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The export command converts a trained Neurenix model to different formats for deployment across various platforms and frameworks.

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Usage

neurenix export --model <model_file> --format <format> [options]

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Options

OptionTypeRequiredDefaultDescription
--modelstringYes-Path to the model file (.nrx format)
--formatstringYes-Export format (see formats below)
--outputstringNoAuto-generatedOutput file or directory path
--optimizeflagNofalseOptimize the exported model
--quantizestringNononeQuantization type (int8, fp16, none)

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Supported Formats

FormatExtensionDescriptionUse Case
onnx.onnxOpen Neural Network ExchangeCross-platform deployment
torchscript.ptPyTorch TorchScriptPyTorch production
tensorflow_tf/TensorFlow SavedModelTensorFlow serving
tflite.tfliteTensorFlow LiteMobile and edge devices
wasm.wasmWebAssemblyBrowser and edge deployment
c.cC source codeEmbedded systems

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Examples

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Export to ONNX

neurenix export --model models/model.nrx --format onnx

Loading model from models/model.nrx...
Exporting model to onnx format...
Model exported to models/model.onnx

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Export to TorchScript

neurenix export --model models/model.nrx --format torchscript

Loading model from models/model.nrx...
Exporting model to torchscript format...
Model exported to models/model.pt

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Export to TensorFlow

neurenix export --model models/model.nrx --format tensorflow

Loading model from models/model.nrx...
Exporting model to tensorflow format...
Model exported to models/model_tf

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Export to TensorFlow Lite

neurenix export --model models/model.nrx --format tflite

Loading model from models/model.nrx...
Exporting model to tflite format...
Model exported to models/model.tflite

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Export to WebAssembly

neurenix export --model models/model.nrx --format wasm

Loading model from models/model.nrx...
Exporting model to wasm format...
Model exported to models/model.wasm

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Export to C

neurenix export --model models/model.nrx --format c

Loading model from models/model.nrx...
Exporting model to c format...
Model exported to models/model.c

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Custom output path

neurenix export \
  --model models/model.nrx \
  --format onnx \
  --output deployment/production.onnx

Loading model from models/model.nrx...
Exporting model to onnx format...
Model exported to deployment/production.onnx

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Export with optimization

neurenix export \
  --model models/model.nrx \
  --format onnx \
  --optimize

Loading model from models/model.nrx...
Exporting model to onnx format...
Optimizing model...
Model exported to models/model.onnx

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Export with quantization

neurenix export \
  --model models/model.nrx \
  --format tflite \
  --quantize int8

Loading model from models/model.nrx...
Exporting model to tflite format...
Quantizing model to int8...
Model exported to models/model.tflite

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Export with all optimizations

neurenix export \
  --model models/model.nrx \
  --format onnx \
  --optimize \
  --quantize fp16 \
  --output deployment/optimized.onnx

Loading model from models/model.nrx...
Exporting model to onnx format...
Optimizing model...
Quantizing model to fp16...
Model exported to deployment/optimized.onnx

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Quantization Options

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INT8 Quantization

Reduces model size by ~75% with minimal accuracy loss: 
neurenix export \
  --model models/model.nrx \
  --format tflite \
  --quantize int8
Benefits:
  • Smaller model size (4x reduction)
  • Faster inference on compatible hardware
  • Lower memory usage
Trade-offs:
  • Small accuracy degradation (typically less than 1%)
  • Requires calibration data for best results

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FP16 Quantization

Reduces model size by ~50% with negligible accuracy loss: 
neurenix export \
  --model models/model.nrx \
  --format onnx \
  --quantize fp16
Benefits:
  • Smaller model size (2x reduction)
  • Faster inference on GPUs
  • Minimal accuracy loss
Trade-offs:
  • Less size reduction than INT8
  • Requires FP16-capable hardware for speedup

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Format-Specific Details

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ONNX (.onnx)

Best for: Cross-platform deployment, inference optimization 
neurenix export --model models/model.nrx --format onnx
Usage: 
import onnxruntime as ort

session = ort.InferenceSession("models/model.onnx")
outputs = session.run(None, {"input": input_data})

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TorchScript (.pt)

Best for: PyTorch production environments 
neurenix export --model models/model.nrx --format torchscript
Usage: 
import torch

model = torch.jit.load("models/model.pt")
output = model(input_tensor)

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TensorFlow Lite (.tflite)

Best for: Mobile apps (Android/iOS), edge devices 
neurenix export --model models/model.nrx --format tflite --quantize int8
Usage: 
import tensorflow as tf

interpreter = tf.lite.Interpreter(model_path="models/model.tflite")
interpreter.allocate_tensors()

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WebAssembly (.wasm)

Best for: Browser-based inference, edge computing 
neurenix export --model models/model.nrx --format wasm --optimize
Usage: 
const model = await loadWasmModel('models/model.wasm');
const output = model.predict(inputData);

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C (.c)

Best for: Embedded systems, microcontrollers 
neurenix export --model models/model.nrx --format c
Usage: 
#include "model.c"

float input[INPUT_SIZE];
float output[OUTPUT_SIZE];
model_predict(input, output);

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Error Handling

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Model not found

neurenix export --model nonexistent.nrx --format onnx

Error: Model file 'nonexistent.nrx' not found.

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Invalid format

neurenix export --model models/model.nrx --format invalid

usage: neurenix export [<args>]
neurenix export: error: argument --format: invalid choice: 'invalid' 
(choose from 'onnx', 'torchscript', 'tensorflow', 'tflite', 'wasm', 'c')

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Export error

neurenix export --model models/incompatible.nrx --format tflite

Error exporting model: Unsupported operation for TFLite export

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Best Practices

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1. Test exported models

Always verify exported models match original performance: 
# Export model
neurenix export --model models/model.nrx --format onnx

# Test original model
neurenix eval --model models/model.nrx --data data/test.csv

# Test exported model with your deployment framework
# Compare metrics to ensure accuracy is maintained

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2. Choose appropriate format for target platform

# Mobile deployment
neurenix export --model models/model.nrx --format tflite --quantize int8

# Web deployment
neurenix export --model models/model.nrx --format wasm --optimize

# Cloud deployment
neurenix export --model models/model.nrx --format onnx --optimize

# Embedded systems
neurenix export --model models/model.nrx --format c

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3. Optimize for production

Always use optimization for production deployments: 
neurenix export \
  --model models/model.nrx \
  --format onnx \
  --optimize \
  --quantize fp16

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4. Version exported models

neurenix export \
  --model models/model_v1.0.nrx \
  --format onnx \
  --output deployment/model_v1.0.onnx

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5. Document export settings

Create a script to reproduce exports: 
#!/bin/bash
# export_production.sh

neurenix export \
  --model models/production.nrx \
  --format onnx \
  --optimize \
  --quantize fp16 \
  --output deployment/production.onnx

echo "Export completed: deployment/production.onnx"

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Deployment Workflow

# 1. Train model
neurenix run train.py

# 2. Evaluate performance
neurenix eval --model models/model.nrx --data data/test.csv

# 3. Export for deployment
neurenix export \
  --model models/model.nrx \
  --format onnx \
  --optimize \
  --quantize fp16

# 4. Verify exported model
# Use your deployment framework to test

# 5. Deploy
neurenix serve --model models/model.nrx --port 8000

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See Also

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