Show HN: I created a new Deep Learning Framework

A developer released picodl, a from-scratch deep learning framework built solely on numpy with zero dependencies, featuring a hand-traced autograd engine and 25+ differentiable ops. The framework includes layers, losses, optimizers, and a training loop, installable via pip install picodl-nn. It aims to provide fully readable code for educational purposes.

// numpy-only · zero dependencies picodl is a from-scratch autograd engine and neural network stack - every gradient traced by hand, every op built on plain numpy. No hidden framework underneath. bash $ pip install picodl-nn // what's inside Each piece does one job and hands a plain Tensor to the next. Nothing is generated for you - every layer, loss, and optimizer is code you can read start to finish. tensor.py Tracks every op into a graph, topologically sorts on .backward . 25+ differentiable ops: matmul, conv2d, softmax, attention primitives, and more. layers.py Linear, Conv2D, Embedding, LayerNorm, BatchNorm2D, MaxPool2D, AvgPool2D, GlobalAvgPool, Dropout, GELU, TiedLinear. loss.py MSE, BinaryCrossEntropy, NLLLoss, CrossEntropyLoss - built from the same primitive ops as everything else. optim.py SGD, RMSprop, Adam, AdamW - decoupled weight decay included, per-parameter state tracked by identity. nn.py Chains layers, exposes params for the optimizer, saves and loads weights to any file extension you like. data.py / train.py BatchIterator for mini-batches, a train loop that accepts raw numpy or Tensor input directly. // thirty seconds in A small classifier - GELU hidden layer, Adam optimizer, saved to disk when done. python from picodl.nn import NeuralNet from picodl.layers import Linear, GELU from picodl.loss import CrossEntropyLoss from picodl.optim import AdamW from picodl.train import train net = NeuralNet Linear 784, 128 , GELU , Linear 128, 10 , train net, x train, y train, num epochs=10, loss=CrossEntropyLoss , optimizer=AdamW lr=0.001, weight decay=0.01 net.save "model.picodl"