Installation

This guide will help you install Entrenar and set up your development environment for neural network training with autograd, optimizers, and LoRA/QLoRA fine-tuning.

Prerequisites

Before installing Entrenar, ensure you have:

• Rust 1.70+: Install from rustup.rs
• Cargo: Comes bundled with Rust
• Git: For cloning the repository (optional)

# Verify Rust installation
rustc --version  # Should show 1.70 or higher
cargo --version

Installation Methods

Method 1: Add as Cargo Dependency (Recommended)

Add Entrenar to your Cargo.toml:

[dependencies]
entrenar = "0.1"
ndarray = "0.15"  # Required for tensor operations

Then run:

cargo build

Method 2: Clone and Build from Source

For development or to run examples:

# Clone the repository
git clone https://github.com/paiml/entrenar.git
cd entrenar

# Run tests to verify installation
cargo test

# Run quality gates
cargo clippy -- -D warnings
cargo fmt --check

# Build in release mode for performance
cargo build --release

Verifying Installation

Create a simple test file test_install.rs:

use entrenar::Tensor;

fn main() {
    // Create a simple tensor
    let x = Tensor::from_vec(vec![1.0, 2.0, 3.0], true);
    println!("Tensor created: {:?}", x.data());

    // Test autograd
    let y = &x * &x;  // y = x²
    println!("Forward pass successful!");

    println!("✅ Entrenar is installed correctly!");
}

Run it:

cargo run --example test_install

Expected output:

Tensor created: [1.0, 2.0, 3.0]
Forward pass successful!
✅ Entrenar is installed correctly!

Feature Flags

Entrenar supports optional features via Cargo feature flags:

[dependencies]
entrenar = { version = "0.1", features = ["simd", "quantization"] }

Available features:

Development Dependencies

For contributing or running the full test suite:

[dev-dependencies]
proptest = "1.0"         # Property-based testing
approx = "0.5"           # Floating-point comparisons
serde_json = "1.0"       # JSON serialization
criterion = "0.5"        # Benchmarking
cargo-mutants = "24.0"   # Mutation testing

Install development tools:

# Code coverage
cargo install cargo-llvm-cov

# Mutation testing
cargo install cargo-mutants

# Benchmarking
cargo install cargo-criterion

Platform-Specific Notes

Linux

No special configuration required. SIMD acceleration works out of the box on x86_64 and ARM64.

macOS

Apple Silicon (M1/M2) users get native ARM64 SIMD support:

# Verify ARM64 build
cargo build --release
file target/release/entrenar
# Should show: Mach-O 64-bit executable arm64

Windows

Windows users should use the MSVC toolchain:

rustup default stable-msvc
cargo build

IDE Setup

Visual Studio Code

Recommended extensions:

• rust-analyzer: IntelliSense and code completion
• CodeLLDB: Debugging support
• Even Better TOML: Cargo.toml syntax highlighting

RustRover / IntelliJ IDEA

The Rust plugin provides excellent support for Entrenar development.

Troubleshooting

Error: "cannot find crate ndarray"

Solution: Add ndarray = "0.15" to your Cargo.toml dependencies.

Error: "SIMD operations not available"

Solution: Ensure you're compiling in release mode for SIMD optimizations:

cargo build --release

Tests Failing on Fresh Install

Solution: Run with increased stack size for gradient checking tests:

RUST_MIN_STACK=8388608 cargo test

Slow Compile Times

Solution: Enable parallel compilation:

# Add to ~/.cargo/config.toml
[build]
jobs = 4  # Or number of CPU cores

Next Steps

Now that Entrenar is installed:

1. Quick Start - Train your first neural network
2. First Training Loop - Build a complete training pipeline
3. Core Concepts - Understand Entrenar's architecture

Getting Help

• Documentation: https://paiml.github.io/entrenar
• Issues: GitHub Issues
• Examples: See examples/ directory in the repository
• Tests: See src/*/tests.rs for usage patterns

Ready to train? Continue to Quick Start →