py2edg 0.1.0

One-line edge deployment for CV models — convert, quantize, benchmark, and ship to any device.

🚀 Py2Edg

One-line edge deployment for Computer Vision models

Convert, quantize, optimize, and benchmark CV models for any edge device — in a single function call.

Built by Mouissat Rabah Abderrahmane

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The Problem

Deploying a CV model to edge devices is painful. You need to:

1. Export PyTorch → ONNX (handle dynamic axes, opsets, tracing issues)
2. Optimize the graph (constant folding, operator fusion, dead code elimination)
3. Quantize (FP16? INT8? Dynamic? Static with calibration?)
4. Convert to target format (TFLite? OpenVINO? CoreML?)
5. Benchmark (latency, throughput, memory)
6. Repeat for each device...

Py2Edg does all of this in one line.

Quick Start

pip install py2edg

import py2edg as rcv

# Deploy to Raspberry Pi 4 — converts, optimizes, quantizes, benchmarks
report = rcv.deploy("yolov8n.pt", device="rpi4")

That's it. Py2Edg automatically:

• Converts your PyTorch model to ONNX
• Optimizes the computation graph
• Quantizes to INT8 (optimal for RPi4)
• Converts to TFLite (best format for RPi4)
• Benchmarks and generates a deployment report

Core API

rcv.deploy() — Full Pipeline

# Raspberry Pi 4
report = rcv.deploy("model.pt", device="rpi4")

# NVIDIA Jetson Nano
report = rcv.deploy("model.pt", device="jetson_nano")

# Orange Pi 5
report = rcv.deploy("model.pt", device="orange_pi")

# Custom configuration
report = rcv.deploy(
    "model.pt",
    device="rpi4",
    quantize="int8",
    input_shape=(1, 3, 320, 320),
    benchmark_runs=200,
)
report.print()

rcv.convert() — Format Conversion

# PyTorch → ONNX
rcv.convert("model.pt", target="onnx")

# ONNX with FP16 quantization
rcv.convert("model.pt", target="onnx", quantize="fp16")

# Auto-configure for device
rcv.convert("model.pt", device="jetson_nano")

# To TFLite with INT8
rcv.convert("model.onnx", target="tflite", quantize="int8")

rcv.benchmark() — Performance Measurement

stats = rcv.benchmark("model.onnx", input_shape=(1, 3, 640, 640))
print(f"Latency: {stats.latency_mean_ms:.1f} ms")
print(f"Throughput: {stats.throughput_fps:.0f} FPS")
print(f"P95: {stats.latency_p95_ms:.1f} ms")

rcv.compare() — Side-by-Side Comparison

rcv.compare(
    "model.onnx",
    "model_fp16.onnx",
    "model_int8.onnx",
    input_shape=(1, 3, 640, 640),
)
# ┌───────────────────┬──────────┬───────────┬─────────┬───────────┐
# │ Model             │ Size(MB) │ Mean(ms)  │   FPS   │ vs Base   │
# ├───────────────────┼──────────┼───────────┼─────────┼───────────┤
# │ model             │    25.30 │     18.42 │    54.3 │  baseline │
# │ model_fp16        │    12.70 │     12.15 │    82.3 │     1.52x │
# │ model_int8        │     6.80 │      8.91 │   112.2 │     2.07x │
# └───────────────────┴──────────┴───────────┴─────────┴───────────┘

rcv.validate() — Output Accuracy Check

result = rcv.validate(pytorch_model, "model_fp16.onnx")
print(f"Max diff: {result['max_diff']:.6f}")
print(f"Passed: {result['passed']}")

Built-in Device Profiles

Device	Format	Quantize	Notes
rpi4	TFLite	INT8	Best for real-time on RPi4
rpi5	TFLite	INT8	2x faster than RPi4
jetson_nano	ONNX	FP16	CUDA + TensorRT
jetson_orin	ONNX	FP16	Extremely powerful
orange_pi	ONNX	INT8	RK3588 NPU support
coral_tpu	TFLite	UINT8	Edge TPU compiler
android_cpu	TFLite	INT8	XNNPACK delegate
android_gpu	TFLite	FP16	GPU delegate
ios_coreml	CoreML	FP16	Apple Neural Engine
server_gpu	ONNX	FP16	TensorRT provider

Custom Devices

rcv.register_device(rcv.DeviceProfile(
    name="my_fpga",
    display_name="Custom FPGA Board",
    compute="npu",
    memory_mb=256,
    preferred_format="onnx",
    quantize="int8",
))

Deployment Recipes

Save and share reproducible deployment configs:

recipe = rcv.DeployRecipe(
    name="yolo-rpi4",
    model="yolov8n.pt",
    device="rpi4",
)
recipe.apply_device_defaults()
rcv.save_recipe(recipe, "deploy.yaml")

# Later, or on another machine:
recipe = rcv.load_recipe("deploy.yaml")
report = rcv.deploy(recipe=recipe)

CLI

# Full deployment
py2edg deploy model.pt --device rpi4

# Convert only
py2edg convert model.pt --target onnx --quantize fp16

# Benchmark
py2edg benchmark model.onnx --runs 200

# Inspect model
py2edg inspect model.onnx

# List devices
py2edg devices

# Create recipe
py2edg recipe create --device jetson_nano --model model.pt -o deploy.yaml

Architecture

py2edg/
├── api.py          # High-level one-liner API (convert, deploy, benchmark, ...)
├── converter.py    # Format conversion engine (PyTorch→ONNX→TFLite/OpenVINO)
├── quantizer.py    # Quantization (FP16, INT8 static/dynamic)
├── optimizer.py    # Graph optimization (fusion, pruning, simplification)
├── benchmark.py    # Speed/memory benchmarking with statistics
├── devices.py      # Edge device profiles (RPi, Jetson, Coral, mobile, ...)
├── recipe.py       # YAML-based deployment recipes
├── report.py       # Deployment report generation
├── cli.py          # Command-line interface
└── _imports.py     # Lazy optional dependency management

Installation Options

pip install py2edg                    # Core (ONNX Runtime)
pip install py2edg[torch]             # + PyTorch support
pip install py2edg[tflite]            # + TFLite conversion
pip install py2edg[openvino]          # + OpenVINO support
pip install py2edg[full]              # Everything

Contributing

git clone https://github.com/Rahimdzx/py2edg.git
cd py2edg
pip install -e ".[dev]"
pytest

License

MIT License — see LICENSE for details.

Author

Mouissat Rabah Abderrahmane

• AI & Robotics Engineer | MSc from Saint Petersburg State University
• GitHub: @Rahimdzx

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Made with ❤️ in Algeria 🇩🇿