appleparan

import pandas as pd
from datasets import load_dataset_builder, load_dataset, Dataset

def main(dataset_name1 = 'kyujinpy/OpenOrca-KO', dataset_name2 = 'Open-Orca/OpenOrca', split='train'):
    # Load the dataset builder
    builder1 = load_dataset_builder(dataset_name1)
    builder2 = load_dataset_builder(dataset_name2)

    # kyujinpy/OpenOrca-KO
    # id, input, instruction, output

appleparan

https://www.facebook.com/groups/juliakorea/permalink/2757780527680241/ 답변

와 좋은 질문이네요. 덕분에 저도 원론적인 부분에서 다시 이해를 다잡는 좋은 기회가 되었습니다.

• 기본적으로 Julia의 Argument Passing 은 "pass-by-sharing"입니다. 이 점을 염두에 두셔야 합니다.

그럼 BenchmarkTools.jl의 @btime과 InteractiveUtils의 @code_lowered를 이용해서 각자의 코드가 내부적으로 어떻게 동작하는 지 알아봅시다.

1. test1

appleparan

Keybase proof

I hereby claim:

• I am appleparan on github.
• I am appleparan (https://keybase.io/appleparan) on keybase.
• I have a public key ASC72HifOLmY_OF7OZd3w4FUxpjbiS6zQvQQ7_enOcMjZgo

To claim this, I am signing this object:

appleparan

#include <stdio.h>
#include <complex.h>
/*
Compile option : icc -std=c99 main.c -o main
*/
int main() {
    double complex a;

    a = 1.0 + I * 2.0;

appleparan

using DataFrames

s = DataFrame([[true], [true], [false], [false], [false], [false], [false], [false], [false]],
[:MBF, :SBF, :VOX1, :HCM1, :YHP1, :SFF, :ACE2, :SWI5, :CLN3])

for step in 1:30
    last = s[end, :]
    new = proceed(last)
    push!(s, new)
end

appleparan

#= /home/appleparan/src/DebugFlux/test_curnn.jl:12 =# Zygote.@code_adjoint(gradient((m->begin
                #= /home/appleparan/src/DebugFlux/test_curnn.jl:12 =#
                sum(m(x))
            end), m)) = Zygote.Adjoint(1: (%4, %5 :: Zygote.Context, %1, %2, %3)
  %6 = (ZygoteRules._pullback)(%5, tuple, %2)
  %7 = (getindex)(%6, 1)
  %8 = (getindex)(%6, 2)                                                                                                                                                                                                                                         %9 = (ZygoteRules._pullback)(%5, Core._apply, ZygoteRules.pullback, %7, %3)
  %10 = (getindex)(%9, 1)                                                                                                                                                                                                                                        %11 = (getindex)(%9, 2)
  %12 = (ZygoteRules._pullback)(%5, Base.indexed_iterate, %10, 1)
  

appleparan

# This file is machine-generated - editing it directly is not advised

[[AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "380e36c66edfa099cd90116b24c1ce8cafccac40"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "0.4.1"

[[AbstractTrees]]
deps = ["Markdown", "Test"]

appleparan

• KNL (Xeon Phi) only

FFTW compilation option

• Specific compiler option

$ CC=icc CFLAGS="-fast -fPIC -traceback -xCOMMON-AVX512" ./configure --prefix=$HOME/usr/local/fftw_mic/ --enable-avx512 --enable-openmp
$ make && make install

appleparan

┌ Debug: Initializing CUDA after call to cuMemAlloc
└ @ CUDAnative ~/.julia/packages/CUDAnative/wU0tS/src/init.jl:32
┌ Debug: (Re)compiling function
│   job = CUDAnative.CompilerJob(getfield(GPUArrays, Symbol("##23#24"))(), Tuple{CuArrays.CuKernelState,CuDeviceArray{Float32,1,CUDAnative.AS.Global},Base.Broadcast.Broadcasted{Nothing,Tuple{Base.OneTo{Int64}},typeof(CUDAnative.abs),Tuple{Base.Broadcast.Extruded{CuDeviceArray{Float32,1,CUDAnative.AS.Global},Tuple{Bool},Tuple{Int64}}}}}, v"6.1.0", true, nothing, nothing, nothing, nothing)
└ @ CUDAnative ~/.julia/packages/CUDAnative/wU0tS/src/compiler/driver.jl:36
┌ Debug: Compiled getfield(GPUArrays, Symbol("##23#24"))() to PTX 6.1.0 for SM 6.1.0 using 11 registers.
│ Memory usage: 0 bytes local, 0 bytes shared, 256 bytes constant
└ @ CUDAnative ~/.julia/packages/CUDAnative/wU0tS/src/execution.jl:380
┌ Debug: (Re)compiling function
│   job = CUDAnative.CompilerJob(getfield(GPUArrays, Symbol("##23#24"))(), Tuple{CuArrays.CuKernelState,CuDeviceArray{Float32,1,CUDAna

appleparan

# CentOS-Base.repo
#
# The mirror system uses the connecting IP address of the client and the
# update status of each mirror to pick mirrors that are updated to and
# geographically close to the client.  You should use this for CentOS updates
# unless you are manually picking other mirrors.
#
# If the mirrorlist= does not work for you, as a fall back you can try the
# remarked out baseurl= line instead.
#