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v0.1.0a1

Highlights

The first public alpha turns YAML into a local vLLM optimization run with owned server lifecycle, GuideLLM benchmarking, deterministic search, immutable artifacts, and decision-focused JSON, CSV, and HTML reports.

Release artifacts

Platform Install
PyPI pip install "vtune==0.1.0a1"
Linux/WSL runtime pip install "vtune[runtime]==0.1.0a1"

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

Experiment engine

  • Added typed YAML configuration, arbitrary vLLM flags and environment values, readiness polling, process ownership, trial management, baselines, retries, intelligent timeouts, and failure classification.
  • Added GuideLLM execution and normalized benchmark metrics.

Search, reports, and reproduction

  • Added Grid, Random, and Optuna TPE search with deterministic seeds, persistent Optuna storage, and duplicate prevention.
  • Stored commands, environment metadata, versions, timing, checksums, logs, and exit state for reproduction.
  • Added parameter effects, baseline comparison, score history, and throughput/latency views.

Security

  • Added loopback-only defaults, safe experiment names, and name-based secret redaction for persisted commands and environment values.

Validation and limitations

  • All 106 private tests passed.
  • One vLLM 0.28.0 and GuideLLM 0.7.3 compatibility experiment completed; this was not a general performance claim.
  • The wheel passed Ubuntu 24.04 and Windows Python 3.12 checks.
  • Experiment execution remains Linux-only.

Full changelog: first public release through v0.1.0a1.