Documentation Index
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The Neurenix CLI provides a comprehensive set of commands for managing your machine learning projects, from initialization to deployment.
Installation
The Neurenix CLI is included when you install the neurenix package:
Basic Usage
All Neurenix CLI commands follow this pattern:
neurenix <command> [<args>]
Available Commands
The Neurenix CLI provides the following commands:
| Command | Description |
|---|
init | Initialize a new Neurenix project with folder structure and config |
run | Run a model training script with specified configuration |
eval | Evaluate a trained model with specific metrics |
export | Export a trained model to various formats (ONNX, TorchScript, etc.) |
serve | Serve a trained model as a RESTful API |
save | Save the current project state |
predict | Make predictions using a trained model |
hardware | Manage hardware settings and device selection |
preprocess | Preprocess input data for model training |
monitor | Monitor model training in real-time |
optimize | Optimize a model with hyperparameters or quantization |
dataset | Manage datasets for training and evaluation |
help | Display help information about commands |
Getting Help
To get help for any command:
# Show all available commands
neurenix help
# Get help for a specific command
neurenix help <command>
neurenix <command> --help
Common Workflows
Quick Start
# Initialize a new project
neurenix init --name my-project --template basic
# Navigate to project directory
cd my-project
# Train a model
neurenix run train.py
# Evaluate the model
neurenix eval --model models/model.nrx --data data/test.csv
# Export for deployment
neurenix export --model models/model.nrx --format onnx
# Serve the model
neurenix serve --model models/model.nrx --port 8000
Advanced Workflow
# Initialize with advanced template
neurenix init --name advanced-project --template advanced
# Train with custom configuration
neurenix run train.py --config config.json --epochs 50 --batch-size 64
# Monitor training
neurenix monitor --log-dir logs
# Optimize the model
neurenix optimize --model models/model.nrx --method quantization
# Export optimized model
neurenix export --model models/model.nrx --format onnx --optimize --quantize int8
Project Structure
When you initialize a Neurenix project, the CLI creates the following structure:
my-project/
├── config.json # Project configuration
├── train.py # Training script
├── data/ # Dataset directory
├── models/ # Saved models
├── configs/ # Additional configurations
└── logs/ # Training logs
Next Steps