neurenix export

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

Usage

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

Options

Option	Type	Required	Default	Description
`--model`	string	Yes	-	Path to the model file (`.nrx` format)
`--format`	string	Yes	-	Export format (see formats below)
`--output`	string	No	Auto-generated	Output file or directory path
`--optimize`	flag	No	`false`	Optimize the exported model
`--quantize`	string	No	`none`	Quantization type (`int8`, `fp16`, `none`)

Supported Formats

Format	Extension	Description	Use Case
`onnx`	`.onnx`	Open Neural Network Exchange	Cross-platform deployment
`torchscript`	`.pt`	PyTorch TorchScript	PyTorch production
`tensorflow`	`_tf/`	TensorFlow SavedModel	TensorFlow serving
`tflite`	`.tflite`	TensorFlow Lite	Mobile and edge devices
`wasm`	`.wasm`	WebAssembly	Browser and edge deployment
`c`	`.c`	C source code	Embedded systems

Examples

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

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

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

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

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

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

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

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

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

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

Quantization Options

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

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

Format-Specific Details

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})

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)

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()

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);

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);

Error Handling

Model not found

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

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

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')

Export error

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

Error exporting model: Unsupported operation for TFLite export

Best Practices

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

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

3. Optimize for production

Always use optimization for production deployments:

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

4. Version exported models

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

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"

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

Commands

Usage

Options

Supported Formats

Examples

Export to ONNX

Export to TorchScript

Export to TensorFlow

Export to TensorFlow Lite

Export to WebAssembly

Export to C

Custom output path

Export with optimization

Export with quantization

Export with all optimizations

Quantization Options

INT8 Quantization

FP16 Quantization

Format-Specific Details

ONNX (.onnx)

TorchScript (.pt)

TensorFlow Lite (.tflite)

WebAssembly (.wasm)

C (.c)

Error Handling

Model not found

Invalid format

Export error

Best Practices

1. Test exported models

2. Choose appropriate format for target platform

3. Optimize for production

4. Version exported models

5. Document export settings

Deployment Workflow

See Also

​Usage

​Options

​Supported Formats

​Examples

​Export to ONNX

​Export to TorchScript

​Export to TensorFlow

​Export to TensorFlow Lite

​Export to WebAssembly

​Export to C

​Custom output path

​Export with optimization

​Export with quantization

​Export with all optimizations

​Quantization Options

​INT8 Quantization

​FP16 Quantization

​Format-Specific Details

​ONNX (.onnx)

​TorchScript (.pt)

​TensorFlow Lite (.tflite)

​WebAssembly (.wasm)

​C (.c)

​Error Handling

​Model not found

​Invalid format

​Export error

​Best Practices

​1. Test exported models

​2. Choose appropriate format for target platform

​3. Optimize for production

​4. Version exported models

​5. Document export settings

​Deployment Workflow

​See Also

Usage

Options

Supported Formats

Examples

Export to ONNX

Export to TorchScript

Export to TensorFlow

Export to TensorFlow Lite

Export to WebAssembly

Export to C

Custom output path

Export with optimization

Export with quantization

Export with all optimizations

Quantization Options

INT8 Quantization

FP16 Quantization

Format-Specific Details

ONNX (.onnx)

TorchScript (.pt)

TensorFlow Lite (.tflite)

WebAssembly (.wasm)

C (.c)

Error Handling

Model not found

Invalid format

Export error

Best Practices

1. Test exported models

2. Choose appropriate format for target platform

3. Optimize for production

4. Version exported models

5. Document export settings

Deployment Workflow

See Also