neurenix predict

The predict command loads a trained model and generates predictions on new input data, with support for various output formats.

Usage

neurenix predict --model <model_file> --input <data_path> [options]

Options

Option	Type	Default	Description
`--model`	string	required	Path to the trained model file
`--input`	string	required	Input data file or directory
`--output`	string	`predictions.csv`	Output file for predictions
`--batch-size`	integer	`32`	Batch size for prediction
`--device`	string	`auto`	Device to use (`cpu`, `cuda`, `auto`)
`--format`	string	auto	Output format (`csv`, `json`, `npy`)

Output Formats

The command supports multiple output formats:

csv: Comma-separated values (default)
json: JSON array format
npy: NumPy binary format (requires NumPy)

If --format is not specified, the format is inferred from the output file extension.

Examples

Basic prediction

neurenix predict --model models/classifier.nrx --input data/test.csv

Loading model from models/classifier.nrx...
Loading input data from data/test.csv...
Making predictions with batch size 32...
Saving predictions to predictions.csv...
Predictions saved to predictions.csv

Predict with custom output

neurenix predict \
  --model models/model.nrx \
  --input data/new_data.csv \
  --output results/predictions.csv

Loading model from models/model.nrx...
Loading input data from data/new_data.csv...
Making predictions with batch size 32...
Saving predictions to results/predictions.csv...
Predictions saved to results/predictions.csv

JSON output format

neurenix predict \
  --model models/classifier.nrx \
  --input data/test.csv \
  --output predictions.json \
  --format json

Loading model from models/classifier.nrx...
Loading input data from data/test.csv...
Making predictions with batch size 32...
Saving predictions to predictions.json...
Predictions saved to predictions.json

Large batch size for GPU

neurenix predict \
  --model models/model.nrx \
  --input data/large_dataset.csv \
  --batch-size 256 \
  --device cuda

Loading model from models/model.nrx...
Loading input data from data/large_dataset.csv...
Making predictions with batch size 256...
Saving predictions to predictions.csv...
Predictions saved to predictions.csv

NumPy output format

neurenix predict \
  --model models/model.nrx \
  --input data/images/ \
  --output predictions.npy \
  --format npy

Loading model from models/model.nrx...
Loading input data from data/images/...
Making predictions with batch size 32...
Saving predictions to predictions.npy...
Predictions saved to predictions.npy

CPU-only prediction

neurenix predict \
  --model models/model.nrx \
  --input data/test.csv \
  --device cpu

Input Data

The --input parameter accepts:

Single file: CSV, JSON, or other supported formats
Directory: Loads all compatible files in the directory

# Single file
neurenix predict --model model.nrx --input test_data.csv

# Directory of images
neurenix predict --model model.nrx --input images/test/

Error Handling

Model not found

neurenix predict --model missing.nrx --input data.csv

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

Input data not found

neurenix predict --model model.nrx --input missing.csv

Error: Input 'missing.csv' not found.

Prediction error

neurenix predict --model model.nrx --input incompatible_data.csv

Loading model from model.nrx...
Loading input data from incompatible_data.csv...
Error making predictions: Input shape mismatch

Batch Processing

For large datasets, adjust batch size based on available memory:

# Small batch for CPU
neurenix predict --model model.nrx --input large.csv --batch-size 16 --device cpu

# Large batch for GPU
neurenix predict --model model.nrx --input large.csv --batch-size 512 --device cuda

Performance Tip: Larger batch sizes generally provide better throughput on GPUs, while smaller batches may be necessary for CPU or limited memory scenarios.

Prediction Pipeline

1. Load trained model

neurenix predict --model models/production_model.nrx --input new_data.csv

2. Make predictions

The model processes input data in batches and generates predictions.

3. Save results

Predictions are saved in the specified format and location.

Best Practices

1. Use appropriate batch sizes

Match batch size to your hardware capabilities:

# CPU: smaller batches
neurenix predict --model model.nrx --input data.csv --batch-size 32 --device cpu

# GPU: larger batches
neurenix predict --model model.nrx --input data.csv --batch-size 256 --device cuda

2. Choose the right output format

# CSV for tabular data
neurenix predict --model model.nrx --input data.csv --format csv

# JSON for structured output
neurenix predict --model model.nrx --input data.csv --format json

# NPY for numerical arrays
neurenix predict --model model.nrx --input data.csv --format npy

3. Organize prediction outputs

Store predictions in organized directories:

mkdir -p predictions/$(date +%Y%m%d)
neurenix predict \
  --model models/model.nrx \
  --input data/batch_1.csv \
  --output predictions/$(date +%Y%m%d)/batch_1_predictions.csv

4. Use auto device selection

Let Neurenix choose the best available device:

neurenix predict --model model.nrx --input data.csv --device auto

Integration Examples

Batch prediction script

#!/bin/bash
for file in data/test_batch_*.csv; do
  output="predictions/$(basename $file .csv)_pred.csv"
  neurenix predict --model models/best.nrx --input $file --output $output
done

Python integration

import subprocess
import os

def run_predictions(model_path, input_data, output_path):
    cmd = [
        "neurenix", "predict",
        "--model", model_path,
        "--input", input_data,
        "--output", output_path,
        "--device", "auto"
    ]
    subprocess.run(cmd, check=True)

run_predictions(
    "models/classifier.nrx",
    "data/test.csv",
    "results/predictions.csv"
)

Commands

Usage

Options

Output Formats

Examples

Basic prediction

Predict with custom output

JSON output format

Large batch size for GPU

NumPy output format

CPU-only prediction

Input Data

Error Handling

Model not found

Input data not found

Prediction error

Batch Processing

Prediction Pipeline

1. Load trained model

2. Make predictions

3. Save results

Best Practices

1. Use appropriate batch sizes

2. Choose the right output format

3. Organize prediction outputs

4. Use auto device selection

Integration Examples

Batch prediction script

Python integration

See Also

​Usage

​Options

​Output Formats

​Examples

​Basic prediction

​Predict with custom output

​JSON output format

​Large batch size for GPU

​NumPy output format

​CPU-only prediction

​Input Data

​Error Handling

​Model not found

​Input data not found

​Prediction error

​Batch Processing

​Prediction Pipeline

​1. Load trained model

​2. Make predictions

​3. Save results

​Best Practices

​1. Use appropriate batch sizes

​2. Choose the right output format

​3. Organize prediction outputs

​4. Use auto device selection

​Integration Examples

​Batch prediction script

​Python integration

​See Also

Usage

Options

Output Formats

Examples

Basic prediction

Predict with custom output

JSON output format

Large batch size for GPU

NumPy output format

CPU-only prediction

Input Data

Error Handling

Model not found

Input data not found

Prediction error

Batch Processing

Prediction Pipeline

1. Load trained model

2. Make predictions

3. Save results

Best Practices

1. Use appropriate batch sizes

2. Choose the right output format

3. Organize prediction outputs

4. Use auto device selection

Integration Examples

Batch prediction script

Python integration

See Also