Node.js bindings for NumRs
npm install @numrs/nodeNumRs Node is the native Node.js binding for the NumRs numerical engine. It provides high-performance, hardware-accelerated (Metal/Accelerate on macOS, MKL/OpenBLAS elsewhere) tensor operations and deep learning capabilities.
- Hardware Acceleration: Uses Metal on Apple Silicon for GPU-like performance.
- Multithreading: Native parallelization via Rayon.
- Zero-Copy: Efficient memory sharing between Node.js and Rust.
- Deep Learning: Full Autograd engine and Neural Network modules.
- Type Safe: Full TypeScript support.
``bash`
npm install @numrs/node
> Note: Requires Rust to be installed on your system to compile the native extension.
> `bash`
> curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
>
`typescript
import { Tensor, nn } from '@numrs/node';
// Create tensors with autograd enabled
const x = Tensor.randn([2, 5], true);
const w = Tensor.randn([5, 10], true);
// Fast matrix multiplication
const y = x.matmul(w);
// Backward pass
y.backward();
console.log("Gradient:", x.grad());
`
`typescript
import { Sequential, nn } from '@numrs/node';
const model = new Sequential();
model.add_linear(new nn.Linear(10, 32));
model.add_relu(new nn.ReLU());
model.add_linear(new nn.Linear(32, 2));
// Forward pass
const input = Tensor.randn([100, 10]);
const output = model.forward(input);
`
Use the TrainerBuilder for a highly optimized, native training loop.
`typescript
import { TrainerBuilder, Dataset } from '@numrs/node';
// 1. Prepare Data
const input = Tensor.randn([100, 10]);
const target = Tensor.randn([100, 2]);
const dataset = new Dataset(input, target);
// 2. Train
const trainer = new TrainerBuilder(model, dataset)
.batch_size(16)
.learning_rate(0.01)
.optimizer("adam") // Supports: adam, sgd, rmsprop, etc.
.loss("mse") // Supports: mse, cross_entropy
.max_epochs(50)
.build();
const history = trainer.fit();
console.log("Final Loss:", history.final_loss);
`
| Category | Supported Algorithms |
| :----------------- | :--------------------------------------------------------------------------------------------------------------------------------------- |
| Optimizers | "sgd", "adam", "adamw", "nadam", "radam", "rmsprop", "adagrad", "adadelta", "lamb", "adabound", "lbfgs", "rprop" |"mse"
| Loss Functions | (Regression), "cross_entropy" (Classification) |
buffers without Tensor overhead.`javascript
const numrs = require('@numrs/node');
const a = new Float32Array([1, 2, 3, 4]);
const b = new Float32Array([5, 6, 7, 8]);
// Element-wise addition
const c = numrs.add(a, [2, 2], b, [2, 2]);
`$3
Export your trained models to ONNX for verifying graphs or interoperability.
javascript
model.save_onnx(dummy_input, "model.onnx");
``For full API documentation, please refer to the main NumRs Repository.
AGPL-3.0-only