of-time compilation
• Validate tensor inputs in a kernel launcher

Add a diffusion model or Diffusers generation pipeline to SGLang. Examine reference code, choose a suitable pipeline structure, reuse existing components, and implement model settings and processing stages, with guidance for later multi-GPU support.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new diffusion model to SGLang
• Porting a Diffusers generation pipeline
• Adding a variant by reusing existing model components

Add or verify model support in SGLang's Rust processor so chat requests produce the same prompt text and token IDs as Python. Use reference-generated test cases to find differences in tools, reasoning settings, conversation history, and errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding model support to sglang-processor
• Porting model rendering from sgl-router
• Rechecking prompt behavior after a Dynamo update

Organize SGLang GPU operations when adding or moving code. It guides API placement, operation registration, test organization, and migration checks to preserve existing behavior, references, and continuous integration coverage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose where to place a new GPU operation
• Split or move existing operation files
• Review API registration and test placement

Quantize diffusion transformers with NVIDIA ModelOpt, using FP8 or NVFP4 lower-precision numbers, and prepare the saved weights for SGLang Diffusion. Follow a workflow for loading, checking output quality, and comparing speed under matching conditions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing ModelOpt FP8 weights for SGLang Diffusion
• Checking quality after diffusion model quantization
• Comparing BF16 and quantized generation speed

Guide changes to SGLang runtime configuration and shared process state. Identify where resolved settings, runtime flags, resources, and per-forward state belong, and use the documented access patterns and checks when developing or testing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review how server settings are read and updated
• Choose where shared and per-forward state should live
• Check configuration publication order in worker processes

Launch an SGLang server, run a quick accuracy check, and capture an execution trace for performance inspection. Return the trace location and generated files, ready to open in Perfetto or Chrome tracing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Capture an execution trace of SGLang inference
• Check basic accuracy before profiling
• Record input processing and token generation separately

Understand and modify SGLang's CI pipelines, including test stage order, start conditions, early stopping after failures, and workload splitting. Use the guide to add jobs and investigate failed, skipped, or cancelled checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add a test job to an SGLang CI stage
• Investigate failed or skipped CI checks
• Update test partitions and waiting job counts

Write and register SGLang unit and end-to-end tests, from choosing models and file locations to reusing server fixtures. Follow project rules for meaningful assertions, CI registration, dependency mocking, and safe server cleanup after tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding CPU-only unit tests for component logic
• Testing features through a running SGLang server
• Registering tests in an appropriate CI suite

Prepare batches of merged SGLang pull requests for a release branch. It checks conflicts and previous attempts, starts the cherry-pick workflow, monitors completion, and reports created pull requests or failure reasons.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare multiple merged PRs for a release branch
• Check conflicts before starting workflows
• Detect previous cherry-pick PRs

답변 평가를 활용하는 강화 학습으로 대규모 언어 모델을 추가 훈련하는 절차를 안내합니다. Megatron-LM과 SGLang을 결합해 데이터 준비, 병렬 실행, 맞춤형 생성을 다룹니다.

• 언어 모델의 GRPO 훈련 시작
• 답변 생성과 훈련을 동시에 실행
• 도구를 사용하는 에이전트 훈련

Measure denoising latency in SGLang diffusion models and identify slow operations with profiler traces. Compare performance and output quality on the same GPUs, check existing optimizations, and gather evidence before starting low-level kernel work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measuring diffusion denoising latency
• Ranking slow operations from profiler traces
• Checking whether existing optimizations address a bottleneck

Guide changes and reviews for SGLang’s Scheduler, TokenizerManager, and ModelRunner classes. Keep initialization in small, replaceable helpers and separate computation from coordination so large classes retain clear responsibilities and explicit dependencies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing changes to SGLang’s three large classes
• Splitting initialization into replaceable helpers
• Moving computation out of ModelRunner

Convert an existing SGLang cookbook page to shared templates driven by model configuration. Preserve its deployment commands and interactive choices, transfer eligible benchmark measurements, and check equivalence, links, and rendered behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Migrating a legacy SGLang cookbook page
• Moving deployment choices into model configuration
• Transferring reproducible benchmark measurements

Choose SGLang Diffusion settings for a specific model, GPU, and memory budget. Compare generation speed, memory use, and output quality to select command-line options, including CPU offloading, multiple GPUs, caching, and reduced precision.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing generation settings for a specific model and GPU
• Reducing GPU memory use to fit a memory budget
• Comparing performance settings across multiple GPUs

Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Locate where a multi-GPU inference run freezes
• Compare logs from distributed processes
• Trace the first difference in process state

Investigate SGLang serving failures by collecting server state, preserving failing requests, and replaying them on a clean target. Summarize evidence and likely causes, then choose the next debugging tool for crashes, stalls, or performance regressions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate failed health checks and growing queues
• Reproduce request-specific timeouts or crashes
• Investigate output changes after deployment

Investigate recurring SGLang automated test failures by comparing logs, commit history, and GPU environments. Narrow down code changes or machine-specific problems, optionally reproduce failures remotely, and summarize the evidence in a structured report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding the change behind a recurring test failure
• Checking whether failures depend on a GPU or runner
• Reproducing a test failure on a remote GPU server

Guide test design for SGLang's scripted runtime by deciding when a test harness API adds real value. Favor direct reads, meaningful outcome checks, and the engine's actual execution loop over unnecessary wrappers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Decide whether a test harness needs a new API
• Review wrappers around directly accessible test state
• Test timeout behavior through the real engine loop

Add a model page to the SGLang Cookbook using shared templates. Organize verified launch settings and measured benchmarks, create configuration files and documentation, and update navigation, vendor cards, and page validation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add documentation for a newly supported model
• Organize verified launch recipes by hardware
• Publish measured speed and accuracy results

Collect and review SGLang startup logs to identify unnecessary output and trace its source. Get saved logs and specific cleanup proposals while preserving useful diagnostics, with runtime changes left for user approval.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating repeated messages in startup logs
• Checking which startup warnings need attention
• Comparing startup logs across models and GPU counts

Apply consistent names to SGLang speculative decoding code, which verifies draft tokens to generate text. Distinguish tokens with or without the bonus token, counts, counters, and arrays when adding identifiers, renaming code, or reviewing changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Naming variables in speculative decoding code
• Renaming identifiers to match their meaning
• Reviewing metric and counter names