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.
Related
there is a post How can I run Mozilla TTS/Coqui TTS training with CUDA on a Windows system? answered, by GuyPaddock, but I have RTX a5000 graphic card, running Windows 10. I'm not a programmer, but I think it needs CUDA version 11.x for this card. Will there be someone good who would write step by step what I should install to be able to run it and train models? (kidna RETARD guide) It's best not to mess with the webUI from AUTOMATIC1111, which requires python 3.10.6. Thanks in advance.
Trying to install it from the link above and also from youtube. I am trying to install this on python 3.10.8 because stable diffusion needs python 3.10.6, And version 3.10.8 is from October like CUDA 11.8. If possible, I'd like a step by step explanation of what I need to do to make it work?
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.
It is now Oct 29, 2018
After much googling, I have not found a definitive answer or any examples of people using the latest cuda10 for tensorflow on ubuntu 14.04.
My dilemma is whether to upgrade my OS (currently at 14.04) in order to run cuda9 so I can use the latest tensorflow version or use CUDA10 on my existing 14.04 install.
Note cuda9 does not support 14.04, however, Nvidia has indicated that 14.04 will be supported for cuda10.
So, any examples/experiences of people using tensorflow with cuda10 on ubuntu14.04 are keenly sought after!
Also note cuda10 is not specifically supported by tensorflow...yet...they say "soon". But TF can be built from source with cuda10.
This is a link for cuda10+tensorflow on ubuntu16.04:
https://github.com/tensorflow/tensorflow/issues/22706
The short answer, I realize, is "try building it myself". Before I do that, I thought I'd ask around. Thanks.
I don't know whether CUDA 10 can work well on Ubuntu 14.04, but I was managed to build TensorFlow with CUDA 10 on Ubuntu 18.04 with using NVIDIA released docker image.
You can pull the 'TensorFlow Release 18.09' and try it on your current system.
If the previous step does not work, consider upgrading your OS to 18.04.
I wrote down my installation experience on this page, you could read it for some detail if you need.
I'm running a Windows computer with just a CPU (no GPU). When I run pip install tensorflow -vvv in order to see what pip is doing, it lists a lot of links, but for all of them, it says "Skipping link ... it is not compatible with this Python."
Does tensorflow support Python 3.6.4 on Windows? If so, what binary URL should I use to install it?
(I previously installed with this version due to reading this, but ran into this error without the DLL load failed message, so I'm wondering if there's a better version I should use.)
Also, I'm aware that Tensorflow says they support Python 3.x, but right now it hasn't been working for me.
You have probably installed Python 32bits, you need the 64bits version
Upon trying to install Tensorflow for conda environment, I encountered with the following error message, without any progress:
tensorflow-1.1.0-cp35-cp35mwin_amd64.whl is not a supported wheel on this platform
Have you tried uninstalling and re-installing TensorFlow using pip within your Conda environment? I.e.:
pip uninstall tensorflow
Followed by:
pip install tensorflow
If it doesn't work, the issue may be with your Python installation. TensorFlow only supports 64-bit Python 3.5+ on Windows (see more info here).
Perhaps you have Python's default installation, which comes in a 32-bit version. If that's the case, you can download the 64-bit Python 3.5 or later from here to run in your Conda environment and then you should be able to install/run TensorFlow without any issues.
Make sure that the Python version installed in the Environment is 3.5 not 3.6. Since 3.6 was released Conda automatically sets that version as default for python 3. However, it is still not supported by Tensorflow.
You can work using tensorflow library along with other essential libraries using the Dockerfile. Using Docker for environment are a good way to run experiments in reproducible manner as in this blog
You can also try using datmo in order setup environment and track machine learning projects for making it reproducible using datmo CLI tool.