Good news!
I had wished there's would be some integration in several CUDA packages.
By the way, is there's any plan for 'standard' GPU array type, such
as https://github.com/JuliaGPU/GPUArrays.jl ?
CUDArt, CUDAdrv has its own CUDA array type and there's package such as
ArrayFire.jl
For
Thank you for great work!
I have to print it out and place on my desk.
By the way, PDF file has title 'Python 2.5 Reference Card'.
(not file name, my acrobat reader shows that title on the window title.
maybe that is in the PDF file metadata?)
Wonderful jobs, Jonathan!
I'd better try this version rather than use TensorFlow in python.
Does it based on PyCall package?
-Kyunghun
2016년 9월 1일 목요일 오전 7시 31분 58초 UTC+9, Jonathan Malmaud 님의 말:
>
> Hello,
> I'm pleased to announce the release of TensorFlow.jl, enabling modern
>
questions, if you aren't using the tools advertised at
http://docs.julialang.org/en/release-0.3/manual/performance-tips/
you will likely find them to be a big help.
--Tim
On Wednesday, February 11, 2015 07:25:38 AM Kyunghun Kim wrote:
Hi, all.
I am sorry that I am writing repeating
Hi, all.
I am sorry that I am writing repeating these questions again. (performance
compared to ~)
I have some signal processing code written in MATLAB, and rewriting the
code with Julia.
The signal processing function take about 1024 x 1024 floating number array
as input called in loop
You can run 32 bit applications in 64 bit OS, but you have to use only 64
bit DLL/libraries in 64 bit applications.
It is not available because they use different size of memory address.
2015년 2월 5일 목요일 오후 2시 46분 22초 UTC+9, 进陆 님의 말:
I am using Julia Version 0.3.4 (2014-12-26 10:42 UTC)(
