I have a computer running Microsoft Windows 7 with an Nvidia card. If I install Ubuntu with Wubi, can Theano use the GPU, and if so would it be just as efficiently as I was using Ubuntu without Wubi?
Wubi is just an easy way to install Ubuntu so it dual-boots with Windows. Once Ubuntu is installed neither Windows nor Ubuntu operate in any fundamentally different way compared to the same end result via an alternate installation method.
If you think that installing Ubuntu via Wubi will in some way change Windows so Theano on Windows can/must use the GPU differently/better then you're mistaken. You'll need to install Theano in each operating system you want to run it in. And the way in which Theano uses the GPU will be depend on each installation independent of any others. So, if you have a Windows/Ubuntu dual-boot system (whether that is achieved via Wubi or not), an installation of Theano in Windows will have no impact on an installation of Theano in Ubuntu, and vice versa.
Related
I got the message "The TensorFlow library wasn't compiled to use SSE instructions...etc"
According to some answers in StackOverflow, this type of message, is coming up when "old" computers are used. All hints and suggestions related to installing TensorFlow did not work for me. My PCs are ca. 8-10 years old.
My question: What minimum configuration of a NEW PC (e.g. Intel, NVIDIA with GPU graphic card) is supposed to be used in order to make TensorFlow installed/working? Is any newer PC and/or notebook appropriate, independently of the Manufacturer?
I use OS Ubuntu 20.04, eventually Windows in rare cases.
Thank you
Bruno
The minimum system and software requirement to work with tensorflow was
System requirements
Ubuntu 16.04 or higher (64-bit)
macOS 10.12.6 (Sierra) or higher (64-bit) (no GPU support)
Windows Native - Windows 7 or higher (64-bit)
Windows WSL2 - Windows 10 19044 or higher (64-bit)
Software requirements
Python 3.7–3.10
pip version 19.0 or higher for Linux (requires manylinux2010 support) and Windows. pip version 20.3 or higher for macOS.
For, more details please refer to this documentation. Thank You.
I want to run TensorFlow on my microserver. I'd like to install a non-systemd Linux if possible e.g. Alpine, but I am new to TensorFlow and I am not sure how much it relies on systemd or if it would run without it. Would it?
TensorFlow is a regular programming library, it's not a system library and not running as a service, thus it isn't dependent on systemd.
I've tested TensorFlow on Windows 10 Subsystem for Linux (WSL) which doesn't come with systemd and it's still working.
I am trying to run Nvidia rapids on a windows computer but haven't had any luck. I have installed docker desktop for windows and downloaded the rapids image. Cuda 10.0 is installed, and Nvidia-container-toolkit isn't. I haven't been able to make it run. Any thoughts or guidance?
I'm not sure if anyone has given a more definite 'updated' answer to the original question. At this point (August 2020) the answer is "Yes!". You definitely can run RAPIDS in WSL2 on Windows 10 subject to a few conditions:
Requirements
You must use RAPIDS in the Windows Subsystem for Linux version 2 (WSL2);
Windows 10 Version
2004 (OS Build 202001.1000 or later)
You have to sign up to get Windows Insider Preview versions, specifically the Developer Channel. This is required for the WSL2 VM to have GPU access. https://insider.windows.com/en-us/
CUDA version 455.41 in CUDA SDK v11.1
You must be using a special version of the NVIDA CUDA drivers (I'm using )
that you must get by a special download from NVIDIA's site. You must
join the NVIDIA Developer Program to get access to the version
-- then search for 'WSL2 CUDA Driver' and it should lead you to it.
Setup
Install the developer preview version of windows. Make sure to click the check box in 'update' that installs other recommended updates too.
Install the windows CUDA driver from the NVIDIA Developer Program
Enable WSL 2 by enabling the "Virtual Machine Platform" optional feature. You can find more steps here https://learn.microsoft.com/en-us/windows/wsl/install-win10
Install WSL from the Windows Store (Ubuntu-20.04 confirmed working)
Install python on the WSL VM, tested with Anaconda
Install Rapids AI (It's best to install this right now before you have hundreds of other packages for 'conda' to try to self-consistently reconcile with the rapids dependency graphs -- you can always install additional python packages via pip or conda later.)
After doing this, if you launch ipython...
Python 3.8.3 (default, May 19 2020, 18:47:26)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.17.0 -- An enhanced Interactive Python. Type '?' for help.
>>> import cuml
>>> cuml.__version__
'0.15.0'
>>> import cudf
>>> cudf.__version__
'0.15.0'
>>> import dask_cudf
>>> dask_cudf.__version__
'0.15.0'
>>> import cupy
>>> cupy.__version__
'7.8.0'
...and you're good to go with RAPIDS AI.
Update 9/6/20: The answer written by Wesley is accurate with the latest Windows Insider Preview with WSL2. Rather than revising this answer, I've just made the edits to his. https://stackoverflow.com/a/59364773/6779504
No. As it exists now, RAPIDS requires a Linux host. This came up in a recent workshop by NVIDIA. It was also mentioned that RAPIDS won't work with WSL. It may work with WSL version 2, but I haven't tried it nor am aware of someone that as.
The only option would if you could assign a GPU to a Linux VM on the Windows host. This possible but sufficiently complex that dual-booting is a better solution.
I have a Gforce 1080 Ti GPU and I installed visuall studio 2017 enterprise, 430.64-desktop-win10-64bit-international-whql, cuda_10.0.130_411.31_win10, cudnn-9.0-windows10-x64-v7.4.2.24 and Anaconda3-5.2.0-Windows-x86_64 respectively on my computer. after that, I make a virtual environment variable using Anaconda command prompt and install TensorFlow-GPU using this command: pip install --ignore-installed --upgrade tensorFlow-gpu==1.9 but my system using CPU instead of gpu.one time at first it used gpu and then during learning my network, it used CPU again. what is the problem? and what should I do to solve this problem and make force my system to use GPU? please help me. thank you.
According to https://www.tensorflow.org/install/source#tested_source_configurations
tensorflow_gpu-1.9.0 only supports CUDA 9.0, it might be the issue. I suggest you could try tensorflow_gpu-1.13.1
I'm want to run tensorflow on a very standard machine setup (windows 64 bit) and have read that tensorflow has greater performance if built from source as it is optimised for your system. When installing tensorflow via pip for why does pip not select the optimal build for your system?
Also if you did install via pip is there a way or being able to tell whether the optimal build has been installed, or is the only way of knowing that simply remembering how you installed it?
Google has taken the position, that it is not reasonable to build TF for every possible instruction set out there. They only release generic builds for Linux, Mac and Windows. You must build from source if you want all the optimizations for your particular machine's instruction set.