Torch-like deep learning framework for Javascript
npm install catniff
npm install catniff
`
Ensure you have Node v24 as well for things like float16 to work.
Tensors
Tensors in Catniff can be created by passing in a number or an nD array, and there are built-in methods that can be used to perform tensor arithmetic:
`js
const { Tensor } = require("catniff");
// Tensor init
const A = new Tensor([ 1, 2, 3 ]);
const B = new Tensor(3);
// Tensor addition (.val() returns the raw value rather than the tensor object)
console.log(A.add(B).val());
`
Autograd
To compute the gradient wrt multiple variables of our mathematical expression, we can simply set requiresGrad to true:
`js
const { Tensor } = require("catniff");
const X = new Tensor(
[
[ 0.5, -1.0 ],
[ 2.0, 0.0 ]
],
{ requiresGrad: true }
);
const Y = new Tensor(
[
[ 1.0, -2.0 ],
[ 0.5, 1.5 ]
],
{ requiresGrad: true }
);
const D = X.sub(Y);
const E = D.exp();
const F = E.add(1);
const G = F.log();
G.backward();
// X.grad and Y.grad are tensor objects themselves, so we call .val() here to see their raw values
console.log(X.grad.val(), Y.grad.val());
`
Optimizer
Catniff comes bundled with optimizers as well:
`js
const { Tensor, Optim } = require("catniff");
// Define some parameter
const w = new Tensor([1.0], { requiresGrad: true });
// Define a fake loss function: L = (w - 3)^2
const loss = w.sub(3).pow(2);
// Calculate gradient
loss.backward();
// Use Adam optimizer
const optim = new Optim.Adam([w]);
// Optimization step
optim.step();
console.log("Updated weight:", w.data); // Should move toward 3.0
`
Neural networks & Deep learning
There are built-in neural network constructs in Catniff as well, from simple prebuilt nn layers:
`js
const { Tensor, nn } = require("catniff");
// Linear layer with input size of 20 and output size of 10
const linear = nn.Linear(20, 10);
// RNN cell with input size of 32 and hidden size of 64
const rnnCell = nn.RNNCell(32, 64);
// Same thing but using GRU
const gruCell = nn.GRUCell(32, 64);
// Same thing but using LSTM
const lstmCell = nn.LSTMCell(32, 64);
// Forward passes
const a = Tensor.randn([20]);
const b = Tensor.randn([32]);
const c = Tensor.randn([64]);
linear.forward(a);
rnnCell.forward(b, c);
gruCell.forward(b, c);
lstmCell.forward(b, c, c);
`
to more advanced constructs like normalization, embedding, and attention:
`js
// 1. Embedding: tokens -> vectors
const embedding = new nn.Embedding(100, 64);
const tokens = new Tensor([[1, 5, 23], [8, 2, 15]]);
const embedded = embedding.forward(tokens);
// 2. Self-Attention
const attention = new nn.MultiheadAttention(64, 8, 0.1);
const [output, weights] = attention.forward(embedded, embedded, embedded);
// 3. Layer Normalization
const layerNorm = new nn.LayerNorm(64);
const normalized = layerNorm.forward(output);
console.log(normalized.val());
`
And it can still do much more, check out the docs and examples below for more information.
Documentation
Full documentation is available in ./docs/documentation.md.
All available APIs are in ./src/ if you want to dig deeper.
Examples
* Shakespeare-style text generator.
* Simple neural net for XOR calculation.
* N-th order derivative calculation.
* Tensors.
* Optimizer.
* Simple quadratic equation.
Todos
* More general tensor ops.
* More general neural net APIs.
* GPU acceleration.
* Comprehensive caching.
* Bug fixes.
* More detailed documentation.
* Code refactoring.
* Proper tests.
Cite Catniff
`bibtex
@misc{catniff,
author = {Phu Minh Nguyen},
title = {Catniff: Torch-like deep learning framework for Javascript},
year = {2025},
publisher = {GitHub},
url = {https://github.com/nguyenphuminh/catniff}
}
``