Observed since 21 Oct, 10:55 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
Reproducible benchmarking and profiling for AI hardware, workloads, and accelerators.
AIHW-Bench is a reproducible Python framework for benchmarking, profiling, comparing, and reporting AI workloads across CPUs, GPUs, embedded systems, simulator-backed environments, and extensible accelerator backends.
Measure the workload. Understand the hardware. Compare the results. Reproduce the experiment.
| Need | AIHW-Bench provides |
|---|---|
| Reproducible measurements | Versioned configuration, immutable sessions, checksums, deterministic suite manifests, and report metadata. |
| Clear comparisons | Latency, throughput, resource metrics, session comparisons, reports, dashboard views, and a local benchmark analysis assistant. |
| Hardware-aware execution | Vendor-neutral capability reporting with explicit detection and runtime boundaries. |
| Extensibility | Replaceable model loaders, benchmark backends, metrics, reporters, visualizers, and plugins. |
Configuration + workload
│
▼
Benchmark service ──► backend + hardware inspection
│ │
▼ ▼
immutable session ◄──────── measurements / observations
│
├──► metrics + statistics
├──► JSON / CSV / Markdown / HTML reports
├──► static dashboard
└──► local benchmark analysis assistant
python -m pip install aihw-bench
Optional runtime integrations are installed only when needed:
python -m pip install "aihw-bench[pytorch]"
python -m pip install "aihw-bench[onnx]"
python -m pip install "aihw-bench[all-backends]"
Run a deterministic reference benchmark and write a JSON report:
aihw-bench benchmark --backend reference --warmup 1 --iterations 10 --report json
Then inspect your environment and generated results:
aihw-bench doctor
aihw-bench report SESSION_ID --format html --format markdown
aihw-bench dashboard --storage-root .aihw-bench/sessions --output-dir dashboard
aihw-bench compare BASELINE_SESSION CANDIDATE_SESSION --output table
aihw-bench suite list
aihw-bench suite materialize --output-dir benchmarks
aihw-bench suite baselines --output-dir benchmarks
The suite ships deterministic synthetic inputs and reference fixtures. They validate benchmark plumbing and reproducibility; they are not universal real-device performance claims.
aihw-bench assistant SESSION_ID --storage-root .aihw-bench/sessions
The built-in assistant is a deterministic, metric-grounded local benchmark analysis assistant. It is not an LLM and does not send benchmark data to an external service.
AIHW-Bench distinguishes four capability states:
| State | Meaning |
|---|---|
| Detected | Hardware or software was identified by a host/runtime probe. |
| Reportable | Its metadata can be stored and displayed. |
| Runnable | An installed backend can execute a benchmark for that target. |
| Accelerated | The selected workload/runtime actually uses the relevant acceleration path. |
This distinction matters: FPGA and RTL information can be detected or reported without implying synthesis, board programming, or vendor-tool execution. See the hardware support guide.
| Area | Built-in scope | Optional or plugin scope |
|---|---|---|
| CPU | Reference and CPU benchmark backends | Runtime-specific optimization paths |
| GPU | CUDA, ROCm, and Intel GPU capability-aware target validation | Matching runtime/backend installation is required for execution and acceleration |
| Models | Metadata and loader contracts | PyTorch, ONNX Runtime, TensorFlow Lite, and third-party plugins |
| Embedded / specialized | Raspberry Pi, Jetson, Coral, FPGA placeholder, and RTL metadata/reporting | Board-specific, vendor, and simulator execution integrations |
Every benchmark session captures resolved configuration, hardware context, execution samples, metrics, diagnostics, and artifact checksums. Built-in reporters generate JSON, CSV, Markdown, and HTML; the static dashboard supports history browsing, filtering, comparison, and export.
AIHW-Bench v2.0.0 is feature-complete and maintained as a stable local benchmarking and reporting toolkit. Distributed remote execution, marketplace installation, real FPGA programming, and model-backed assistant providers remain extension areas rather than claims of the core package.
python -m pip install poetry
poetry install --with dev,docs
poetry run pytest
poetry run ruff check src tests scripts
poetry run black --check src tests scripts
poetry run mypy src
poetry run mkdocs build --strict
See the contribution guide, security policy, and engineering documentation.
AIHW-Bench is licensed under the Apache License 2.0.
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 2.0.1 | 1 Sept 2026 | 8 |
This describes our collected data; it is not a security assessment.
Observed since 21 Oct, 10:55 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:cae34855d5218696d46fa4da66594a98ec2b2223861ee318075e80eda1daa2a0