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The optimize command improves model performance by applying optimization techniques such as quantization, pruning, knowledge distillation, and hyperparameter tuning.
Usage
neurenix optimize --model <model_file> [options]
Options
| Option | Type | Default | Description |
|---|
--model | string | required | Path to the model file |
--output | string | auto | Output file for optimized model |
--technique | string | auto | Optimization technique |
--quantize | string | None | Quantization precision (int8, fp16, fp8) |
--prune | float | None | Pruning level (0.0 to 1.0) |
--data | string | None | Calibration data for optimization |
--config | string | None | Optimization configuration file |
--device | string | auto | Device to use for optimization |
Optimization Techniques
| Technique | Description |
|---|
quantize | Reduce precision to int8, fp16, or fp8 |
prune | Remove less important weights |
distill | Transfer knowledge to smaller model |
hyperparameter | Tune hyperparameters automatically |
auto | Automatically select best technique |
Examples
Auto optimization
neurenix optimize --model models/classifier.nrx
Loading model from models/classifier.nrx...
Optimizing model using auto technique...
Saving optimized model to models/classifier_optimized.nrx...
Optimization Results:
model_size_reduction: 0.45
inference_speedup: 2.3x
accuracy_change: -0.002
Model successfully optimized and saved to models/classifier_optimized.nrx
Quantize to int8
neurenix optimize \
--model models/model.nrx \
--technique quantize \
--quantize int8
Loading model from models/model.nrx...
Optimizing model using quantize technique...
Saving optimized model to models/model_int8.nrx...
Optimization Results:
model_size: 24.5 MB -> 6.2 MB
inference_time: 12.3ms -> 3.8ms
accuracy: 0.923 -> 0.918
Model successfully optimized and saved to models/model_int8.nrx
Quantize to fp16
neurenix optimize \
--model models/large_model.nrx \
--quantize fp16 \
--output models/large_model_fp16.nrx
Loading model from models/large_model.nrx...
Optimizing model using auto technique...
Saving optimized model to models/large_model_fp16.nrx...
Optimization Results:
model_size_reduction: 0.5
inference_speedup: 1.8x
accuracy_change: -0.001
Model successfully optimized and saved to models/large_model_fp16.nrx
Prune model
neurenix optimize \
--model models/model.nrx \
--technique prune \
--prune 0.3
Loading model from models/model.nrx...
Optimizing model using prune technique...
Saving optimized model to models/model_pruned_30.nrx...
Optimization Results:
weights_removed: 30%
model_size_reduction: 0.25
inference_speedup: 1.4x
accuracy_change: -0.015
Model successfully optimized and saved to models/model_pruned_30.nrx
Optimize with calibration data
neurenix optimize \
--model models/model.nrx \
--technique quantize \
--quantize int8 \
--data data/calibration.csv
Loading model from models/model.nrx...
Loading calibration data from data/calibration.csv...
Optimizing model using quantize technique...
Saving optimized model to models/model_int8.nrx...
Optimization Results:
model_size_reduction: 0.75
inference_speedup: 3.2x
accuracy_change: -0.005
Model successfully optimized and saved to models/model_int8.nrx
Use configuration file
neurenix optimize \
--model models/model.nrx \
--config configs/optimize.json
Loading model from models/model.nrx...
Optimizing model using quantize technique...
Saving optimized model to models/model_optimized.nrx...
Optimization Results:
model_size_reduction: 0.6
inference_speedup: 2.5x
accuracy_change: -0.008
Model successfully optimized and saved to models/model_optimized.nrx
Configuration File
Create a JSON configuration for complex optimization:
{
"technique": "quantize",
"quantize": "int8",
"calibration": {
"samples": 1000,
"method": "percentile"
},
"validation": {
"min_accuracy": 0.90
}
}
neurenix optimize --model model.nrx --config optimize_config.json
Quantization Precision
int8 (8-bit Integer)
- Best for: Edge deployment, mobile devices
- Size reduction: ~75%
- Speed improvement: 2-4x
- Accuracy loss: 1-3%
neurenix optimize --model model.nrx --quantize int8
fp16 (16-bit Float)
- Best for: GPU deployment
- Size reduction: ~50%
- Speed improvement: 1.5-2x
- Accuracy loss: less than 1%
neurenix optimize --model model.nrx --quantize fp16
fp8 (8-bit Float)
- Best for: Modern GPUs (H100, A100)
- Size reduction: ~75%
- Speed improvement: 2-3x
- Accuracy loss: less than 2%
neurenix optimize --model model.nrx --quantize fp8
Pruning Levels
| Level | Pruning | Use Case |
|---|
| 0.1 | 10% | Minimal optimization |
| 0.3 | 30% | Balanced optimization |
| 0.5 | 50% | Aggressive optimization |
| 0.7 | 70% | Maximum optimization |
# Light pruning
neurenix optimize --model model.nrx --prune 0.1
# Aggressive pruning
neurenix optimize --model model.nrx --prune 0.5
Higher pruning levels (>0.5) may significantly impact model accuracy. Always validate performance after pruning.
Optimization Results
The command outputs detailed optimization metrics:
Optimization Results:
model_size: 245.3 MB -> 61.2 MB (75% reduction)
inference_time: 45.6ms -> 12.3ms (3.7x speedup)
accuracy: 0.934 -> 0.928 (0.6% decrease)
throughput: 22 samples/sec -> 81 samples/sec
Error Handling
Model not found
neurenix optimize --model missing.nrx
Error: Model file 'missing.nrx' not found.
Invalid quantization precision
neurenix optimize --model model.nrx --quantize invalid
Error: Invalid quantization precision. Choose from: int8, fp16, fp8
Invalid pruning level
neurenix optimize --model model.nrx --prune 1.5
Error: Pruning level must be between 0.0 and 1.0
Optimization failed
neurenix optimize --model model.nrx --quantize int8
Loading model from model.nrx...
Error optimizing model: Quantization failed - model architecture not supported
Use Cases
1. Deploy to mobile devices
neurenix optimize \
--model models/production.nrx \
--quantize int8 \
--data data/calibration.csv \
--output models/mobile.nrx
2. Reduce inference costs
neurenix optimize \
--model models/large_model.nrx \
--quantize fp16 \
--output models/efficient.nrx
3. Speed up real-time inference
neurenix optimize \
--model models/detector.nrx \
--technique quantize \
--quantize int8 \
--device cuda
4. Compress models for storage
neurenix optimize \
--model models/checkpoint.nrx \
--prune 0.4 \
--output models/compressed.nrx
5. Automated optimization pipeline
# Try different optimization strategies
for technique in quantize prune auto; do
neurenix optimize \
--model models/base.nrx \
--technique $technique \
--output models/optimized_${technique}.nrx
done
Best Practices
1. Use calibration data
Provide representative data for better quantization:
neurenix optimize \
--model model.nrx \
--quantize int8 \
--data data/representative_samples.csv
2. Start with conservative settings
Begin with less aggressive optimization:
# Start with fp16
neurenix optimize --model model.nrx --quantize fp16
# If accuracy is acceptable, try int8
neurenix optimize --model model.nrx --quantize int8
3. Validate after optimization
Always check model performance:
# Optimize
neurenix optimize --model model.nrx --output optimized.nrx
# Validate
neurenix eval --model optimized.nrx --data data/test.csv
4. Keep original model
Never overwrite your original model:
# Good: explicit output
neurenix optimize --model model.nrx --output model_optimized.nrx
# Bad: might overwrite (don't do this)
# neurenix optimize --model model.nrx --output model.nrx
5. Document optimization settings
Save optimization configuration:
cat > optimize_config.json << EOF
{
"technique": "quantize",
"quantize": "int8",
"notes": "Optimized for mobile deployment"
}
EOF
neurenix optimize --model model.nrx --config optimize_config.json
Optimization Workflow
#!/bin/bash
# 1. Train model
neurenix run train.py
# 2. Evaluate baseline
neurenix eval --model models/model.nrx --data data/test.csv > baseline.txt
# 3. Optimize
neurenix optimize \
--model models/model.nrx \
--quantize int8 \
--data data/calibration.csv \
--output models/model_int8.nrx
# 4. Evaluate optimized
neurenix eval --model models/model_int8.nrx --data data/test.csv > optimized.txt
# 5. Compare results
diff baseline.txt optimized.txt
See Also