juliacrypto / toyfhe.jl Goto Github PK
View Code? Open in Web Editor NEWToy implementation of FHE algorithms
License: Other
Toy implementation of FHE algorithms
License: Other
I don't like the explicit (i)nntt
calls in all the various places. A better design would be to have a single ring element struct that keeps both representations as Union{Nothing, T}
and produced the appropriate one on demand as required.
Since the Manifest.toml
file is checked in to source control, perhaps we could document somewhere which version of Julia was used to generate the manifest file?
I think the issue is that the manifest uses an old version of CuArrays that does not work on Julia master.
cc: @Keno
julia> cd("ToyFHE.jl/examples/encrypted_mnist")
julia rm("mnist_conv.bson"; force = true)
julia> include("train.jl")
[ Info: Precompiling CuArrays [3a865a2d-5b23-5a0f-bc46-62713ec82fae]
┌ Warning: Assignment to `name` in soft scope is ambiguous because a global variable by the same name exists: `name` will be treated as a new local. Disambiguate by using `local name` to suppress this warning or `global name` to assign to the existing global variable.
└ @ ~/.julia/packages/CUDAdrv/WqkY2/src/error.jl:83
┌ Warning: `@get!(dict, key, default)` at /users/daluthge/.julia/packages/Requires/9Jse8/src/require.jl:11 is deprecated, use `get!(()->default, dict, key)` instead.
│ caller = include(::Function, ::Module, ::String) at Base.jl:380
└ @ Base ./Base.jl:380
ERROR: LoadError: LoadError: syntax: invalid keyword argument syntax "(escape (= threads nthreads))"
Stacktrace:
[1] top-level scope at /users/daluthge/.julia/packages/CuArrays/dY5ry/src/array.jl:318
[2] include(::Function, ::Module, ::String) at ./Base.jl:380
[3] include at ./Base.jl:368 [inlined]
[4] include(::String) at /users/daluthge/.julia/packages/CuArrays/dY5ry/src/CuArrays.jl:1
[5] top-level scope at /users/daluthge/.julia/packages/CuArrays/dY5ry/src/CuArrays.jl:22
[6] include(::Function, ::Module, ::String) at ./Base.jl:380
[7] include(::Module, ::String) at ./Base.jl:368
[8] top-level scope at none:2
[9] eval at ./boot.jl:331 [inlined]
[10] eval(::Expr) at ./client.jl:451
[11] top-level scope at ./none:3
in expression starting at /users/daluthge/.julia/packages/CuArrays/dY5ry/src/array.jl:318
in expression starting at /users/daluthge/.julia/packages/CuArrays/dY5ry/src/CuArrays.jl:22
ERROR: LoadError: Failed to precompile CuArrays [3a865a2d-5b23-5a0f-bc46-62713ec82fae] to /users/daluthge/.julia/compiled/v1.5/CuArrays/7YFE0_YKowg.ji.
Stacktrace:
[1] error(::String) at ./error.jl:33
[2] compilecache(::Base.PkgId, ::String) at ./loading.jl:1288
[3] _require(::Base.PkgId) at ./loading.jl:1029
[4] require(::Base.PkgId) at ./loading.jl:927
[5] require(::Module, ::Symbol) at ./loading.jl:922
[6] include(::String) at ./client.jl:441
[7] top-level scope at REPL[10]:1
in expression starting at /gpfs_home/daluthge/Desktop/ToyFHE.jl/examples/encrypted_mnist/train.jl:9
julia> versioninfo(verbose = true)
Julia Version 1.5.0-DEV.274
Commit 8eb0f9fefb (2020-02-15 12:41 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
"Red Hat Enterprise Linux Server release 7.3 (Maipo)"
uname: Linux 3.10.0-957.5.1.el7.x86_64 #1 SMP Wed Dec 19 10:46:58 EST 2018 x86_64 x86_64
CPU: Intel(R) Xeon(R) Gold 5122 CPU @ 3.60GHz:
speed user nice sys idle irq
#1 3601 MHz 302347066 s 170 s 53586390 s 337540460 s 0 s
#2 3601 MHz 235401396 s 383 s 47128775 s 410325724 s 0 s
#3 3601 MHz 103237285 s 288 s 30844130 s 559882062 s 0 s
#4 3601 MHz 63412689 s 364 s 22978592 s 607626432 s 0 s
#5 3601 MHz 315443735 s 113 s 49256804 s 329997494 s 0 s
#6 3601 MHz 155708333 s 578 s 37188177 s 501770058 s 0 s
#7 3601 MHz 54762847 s 546 s 15693504 s 624338616 s 0 s
#8 3601 MHz 46859843 s 550 s 14616044 s 632741590 s 0 s
Memory: 93.04103088378906 GB (74548.9140625 MB free)
Uptime: 6.958699e6 sec
Load Avg: 1.20654296875 1.44921875 1.22314453125
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake)
Environment:
CPLUS_INCLUDE_PATH = /gpfs/runtime/opt/gcc/8.3/include
MANPATH = /gpfs/runtime/opt/python/3.7.4/share/man:/gpfs/runtime/opt/git/2.20.2/share/man:/gpfs/runtime/opt/gcc/8.3/share/man:/gpfs/runtime/opt/binutils/2.31/share/man:/gpfs/runtime/opt/intel/2017.0/man/common/man1:
TERM = xterm-256color
LIBRARY_PATH = /gpfs/runtime/opt/cuda/10.2/cuda/lib64:/gpfs/runtime/opt/cuda/10.2/cuda/lib:/gpfs/runtime/opt/cudnn/7.6.5/lib64:/gpfs/runtime/opt/python/3.7.4/lib:/gpfs/runtime/opt/binutils/2.31/lib:/gpfs/runtime/opt/intel/2017.0/lib/intel64:/gpfs/runtime/opt/intel/2017.0/mkl/lib/intel64
INCLUDE_PATH = /gpfs/runtime/opt/cudnn/7.6.5/include
CUDA_HOME = /gpfs/runtime/opt/cuda/10.2/cuda
LD_LIBRARY_PATH = /gpfs/runtime/opt/cuda/10.2/src/lib64:/gpfs/runtime/opt/cuda/10.2/src/lib:/gpfs/runtime/opt/cudnn/7.6.5/lib64:/gpfs/runtime/opt/python/3.7.4/lib:/gpfs/runtime/opt/gcc/8.3/lib64:/gpfs/runtime/opt/binutils/2.31/lib:/gpfs/runtime/opt/intel/2017.0/lib/intel64:/gpfs/runtime/opt/intel/2017.0/mkl/lib/intel64:/gpfs/runtime/opt/java/8u111/jre/lib/amd64
CPATH = /gpfs/runtime/opt/cuda/10.2/cuda/include:/gpfs/runtime/opt/cudnn/7.6.5/include:/gpfs/runtime/opt/python/3.7.4/include:/gpfs/runtime/opt/gcc/8.3/include:/gpfs/runtime/opt/binutils/2.31/include:/gpfs/runtime/opt/intel/2017.0/mkl/include
NLSPATH = /gpfs/runtime/opt/intel/2017.0/lib/intel64/locale/en_US:/gpfs/runtime/opt/intel/2017.0/mkl/lib/intel64/locale/en_US
PATH = /gpfs/runtime/opt/cuda/10.2/cuda/bin:/users/daluthge/bin:/gpfs/runtime/opt/python/3.7.4/bin:/gpfs/runtime/opt/git/2.20.2/bin:/gpfs/runtime/opt/gcc/8.3/bin:/gpfs/runtime/opt/binutils/2.31/bin:/gpfs/runtime/opt/intel/2017.0/bin:/gpfs/runtime/opt/matlab/R2017b/bin:/gpfs/runtime/opt/java/8u111/bin:/usr/lib64/qt-3.3/bin:/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/usr/lpp/mmfs/bin:/usr/lpp/mmfs/sbin:/opt/ibutils/bin:/gpfs/runtime/bin
C_INCLUDE_PATH = /gpfs/runtime/opt/cudnn/7.6.5/include:/gpfs/runtime/opt/gcc/8.3/include
LD_RUN_PATH = /gpfs/runtime/opt/cuda/10.2/cuda/lib64:/gpfs/runtime/opt/cuda/10.2/cuda/lib:/gpfs/runtime/opt/cudnn/7.6.5/lib64:/gpfs/runtime/opt/cudnn/7.6.5/lib:/gpfs/runtime/opt/python/3.7.4/lib:/gpfs/runtime/opt/gcc/8.3/lib64:/gpfs/runtime/opt/binutils/2.31/lib:/gpfs/runtime/opt/intel/2017.0/lib/intel64:/gpfs/runtime/opt/intel/2017.0/mkl/lib/intel64
JAVA_HOME = /gpfs/runtime/opt/java/8u111
MODULEPATH = /gpfs/runtime/modulefiles
HOME = /users/daluthge
IPP_PATH = /gpfs/runtime/opt/intel/2017.0/ipp
MODULEHOME = /gpfs/runtime/pymodules
PKG_CONFIG_PATH = /gpfs/runtime/opt/python/3.7.4/lib/pkgconfig
QT_PLUGIN_PATH = /usr/lib64/kde4/plugins:/usr/lib/kde4/plugins
(ToyFHE) pkg> st
Project ToyFHE v0.1.0
Status `/gpfs_home/daluthge/Desktop/ToyFHE.jl/Project.toml`
[c3fe647b] AbstractAlgebra v0.7.1 #master (https://github.com/Nemocas/AbstractAlgebra.jl.git)
[15f4f7f2] AutoHashEquals v0.2.0
[fbb218c0] BSON v0.2.4
[c3b6d118] BitIntegers v0.2.0 #kf/div (https://github.com/Keno/BitIntegers.jl)
[3a865a2d] CuArrays v1.3.0 #master (https://github.com/JuliaGPU/CuArrays.jl.git)
[ab62b9b5] DeepDiffs v1.1.0
[31c24e10] Distributions v0.21.1 #kf/discretenormal (https://github.com/Keno/Distributions.jl)
[7a1cc6ca] FFTW v1.0.1
[587475ba] Flux v0.9.0
[65ccaadd] FourierTransforms v0.0.0 #master (https://github.com/JuliaComputing/FourierTransforms.jl)
[0c68f7d7] GPUArrays v1.0.4 #master (https://github.com/JuliaGPU/GPUArrays.jl.git)
[8d0d7f98] GaloisFields v0.4.0 #master (https://github.com/tkluck/GaloisFields.jl.git)
[3e1990a7] Hecke v0.6.5 #kf/discard2 (https://github.com/Keno/Hecke.jl)
[7869d1d1] IRTools v0.3.0 #master (https://github.com/MikeInnes/IRTools.jl.git)
[aa1ae85d] JuliaInterpreter v0.7.4
[7475f97c] Mods v0.1.0
[2edaba10] Nemo v0.15.1 #master (https://github.com/Nemocas/Nemo.jl.git)
[6fe1bfb0] OffsetArrays v0.11.1
[f27b6e38] Polynomials v0.5.3
[27ebfcd6] Primes v0.4.0+ #master (https://github.com/JuliaMath/Primes.jl.git)
[09ab397b] StructArrays v0.4.0 #kf/fhe (https://github.com/Keno/StructArrays.jl)
[700de1a5] ZygoteRules v0.2.0 #master (https://github.com/FluxML/ZygoteRules.jl.git)
[37e2e46d] LinearAlgebra
[9a3f8284] Random
(ToyFHE) pkg> st -m
Project ToyFHE v0.1.0
Status `/gpfs_home/daluthge/Desktop/ToyFHE.jl/Manifest.toml`
[c3fe647b] AbstractAlgebra v0.7.1 #master (https://github.com/Nemocas/AbstractAlgebra.jl.git)
[621f4979] AbstractFFTs v0.4.1
[1520ce14] AbstractTrees v0.2.1
[79e6a3ab] Adapt v1.0.0
[7d9fca2a] Arpack v0.3.1
[15f4f7f2] AutoHashEquals v0.2.0
[fbb218c0] BSON v0.2.4
[9e28174c] BinDeps v0.8.10
[b99e7846] BinaryProvider v0.5.8
[c3b6d118] BitIntegers v0.2.0 #kf/div (https://github.com/Keno/BitIntegers.jl)
[fa961155] CEnum v0.2.0
[3895d2a7] CUDAapi v1.2.0
[c5f51814] CUDAdrv v4.0.3
[be33ccc6] CUDAnative v2.5.4
[da1fd8a2] CodeTracking v0.5.8
[944b1d66] CodecZlib v0.6.0
[3da002f7] ColorTypes v0.8.0
[5ae59095] Colors v0.9.6
[bbf7d656] CommonSubexpressions v0.2.0
[34da2185] Compat v2.2.0
[8f4d0f93] Conda v1.3.0
[3a865a2d] CuArrays v1.3.0 #master (https://github.com/JuliaGPU/CuArrays.jl.git)
[9a962f9c] DataAPI v1.1.0
[864edb3b] DataStructures v0.17.5
[ab62b9b5] DeepDiffs v1.1.0
[163ba53b] DiffResults v0.0.4
[b552c78f] DiffRules v0.0.10
[31c24e10] Distributions v0.21.1 #kf/discretenormal (https://github.com/Keno/Distributions.jl)
[7a1cc6ca] FFTW v1.0.1
[1a297f60] FillArrays v0.7.4
[53c48c17] FixedPointNumbers v0.6.1
[587475ba] Flux v0.9.0
[f6369f11] ForwardDiff v0.10.6
[65ccaadd] FourierTransforms v0.0.0 #master (https://github.com/JuliaComputing/FourierTransforms.jl)
[0c68f7d7] GPUArrays v1.0.4 #master (https://github.com/JuliaGPU/GPUArrays.jl.git)
[8d0d7f98] GaloisFields v0.4.0 #master (https://github.com/tkluck/GaloisFields.jl.git)
[cd3eb016] HTTP v0.8.8
[3e1990a7] Hecke v0.6.5 #kf/discard2 (https://github.com/Keno/Hecke.jl)
[7869d1d1] IRTools v0.3.0 #master (https://github.com/MikeInnes/IRTools.jl.git)
[83e8ac13] IniFile v0.5.0
[682c06a0] JSON v0.21.0
[aa1ae85d] JuliaInterpreter v0.7.4
[e5e0dc1b] Juno v0.7.2
[929cbde3] LLVM v1.3.2
[1914dd2f] MacroTools v0.5.2
[739be429] MbedTLS v0.7.0
[e89f7d12] Media v0.5.0
[e1d29d7a] Missings v0.4.3
[7475f97c] Mods v0.1.0
[872c559c] NNlib v0.6.0
[77ba4419] NaNMath v0.3.2
[2edaba10] Nemo v0.15.1 #master (https://github.com/Nemocas/Nemo.jl.git)
[6fe1bfb0] OffsetArrays v0.11.1
[bac558e1] OrderedCollections v1.1.0
[90014a1f] PDMats v0.9.10
[69de0a69] Parsers v0.3.8
[f27b6e38] Polynomials v0.5.3
[2dfb63ee] PooledArrays v0.5.2
[27ebfcd6] Primes v0.4.0+ #master (https://github.com/JuliaMath/Primes.jl.git)
[1fd47b50] QuadGK v2.1.1
[3cdcf5f2] RecipesBase v0.7.0
[189a3867] Reexport v0.2.0
[ae029012] Requires v0.5.2
[79098fc4] Rmath v0.6.0
[a2af1166] SortingAlgorithms v0.3.1
[276daf66] SpecialFunctions v0.7.2
[90137ffa] StaticArrays v0.12.1
[2913bbd2] StatsBase v0.32.0
[4c63d2b9] StatsFuns v0.8.0
[09ab397b] StructArrays v0.4.0 #kf/fhe (https://github.com/Keno/StructArrays.jl)
[a759f4b9] TimerOutputs v0.5.3
[9f7883ad] Tracker v0.2.5
[3bb67fe8] TranscodingStreams v0.9.5
[30578b45] URIParser v0.4.0
[81def892] VersionParsing v1.1.3
[a5390f91] ZipFile v0.8.3
[700de1a5] ZygoteRules v0.2.0 #master (https://github.com/FluxML/ZygoteRules.jl.git)
[2a0f44e3] Base64
[ade2ca70] Dates
[8bb1440f] DelimitedFiles
[8ba89e20] Distributed
[b77e0a4c] InteractiveUtils
[76f85450] LibGit2
[8f399da3] Libdl
[37e2e46d] LinearAlgebra
[56ddb016] Logging
[d6f4376e] Markdown
[a63ad114] Mmap
[44cfe95a] Pkg
[de0858da] Printf
[9abbd945] Profile
[3fa0cd96] REPL
[9a3f8284] Random
[ea8e919c] SHA
[9e88b42a] Serialization
[1a1011a3] SharedArrays
[6462fe0b] Sockets
[2f01184e] SparseArrays
[10745b16] Statistics
[4607b0f0] SuiteSparse
[8dfed614] Test
[cf7118a7] UUIDs
[4ec0a83e] Unicode
While evaluating using ToyFHE
, I see the following:
[ Info: Precompiling ToyFHE [ed6b25c6-c39d-11e9-27fc-075f0335bf0b]
Welcome to Nemo version 0.15.1
Nemo comes with absolutely no warranty whatsoever
Welcome to
_ _ _
| | | | | |
| |__| | ___ ___| | _____
| __ |/ _ \/ __| |/ / _ \
| | | | __/ (__| < __/
|_| |_|\___|\___|_|\_\___|
Version 0.6.7 ...
... which comes with absolutely no warranty whatsoever
(c) 2015-2019 by Claus Fieker, Tommy Hofmann and Carlo Sircana
WARNING: using Hecke.modulus in module Utils conflicts with an existing identifier.
WARNING: using Hecke.degree in module Utils conflicts with an existing identifier.
ERROR: LoadError: LoadError: UndefVarError: FmpzModRing not defined
Stacktrace:
[1] getproperty(::Module, ::Symbol) at .\Base.jl:13
[2] top-level scope at C:\Users\marco\.julia\packages\ToyFHE\EUUTq\src\utils.jl:33
[3] include at .\boot.jl:328 [inlined]
[4] include_relative(::Module, ::String) at .\loading.jl:1105
[5] include at .\Base.jl:31 [inlined]
[6] include(::String) at C:\Users\marco\.julia\packages\ToyFHE\EUUTq\src\ToyFHE.jl:1
[7] top-level scope at C:\Users\marco\.julia\packages\ToyFHE\EUUTq\src\ToyFHE.jl:25
[8] include at .\boot.jl:328 [inlined]
[9] include_relative(::Module, ::String) at .\loading.jl:1105
[10] include(::Module, ::String) at .\Base.jl:31
[11] top-level scope at none:2
[12] eval at .\boot.jl:330 [inlined]
[13] eval(::Expr) at .\client.jl:425
[14] top-level scope at .\none:3
in expression starting at C:\Users\marco\.julia\packages\ToyFHE\EUUTq\src\utils.jl:33
in expression starting at C:\Users\marco\.julia\packages\ToyFHE\EUUTq\src\ToyFHE.jl:25
ERROR: Failed to precompile ToyFHE [ed6b25c6-c39d-11e9-27fc-075f0335bf0b] to C:\Users\marco\.julia\compiled\v1.3\ToyFHE\EfSLj_Hqkgq.ji.
Stacktrace:
[1] error(::String) at .\error.jl:33
[2] compilecache(::Base.PkgId, ::String) at .\loading.jl:1283
[3] _require(::Base.PkgId) at .\loading.jl:1024
[4] require(::Base.PkgId) at .\loading.jl:922
[5] require(::Module, ::Symbol) at .\loading.jl:917
I've tried to run the introductory examples on https://juliacomputing.com/blog/2019/11/22/encrypted-machine-learning.html with Julia 1.5.0-beta1. When I reached the circshift
test, Julia threw the following error:
julia> decrypt(circshift(c, gk))
ERROR: MethodError: no method matching decrypt(::CipherText{CKKSEncoding{FixedRational{1099511627776,T} where T},CKKSParams,ToyFHE.NTT.RingElement{ℤ₁₃₂₉₂₂₇₉₉₇₅₆₈₀₈₁₄₅₇₄₀₂₇₀₁₂₀₇₁₀₄₂₄₈₂₅₇/(x¹⁶ + 1),ToyFHE.CRTEncoded{3,Tuple{𝔽₁₀₉₉₅₁₁₆₂₇₈₇₃,𝔽₁₀₉₉₅₁₁₆₂₈₁₆₁,𝔽₁₀₉₉₅₁₁₆₂₈₇₆₉}},StructArrays.StructArray{ToyFHE.CRTEncoded{3,Tuple{𝔽₁₀₉₉₅₁₁₆₂₇₈₇₃,𝔽₁₀₉₉₅₁₁₆₂₈₁₆₁,𝔽₁₀₉₉₅₁₁₆₂₈₇₆₉}},1,Tuple{Array{𝔽₁₀₉₉₅₁₁₆₂₇₈₇₃,1},Array{𝔽₁₀₉₉₅₁₁₆₂₈₁₆₁,1},Array{𝔽₁₀₉₉₅₁₁₆₂₈₇₆₉,1}},Int64}},2})
Closest candidates are:
decrypt(::PrivKey, ::CipherText{T,P,T1,N} where N where T1 where P<:ToyFHE.SHEShemeParams) where T at /home/mschlott/gdrive/work/code/CryptoSim.jl/ToyFHE.jl/src/rlwe_she.jl:199
decrypt(::KeyPair, ::Any) at /home/whatever/ToyFHE.jl/src/rlwe_she.jl:217
Stacktrace:
[1] top-level scope at REPL[21]:1
Here's an MWE:
julia> using ToyFHE
julia> N = 8;
julia> ℛ = NegacyclicRing(2N, (40, 40, 40))
ℤ₁₃₂₉₂₂₇₉₉₇₅₆₈₀₈₁₄₅₇₄₀₂₇₀₁₂₀₇₁₀₄₂₄₈₂₅₇/(x¹⁶ + 1)
julia> params = CKKSParams(ℛ)
CKKS parameters
julia> Tscale = FixedRational{2^40}
FixedRational{1099511627776,T} where T
julia> plain = CKKSEncoding{Tscale}(zero(ℛ))
8-element CKKSEncoding{FixedRational{1099511627776,T} where T} with indices 0:7:
0.0 + 0.0im
0.0 + 0.0im
0.0 + 0.0im
0.0 + 0.0im
0.0 + 0.0im
0.0 + 0.0im
0.0 + 0.0im
0.0 + 0.0im
julia> kp = keygen(params)
CKKS key pair
julia> kp.priv
CKKS private key
julia> kp.pub
CKKS public key
julia> foreach(i->plain[i] = i+1, 0:7); plain
8-element CKKSEncoding{FixedRational{1099511627776,T} where T} with indices 0:7:
1.0 + 0.0im
2.0 + 0.0im
3.0 + 0.0im
4.0 + 0.0im
5.0 + 0.0im
6.0 + 0.0im
7.0 + 0.0im
8.0 + 0.0im
julia> c = encrypt(kp.pub, plain)
CKKS ciphertext (length 2, encoding CKKSEncoding{FixedRational{1099511627776,T} where T})
julia> gk = keygen(GaloisKey, kp.priv; steps=2)
CKKS galois key (element 25)
julia> decrypt(circshift(c, gk))
Any idea what the problem could be?
The tests take quite a while to run. For some of the tests I wrote early one, the parameters are from real world instantiations of the schemes. This is unnecessary. We should be able to just cut down the ring dimension and make the tests much faster.
I know that this will take some time, because currently the package relies on Keno's forked versions of several packages.
But it would be great to eventually get ToyFHE.jl
registered in the General Registry.
I'm just putting this here since it's semi-relevant, but also because Slack is ephemeral.
julia> import Pkg
julia> function load_manifest(; kwargs...)
pkg = Pkg.PackageSpec(; kwargs...)
return load_manifest(pkg)
end
julia> function load_manifest(pkg::Pkg.Types.PackageSpec, shared::Bool = true)
ctx = Pkg.Types.Context()
return load_manifest(ctx, pkg, shared)
end
julia> function load_manifest(ctx::Pkg.Types.Context, pkg::Pkg.Types.PackageSpec, shared::Bool = true)
Pkg.Types.handle_repo_develop!(ctx, pkg, shared)
devpath = Pkg.Types.devpath(ctx, pkg.name, shared)
Pkg.activate(devpath)
Pkg.instantiate()
return devpath
end
julia> load_manifest(url = "https://github.com/JuliaComputing/ToyFHE.jl")
julia> using ToyFHE
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The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.