| Name | aidge-benchmark JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | Benchmarking tool for Eclipse Aidge - validate model correctness, precision, and performance across multiple libraries. |
| upload_time | 2025-11-04 12:27:36 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Eclipse Public License - v 2.0
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| keywords |
aidge
benchmark
inference time
performance
testing
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
> :warning: This repository is still under active construction :construction:. It is not functional yet.
<p align="center">
<img src="https://gitlab.eclipse.org/eclipse/aidge/aidge/-/raw/main/docs/source/_static/Logotype-aidge.png" alt="Aidge logo" width="800"/>
</p>
<p align="right">
<img src="https://gitlab.eclipse.org/eclipse/aidge/aidge_benchmark/badges/main/pipeline.svg?ignore_skipped=true" alt="Pipeline status"/>
<img src="https://gitlab.eclipse.org/eclipse/aidge/aidge_benchmark/badges/main/coverage.svg?job=coverage:ubuntu_python&key_text=Python+coverage&key_width=100" alt="Python coverage"/>
</p>
# :construction: Eclipse Aidge: Benchmarking Module
Benchmarking tool for [Eclipse Aidge](https://gitlab.eclipse.org/eclipse/aidge/aidge) – validate model correctness, precision, and performance across multiple libraries.
## Overview
This module benchmarks ML models across [Eclipse Aidge](https://gitlab.eclipse.org/eclipse/aidge/aidge) modules ([CPU](https://gitlab.eclipse.org/eclipse/aidge/aidge_backend_cpu), [CUDA](https://gitlab.eclipse.org/eclipse/aidge/aidge_backend_cuda), [exportCPP](https://gitlab.eclipse.org/eclipse/aidge/aidge_export_cpp)), as well as external libraries ([Torch](https://pytorch.org/), [ONNXRuntime](https://onnxruntime.ai/)). It measures:
- Correctness (output similarity)
- Precision
- Inference time (performance)
## Useful links
- [Eclipse Aidge main repository](https://gitlab.eclipse.org/eclipse/aidge/aidge)
- [Installation guide](#installation)
- [Usage](#usage)
- [CLI](#command-line-interface-cli)
- [Python API](#python-api)
## Quick start
**System Requirements**
- `python >= 3.10`
- `numpy >= 1.22.0`
- `aidge_core >= 0.7.0`
Any benchmarked library might require additional dependencies, notably `onnx` for external libraries.
Plotting results requires `matplolib` package.
## Installation
See the main Aidge repository for a general instructions on Aidge installation. Once these steps are completed, you can install the benchmarking library two ways:
### 1. Using pip (Recommended) (only available on Linux)
```bash
pip install aidge-benchmark
```
### 2. Build from source
**System Requirements**
- `CMake >= 3.18`
```bash
pip install . -v
```
### Verify installation
```bash
python -c "import aidge_core; import aidge_benchmark; create_benchmark_from_dict({})"
```
## Usage
### Command-line interface (CLI)
This module comes with a callable script: `aidge_benchmark`
```bash
aidge_benchmark --config-file conv2d.json -c -t -m aidge_export_cpp aidge_backend_cpu aidge_backend_cuda torch onnxruntime --save-directory benchmark_results
```
```bash
aidge_benchmark --help
```
More details on the [tutorials](https://gitlab.eclipse.org/eclipse/aidge/aidge/-/blob/dev/examples/tutorials/Benchmarking/benchmark.ipynb?ref_type=heads)
### Python API
Here is an example script to generate a result and time comparison of several libraries with a reference library for the convolution operation and save results as an svg image.
```python
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
import aidge_benchmark as bench
from aidge_onnx import convert_aidge_to_onnx
import aidge_backend_cpu # ensures backend is registered
import aidge_export_cpp # ensures backend is registered
# List of backends we want to test against the reference implementation
TESTED_LIBS: list[str] = ["torch", "aidge_backend_cpu", "aidge_export_cpp"]
REFERENCE_LIB: str = "onnxruntime"
# Benchmark test cases description file
BENCHMARK_FILE = Path("conv2d.json")
N_ITER = 10
# Helper function
def format_time_stats(times: list[float]) -> str:
"""Return mean ± std formatted in ms (scientific notation)."""
mean, std = np.mean(times), np.std(times)
return f"{mean*1e3:.2e} ± {std*1e3:.2e} ms"
def run_single_benchmark(cfg):
"""
Run a benchmark for a single configuration.
Returns a dictionary mapping backend name -> list of timings (seconds).
"""
# Create random input tensors according to the spec
inputs = cfg.generate_inputs()
# Build ONNX and Aidge model description from 'cfg' described with ONNX format
model_aidge = cfg.as_model(input_arrays=inputs)
model = convert_aidge_to_onnx(
model_aidge, f"test-model_{cfg.operation}_{label}", opset=21, ir_version=10
)
# update input names for ONNX
for idx, named_tensor in enumerate(inputs):
if idx < len(model.graph.input):
for node in model.graph.node:
for i, inp in enumerate(node.input):
if inp == model.graph.input[idx].name:
node.input[i] = named_tensor.name
model.graph.input[idx].name = named_tensor.name
# /!\ specific to ONNX format
# Determine how many inputs are actual data (excluding initializers)
nb_data_inputs = cfg.format.metadata["initializer_rank"]
onnx_param = {"model": model, "inputs": inputs[:nb_data_inputs]}
aidge_param = {"model": model_aidge, "inputs": inputs[:nb_data_inputs]}
backend_params = {
**{lib: onnx_param for lib in ["torch", "onnxruntime"]},
**{lib: aidge_param for lib in ["aidge_backend_cpu", "aidge_export_cpp"]},
}
# Reference run
ref_out = bench.compute_output(REFERENCE_LIB, **backend_params[REFERENCE_LIB])
ref_times = bench.measure_inference_time(
REFERENCE_LIB, **backend_params[REFERENCE_LIB], nb_iterations=N_ITER
)
print(f"\t{REFERENCE_LIB:<20}✔️\t{format_time_stats(ref_times)}")
results = {REFERENCE_LIB: ref_times}
# Test each backend and compare results
for lib in TESTED_LIBS:
out = bench.compute_output(lib, **backend_params[lib])
is_equal = bench.utils.compare_tensors(ref_out, out, verbose=False)
times = bench.measure_inference_time(
lib, **backend_params[lib], nb_iterations=N_ITER
)
print(f"\t{lib:<20}{'✔️' if is_equal else '❌'}\t{format_time_stats(times)}")
results[lib] = times
return results
if __name__ == "__main__":
scheme, default_cfg = bench.create_benchmark_from_json(str(BENCHMARK_FILE))
timing_results = {}
for label, cfg in scheme:
print(f"\n- {label}")
# Merge test config with default settings
full_cfg = default_cfg.override_with(cfg)
timing_results[label] = run_single_benchmark(full_cfg)
fig, _ = bench.visualize.ratio_plot(timing_results, REFERENCE_LIB)
fig.savefig(f"{str(BENCHMARK_FILE)}_performances.svg")
print(f"\nSaved: '{str(BENCHMARK_FILE)}_performances.svg'")
```
## License
Aidge is licensed under Eclipse Public License 2.0, as found in the [LICENSE](LICENSE).
Raw data
{
"_id": null,
"home_page": null,
"name": "aidge-benchmark",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "aidge, benchmark, inference time, performance, testing",
"author": null,
"author_email": null,
"download_url": null,
"platform": null,
"description": "> :warning: This repository is still under active construction :construction:. It is not functional yet.\n\n<p align=\"center\">\n <img src=\"https://gitlab.eclipse.org/eclipse/aidge/aidge/-/raw/main/docs/source/_static/Logotype-aidge.png\" alt=\"Aidge logo\" width=\"800\"/>\n</p>\n\n<p align=\"right\">\n <img src=\"https://gitlab.eclipse.org/eclipse/aidge/aidge_benchmark/badges/main/pipeline.svg?ignore_skipped=true\" alt=\"Pipeline status\"/>\n <img src=\"https://gitlab.eclipse.org/eclipse/aidge/aidge_benchmark/badges/main/coverage.svg?job=coverage:ubuntu_python&key_text=Python+coverage&key_width=100\" alt=\"Python coverage\"/>\n</p>\n\n# :construction: Eclipse Aidge: Benchmarking Module\n\nBenchmarking tool for [Eclipse Aidge](https://gitlab.eclipse.org/eclipse/aidge/aidge) \u2013 validate model correctness, precision, and performance across multiple libraries.\n\n## Overview\n\nThis module benchmarks ML models across [Eclipse Aidge](https://gitlab.eclipse.org/eclipse/aidge/aidge) modules ([CPU](https://gitlab.eclipse.org/eclipse/aidge/aidge_backend_cpu), [CUDA](https://gitlab.eclipse.org/eclipse/aidge/aidge_backend_cuda), [exportCPP](https://gitlab.eclipse.org/eclipse/aidge/aidge_export_cpp)), as well as external libraries ([Torch](https://pytorch.org/), [ONNXRuntime](https://onnxruntime.ai/)). It measures:\n- Correctness (output similarity)\n- Precision\n- Inference time (performance)\n\n## Useful links\n\n- [Eclipse Aidge main repository](https://gitlab.eclipse.org/eclipse/aidge/aidge)\n- [Installation guide](#installation)\n- [Usage](#usage)\n - [CLI](#command-line-interface-cli)\n - [Python API](#python-api)\n\n## Quick start\n\n**System Requirements**\n\n- `python >= 3.10`\n- `numpy >= 1.22.0`\n- `aidge_core >= 0.7.0`\n\nAny benchmarked library might require additional dependencies, notably `onnx` for external libraries.\n\nPlotting results requires `matplolib` package.\n\n## Installation\n\nSee the main Aidge repository for a general instructions on Aidge installation. Once these steps are completed, you can install the benchmarking library two ways:\n\n### 1. Using pip (Recommended) (only available on Linux)\n\n```bash\npip install aidge-benchmark\n```\n\n### 2. Build from source\n\n**System Requirements**\n\n- `CMake >= 3.18`\n\n```bash\npip install . -v\n```\n\n### Verify installation\n\n```bash\npython -c \"import aidge_core; import aidge_benchmark; create_benchmark_from_dict({})\"\n```\n\n## Usage\n\n### Command-line interface (CLI)\n\nThis module comes with a callable script: `aidge_benchmark`\n\n```bash\naidge_benchmark --config-file conv2d.json -c -t -m aidge_export_cpp aidge_backend_cpu aidge_backend_cuda torch onnxruntime --save-directory benchmark_results\n```\n\n```bash\naidge_benchmark --help\n```\n\nMore details on the [tutorials](https://gitlab.eclipse.org/eclipse/aidge/aidge/-/blob/dev/examples/tutorials/Benchmarking/benchmark.ipynb?ref_type=heads)\n\n### Python API\n\nHere is an example script to generate a result and time comparison of several libraries with a reference library for the convolution operation and save results as an svg image.\n\n```python\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom pathlib import Path\n\nimport aidge_benchmark as bench\nfrom aidge_onnx import convert_aidge_to_onnx\nimport aidge_backend_cpu # ensures backend is registered\nimport aidge_export_cpp # ensures backend is registered\n\n# List of backends we want to test against the reference implementation\nTESTED_LIBS: list[str] = [\"torch\", \"aidge_backend_cpu\", \"aidge_export_cpp\"]\nREFERENCE_LIB: str = \"onnxruntime\"\n# Benchmark test cases description file\nBENCHMARK_FILE = Path(\"conv2d.json\")\nN_ITER = 10\n\n\n# Helper function\ndef format_time_stats(times: list[float]) -> str:\n \"\"\"Return mean \u00b1 std formatted in ms (scientific notation).\"\"\"\n mean, std = np.mean(times), np.std(times)\n return f\"{mean*1e3:.2e} \u00b1 {std*1e3:.2e} ms\"\n\n\ndef run_single_benchmark(cfg):\n \"\"\"\n Run a benchmark for a single configuration.\n Returns a dictionary mapping backend name -> list of timings (seconds).\n \"\"\"\n # Create random input tensors according to the spec\n inputs = cfg.generate_inputs()\n\n # Build ONNX and Aidge model description from 'cfg' described with ONNX format\n model_aidge = cfg.as_model(input_arrays=inputs)\n model = convert_aidge_to_onnx(\n model_aidge, f\"test-model_{cfg.operation}_{label}\", opset=21, ir_version=10\n )\n # update input names for ONNX\n for idx, named_tensor in enumerate(inputs):\n if idx < len(model.graph.input):\n for node in model.graph.node:\n for i, inp in enumerate(node.input):\n if inp == model.graph.input[idx].name:\n node.input[i] = named_tensor.name\n model.graph.input[idx].name = named_tensor.name\n\n\n # /!\\ specific to ONNX format\n # Determine how many inputs are actual data (excluding initializers)\n nb_data_inputs = cfg.format.metadata[\"initializer_rank\"]\n\n onnx_param = {\"model\": model, \"inputs\": inputs[:nb_data_inputs]}\n aidge_param = {\"model\": model_aidge, \"inputs\": inputs[:nb_data_inputs]}\n\n backend_params = {\n **{lib: onnx_param for lib in [\"torch\", \"onnxruntime\"]},\n **{lib: aidge_param for lib in [\"aidge_backend_cpu\", \"aidge_export_cpp\"]},\n }\n\n # Reference run\n ref_out = bench.compute_output(REFERENCE_LIB, **backend_params[REFERENCE_LIB])\n ref_times = bench.measure_inference_time(\n REFERENCE_LIB, **backend_params[REFERENCE_LIB], nb_iterations=N_ITER\n )\n print(f\"\\t{REFERENCE_LIB:<20}\u2714\ufe0f\\t{format_time_stats(ref_times)}\")\n results = {REFERENCE_LIB: ref_times}\n\n # Test each backend and compare results\n for lib in TESTED_LIBS:\n out = bench.compute_output(lib, **backend_params[lib])\n is_equal = bench.utils.compare_tensors(ref_out, out, verbose=False)\n times = bench.measure_inference_time(\n lib, **backend_params[lib], nb_iterations=N_ITER\n )\n print(f\"\\t{lib:<20}{'\u2714\ufe0f' if is_equal else '\u274c'}\\t{format_time_stats(times)}\")\n results[lib] = times\n return results\n\nif __name__ == \"__main__\":\n scheme, default_cfg = bench.create_benchmark_from_json(str(BENCHMARK_FILE))\n timing_results = {}\n for label, cfg in scheme:\n print(f\"\\n- {label}\")\n\n # Merge test config with default settings\n full_cfg = default_cfg.override_with(cfg)\n timing_results[label] = run_single_benchmark(full_cfg)\n\n fig, _ = bench.visualize.ratio_plot(timing_results, REFERENCE_LIB)\n fig.savefig(f\"{str(BENCHMARK_FILE)}_performances.svg\")\n print(f\"\\nSaved: '{str(BENCHMARK_FILE)}_performances.svg'\")\n```\n\n## License\n\nAidge is licensed under Eclipse Public License 2.0, as found in the [LICENSE](LICENSE).\n",
"bugtrack_url": null,
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"summary": "Benchmarking tool for Eclipse Aidge - validate model correctness, precision, and performance across multiple libraries.",
"version": "0.1.0",
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"Homepage": "https://www.deepgreen.ai/en/platform",
"Issues": "https://gitlab.eclipse.org/eclipse/aidge/aidge_benchmark/-/issues/",
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