Python Embedded AI
Powerful frameworks for integrating AI into embedded systems using Python. Optimize, train, and deploy edge models efficiently.
Getting Started
Install SDK
Use pip to install the Python SDK and its dependencies.
pip install emb-python-sdk
Version 2.0.0 now supports PyTorch Mobile and ONNX optimization.
Import and Initialize
from emb.sdk import PythonEdge
client = PythonEdge(
api_key="YOUR_API_KEY",
project="edge-computing"
)
Key Features
Tensor Conversion
Convert PyTorch, Keras, or ONNX models for edge deployment.
onnx2emb converter available
Model Optimization
Quantization, pruning, and compression for resource-limited devices.
Prune models by 50%+ W/O loss
Edge Inference
Run inference models on devices with < 0.1s latency.
Real-time inference
Usage Examples
Quantize Neural Network
import emb.quantize as qt
model = qt.Quantizer(model="my-model.onnx")
result = model.quant8()
print(f"Saved: {result.size} MB")
Run Prediction
from emb.models import ObjectDetector
detector = ObjectDetector("resnet34-edge")
frames = detector.stream_capture("/dev/video0")
results = [detector.predict(frame) for frame in frames]
Ready to Build with Python?
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