Show HN: 처음부터 구축한 ML 프레임워크에서 12M 변환기를 교육했습니다.

A TypeScript ML framework with Rust native backends (CPU, CUDA, WebGPU) providing autograd, tensor operations, and neural network training at GPU speed. - Automatic differentiation -- full backward pass through an autograd tape - GPU acceleration -- CUDA (NVIDIA) and WebGPU (Metal/Vulkan/DX12) backends - PyTorch-like API -- familiar Tensor ,Module ,Parameter , optimizer classes - Comprehensive ops -- elementwise, matmul, conv1d/conv2d, pooling, reductions, activations - Built-in modules -- Linear ,Conv1d ,Conv2d ,Embedding ,ReLU ,Sigmoid ,Tanh - Optimizers -- SGD andAdam (AdamW) with learning rate scheduling npm install @mni-ml/framework import { Tensor, Linear, Adam, Parameter, softmax, crossEntropyLoss } from '@mni-ml/framework'; // Create tensors const x = Tensor.rand([32, 10]); const targets = [[0], [1], [2], /* ... */]; // Build a model const layer1 = new Linear(10, 64); const layer2 = new Linear(64, 3); // Forward pass let h = layer1.forward(x).relu(); let logits = layer2.forward(h); let loss = crossEntropyLoss(logits, targets); // Backward pass loss.backward(); // Optimize const params = [...layer1.parameters(), ...layer2.parameters()]; const optimizer = new Adam(params, 0.001); optimizer.step(); optimizer.zeroGrad(); // Creation Tensor.zeros([2, 3]) // zero-filled Tensor.ones([2, 3]) // one-filled Tensor.rand([2, 3]) // uniform [0, 1) Tensor.randn([2, 3]) // normal distribution Tensor.fromFloat32(data, shape) // from Float32Array // Arithmetic (with autograd) a.add(b) a.add(2.0) // addition a.sub(b) // subtraction a.mul(b) a.mul(2.0) // multiplication a.div(b) a.div(2.0) // division a.neg() // negation a.exp() a.log() // exponentials a.pow(2) // power // Activations a.relu() a.sigmoid() // Reductions a.sum(dim) a.sum() // sum along dim or all a.mean(dim) a.mean() // mean along dim or all a.max(dim) // max along dim // Comparisons (returns 0/1 tensor, no gradient) a.lt(b) a.gt(b) a.eq(b) a.isClose(b, tol) // Layout a.view(2, 3) // reshape a.permute(1, 0) // transpose a.contiguous() // ensure contiguous memory // Linear algebra a.matmul(b) // matrix multiplication // Convolution a.conv1d(weight, stride, padding) a.conv2d(weight, stride, padding) // Utilities a.clone() a.detach() // copy / detach from graph a.toString() // debug string a.backward() // run backward pass a.setRequiresGrad(true) // enable gradient tracking import { Linear, Conv1d, Conv2d, ReLU, Sigmoid, Embedding } from '@mni-ml/framework'; const linear = new Linear(inputSize, outputSize); const conv1d = new Conv1d(inChannels, outChannels, kernelSize, stride, padding); const conv2d = new Conv2d(inChannels, outChannels, kernelSize, stride, padding); const embedding = new Embedding(vocabSize, embeddingDim); // Use in forward pass const out = linear.forward(input); import { softmax, gelu, layerNorm, crossEntropyLoss, dropout, avgpool2d, maxpool2d, tile } from '@mni-ml/framework'; const sm = softmax(logits, dim); const g = gelu(x); const ln = layerNorm(x, gamma, beta, eps); const loss = crossEntropyLoss(logits, targets); const dropped = dropout(x, rate, training); const pooled = avgpool2d(x, kernelH, kernelW); const maxPooled = maxpool2d(x, kernelH, kernelW); const tiled = tile(x, [2, 1]); import { Adam, SGD } from '@mni-ml/framework'; const optimizer = new Adam(parameters, lr, beta1, beta2, eps, weightDecay); // or const optimizer = new SGD(parameters, lr); optimizer.step(); // update parameters optimizer.zeroGrad(); // clear gradients TypeScript API (tensor.ts, nn.ts, optimizer.ts) │ └─→ N-API Bridge (lib.rs) │ ├─→ CPU Backend (Vec, pure Rust) ├─→ CUDA Backend (cudarc + .cu kernels) └─→ WebGPU Backend (wgpu + .wgsl shaders) All three backends share the same autograd tape and tensor store. Feature flags are mutually exclusive at compile time: cpu -- default, no GPU requiredcuda -- NVIDIA GPU via CUDAwebgpu -- any GPU via wgpu (Metal, Vulkan, DX12) Only needed if you are contributing or want a custom build. Requires Rust. # CPU (default) npm run build:native # CUDA (requires CUDA toolkit) npm run build:native:cuda # WebGPU npm run build:native:webgpu # Build TypeScript npm run build MIT