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Koç University deep learning framework.
| Date | Stars |
|---|---|
| 2026-07-31 | 1433 |
| 2026-08-02 | 1433 |
| 2026-08-06 | 1433 |
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# Knet
[](https://denizyuret.github.io/Knet.jl/latest)
[](https://travis-ci.org/denizyuret/Knet.jl)
[](https://coveralls.io/github/denizyuret/Knet.jl?branch=master)
[](https://codecov.io/gh/denizyuret/Knet.jl)
[Knet](https://denizyuret.github.io/Knet.jl/latest) (pronounced "kay-net") is the [Koç
University](http://www.ku.edu.tr/en) deep learning framework implemented in
[Julia](http://docs.julialang.org) by [Deniz Yuret](http://www.denizyuret.com) and
collaborators. It supports GPU operation and automatic differentiation using dynamic
computational graphs for models defined in plain Julia. You can install Knet with the
following at the julia prompt: `using Pkg; Pkg.add("Knet")`. Some starting points:
* [Tutorial:](tutorial)
introduces Julia and Knet via examples.
* [Documentation:](https://denizyuret.github.io/Knet.jl/latest)
installation, introduction, design, implementation, full reference and deep learning chapters.
* [Examples:](examples)
more tutorials and example models.
* [Benchmarks:](http://denizyuret.github.io/Knet.jl/latest/tutorial/#Benchmarks-1)
comparison of Knet's speed with TensorFlow, PyTorch, DyNet etc.
* [Paper:](https://goo.gl/zeUBFr)
Yuret, D. "Knet: beginning deep learning with 100 lines of julia." In *Machine Learning Systems Workshop* at NIPS 2016.
* [KnetML:](https://github.com/KnetML)
github organization with Knet repos of models, tutorials, layer collections and other resources.
* [Images:](http://denizyuret.github.io/Knet.jl/latest/install/#Using-Amazon-AWS-1)
Knet machine images are available for [AWS](http://denizyuret.github.io/Knet.jl/latest/install/#Using-Amazon-AWS-1), [Singularity](https://github.com/KnetML/singularity-images) and [Docker](https://github.com/JuliaGPU/docker).
* [Issues:](https://github.com/denizyuret/Knet.jl/issues)
if you find a bug, please open a github issue.
* [knet-users:](https://groups.google.com/forum/#!forum/knet-users)
if you need help or would like to request a feature, please join this mailing list.
* [knet-dev:](https://groups.google.com/forum/#!forum/knet-dev)
if you would like to contribute to Knet development, please join this mailing list and check out these [tips](https://denizyuret.github.io/Knet.jl/latest/install/#Tips-for-developers-1).
* [knet-slack:](https://julialang.slack.com/messages/CDLKQ92P3/details) Slack channel for Knet.
* Related work: Please check out [Flux](https://github.com/FLuxML), [Mocha](https://github.com/pluskid/Mocha.jl), [JuliaML](https://github.com/JuliaML), [JuliaDiff](https://github.com/JuliaDiff), [JuliaGPU](https://github.com/JuliaGPU), [JuliaOpt](https://github.com/JuliaOpt) for related packages.
## Example
Here is a simple example where we define, train and test the
[LeNet](http://yann.lecun.com/exdb/lenet) model for the
[MNIST](http://yann.lecun.com/exdb/mnist) handwritten digit recognition dataset from scratch
using 15 lines of code and 10 seconds of GPU computation.
```julia
# Install packages before first run: using Pkg; pkg"add Knet IterTools MLDatasets"
using Knet, IterTools, MLDatasets
# Define convolutional layer:
struct Conv; w; b; end
Conv(w1,w2,nx,ny) = Conv(param(w1,w2,nx,ny), param0(1,1,ny,1))
(c::Conv)(x) = relu.(pool(conv4(c.w, x) .+ c.b))
# Define dense layer:
struct Dense; w; b; f; end
Dense(i,o; f=identity) = Dense(param(o,i), param0(o), f)
(d::Dense)(x) = d.f.(d.w * mat(x) .+ d.b)
# Define a chain of layers and a loss function:
struct Chain; layers; end
(c::Chain)(x) = (for l in c.layers; x = l(x); end; x)
(c::Chain)(x,y) = nll(c(x),y)
# Load MNIST data:
xtrn,ytrn = MNIST.traindata(Float32); ytrn[ytrn.==0] .= 10
xtst,ytst = MNIST.testdata(Float32); ytst[ytst.==0] .= 10
dtrn = minibatch(xtrn, ytrn, 100; xsExcerpt of 4,976 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e10d0d0c987668d8, topic:deep-learning, desc:deep learning framework, readme:deep learning framework