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v0.1.0a5

Highlights

  • Added native vllm bench serve workloads alongside GuideLLM.
  • Added opt-in parallel trials on explicit, non-overlapping GPU assignments.
  • Kept lifecycle, scoring, artifacts, and reports consistent across backends.

Release artifacts

Platform Install
PyPI pip install "vtune==0.1.0a5"
Linux/WSL runtime pip install "vtune[runtime]==0.1.0a5"

The universal wheel and source distribution are attached to the GitHub release.

Benchmark engine

  • Added benchmark.engine: vllm, forward-compatible benchmark arguments, canonical throughput aliases, request accounting, raw JSON, logs, commands, versions, repeats, retries, and timeout evidence.
  • Documented random, ShareGPT, Hugging Face, custom, and prefix-repetition data.

Parallel execution

  • Added execution.mode: local_parallel with named workers, GPU sets, and deterministic ports.
  • Rejected overlapping devices, GPU sharing, manual ports, and incompatible tensor parallelism before execution.
  • Isolated failures and cleaned owned processes on cancellation.

Validation and limitations

  • All 147 private tests and 23 subtests passed.
  • Python 3.11/3.12 package, documentation, metadata, and controlled two-process integration checks passed.
  • One OPT-125M vLLM 0.28.0 WSL2 run passed on an RTX 3080. Real multi-GPU execution was not tested; parallel measurements remained experimental.

Full changelog: v0.1.0a4...v0.1.0a5.