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Anaconda Python, CUDA, cuDNN and Theano with PyCharm Install for CPU and GPU on Windows 10 #5348

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mustgoplay opened this issue Dec 19, 2016 · 35 comments
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Anaconda Python, CUDA, cuDNN and Theano with PyCharm Install for CPU and GPU on Windows 10 #5348

mustgoplay opened this issue Dec 19, 2016 · 35 comments
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@mustgoplay
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mustgoplay commented Dec 19, 2016 •
edited

This installation has been a nightmare! So I took copious notes and attempted to minimize the installs needed. I am a complete newbie python programmer and so I figured if I could get this to work maybe it'll help other people and maybe they'll include this in the documentation.

References:

Theano Documentation

Zero to Lasagne

Neural-Style-Transfer Git

Conda Managing Environments

GPU-accelerated Deep Learning on Windows 10 native Git

Tutorial: Theano install on Windows 7, 8, 10

Making Theano Faster with CuDNN and CNMeM on Windows 10

  1. Download and install Anaconda. DO NOT install in the default directory for now. The .theanorc file WILL NOT WORK IF THERE ARE SPACES in the directory names pointing to the library. Future versions (after theano 0.8.2) may support spaces. Using python 3.4 allows use of nolearn (lasagne) and pydot-ng (keras). I downloaded the 64-bit installer.:

    https://www.continuum.io/downloads

    Optional - (I did not try this) Downgrade to 3.4:
    conda install python==3.4.4
    conda update --all

Note: It is important to stay consistent with your 32 vs 64 bit install throughout the process or it may lead to errors.

  1. Create new environment for your project. The python parameter sets the python version and the "anaconda" after the environment name means the new environment includes all the core packages. Open the "Anaconda Prompt" and enter the command below corresponding to the version you want:
	conda create -n env_name35 anaconda python=3.5
	conda create -n env_name34 anaconda python=3.4
  1. Change to the environment of choice by closing the prompt and opening the corresponding prompt, e.g. "Anaconda Prompt (env_name35)". Import theano with the following command:

conda install theano

  1. Download check_blas.py:

    https://raw.githubusercontent.com/Theano/Theano/master/theano/misc/check_blas.py

Run it by changing to the directory you saved it into within the command prompt for your new environment and then typing:

python check_blas.py

It will say:

Total execution time: 25.74s on CPU (without direct Theano binding to blas but with numpy/scipy binding to blas).

  1. In order to bind theano to BLAS, create a file called .theanorc and place it in the C:\Users\ [Your_User] directory and have it say the following:
	[global]
	floatx = float32

	[blas]
	ldflags = -LC:\Anaconda3\Library\bin -lmkl_rt

This will bind theano to the mklBLAS library that was included in Anaconda that copied into your environment in step 2. mkl_rt.dll is the library and the directory is the bin directory. Other install instructions often mention OpenBLAS but the mklBLAS is already included and is about 30% faster. Without binding to BLAS you can't use certain layers e.g. convolutional layers and you can get errors like:

AssertionError: AbstractConv2d Theano optimization failed: there is no implementation available supporting the requested options. Did you exclude both "conv_dnn" and "conv_gemm" from the optimizer? If on GPU, is cuDNN available and does the GPU support it? If on CPU, do you have a BLAS library installed Theano can link against?

It will also limit calcs to float32 which is most of the improvement in the speed. Now when you run check_blas you should have things faster and it should say that theano is binding to blas:

Total execution time: 11.77s on CPU (with direct Theano binding to blas).

  1. Now we need to install the Microsoft C++ compiler. You can get it here:

    https://www.visualstudio.com/free-developer-offers/

Download the visual studio community edition. You only need to install:

Programming Languages/Visual C++/Common Tools for Visual C++ 2015

I did not install anything from the other sections including the Windows and Web Development section

  1. Now you can install CUDA from NVidia here:

    https://developer.nvidia.com/cuda-downloads

I only installed the CUDA/development, visual studio integration and runtime files sections.

  1. Add the following lines to .theanorc:
	[nvcc]
	flags=--cl-version=2015 -D_FORCE_INLINES

if you do not include the cl-version then you get the error:

nvcc fatal : nvcc cannot find a supported version of Microsoft Visual Studio. Only the versions 2010, 2012, and 2013 are supported

the D_FORCE_INLINES part is for an Ubuntu bug although I'm not sure it's necessary anymore. It can help prevent this error:

WARNING (theano.sandbox.cuda): CUDA is installed, but device gpu0 is not available (error: cuda unavailable)

Note: This error seems to also show if the g++ version is too new for the CUDA version.

Some installs suggest having a --use-local-env flag as well but with this setup it leads to the error:

	c:\program files\nvidia gpu computing toolkit\cuda\v8.0\include\host_config.h(203): fatal error C1083: Cannot open include file: 'crtdefs.h': No such file or directory
mod.cu

It does help with older versions of c++ that are not the Visual Studio Windows Kits version.

  1. Nvcc was unable to see the Visual Studio include and library files with the new Kits format. To have it see the includes and avoid the error:

LINK : fatal error LNK1104: cannot open file stdio.h

and see the library files and not get the errors:

LINK : fatal error LNK1104: cannot open file libucrt.lib

and

LINK : fatal error LNK1104: cannot open file uuid.lib

Add a new environmental variable called INCLUDE by going to:

My Computer/Right-click/Properties/Advanced System Settings/Environmental Variables/System Variables - New

Fill in the Variable Value:

C:\Program Files (x86)\Windows Kits\10\Include\10.0.14393.0\um;C:\Program Files (x86)\Windows Kits\10\Include\10.0.14393.0\ucrt

Add a second new environmental variable called LIB and fill in the Variable Value:

C:\Program Files (x86)\Windows Kits\10\Lib\10.0.14393.0\ucrt\x64;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.14393.0\um\x64

Make sure you are using the 10.0.HighestNumber

  1. While in the environmental variables section, in the system variables find the PATH variable and add the mingw64 directory:

C:\Anaconda3\envs\corrnet35\Library\mingw-w64\bin

Or you will get a g++ error or a mingw error e.g.:

WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.

And make sure all of the following are also present in the path:

C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\amd64
C:\Anaconda3
C:\Anaconda3\Scripts
C:\Anaconda3\Library\bin
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\libnvvp

Without the first line you can get the error:

nvcc : fatal error : Cannot find compiler 'cl.exe' in PATH

  1. Restart the computer to allow the path changes created by the install to take effect. Now when you run check_blas it should be faster:

Total execution time: 9.83s on CPU (with direct Theano binding to blas).

  1. Now add the g++ information to .theanorc in the global flag cxx and the gcc sections. Mingw-64w was included in the theano install so all you have to do is link it. You can also throw in the fast_run mode. The .theanorc file should now look like this:
	[global]
	floatx = float32
	cxx = C:\Anaconda3\envs\env_name35\Library\mingw-w64\bin\g++.exe
	mode = FAST_RUN

	[blas]
	ldflags = -LC:\Anaconda3\Library\bin -lmkl_rt

	[gcc]
	cxxflags = -LC:\Anaconda3\envs\env_name35\Library\mingw-w64\include -LC:\Anaconda3\envs\env_name35\Library\mingw-w64\lib -lm

	[nvcc]
	flags=--cl-version=2015 -D_FORCE_INLINES

CPU ONLY 13) Update .theanorc to force use of the cpu if you do not have a gpu. I.e. change it to read:

	[global]
	floatx = float32
	cxx = C:\Anaconda3\envs\env_name35\Library\mingw-w64\bin\g++.exe
	mode = FAST_RUN
	force_device = True
	device = cpu

Without the force_device you can get this error:

ERROR (theano.sandbox.cuda): Failed to compile cuda_ndarray.cu: ('nvcc return status...

GPU ONLY 14) Update .theanorc to change global setting for the device to gpu instead of cpu if you have a gpu. I.e. change it to read:

	[global]
	floatx = float32
	cxx = C:\Anaconda3\envs\env_name35\Library\mingw-w64\bin\g++.exe
	mode = FAST_RUN
	device = gpu

Now when you run it you'll see another big increase in speed:

Total execution time: 0.75s on GPU.

GPU ONLY 15) I signed up as an NVidia developer and downloaded cuDNN, which is a set of files to increase convent speeds, and copied the files into the appropriate directories in the C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0 bin/include/lib directories. In order for cuDNN to work you need to enable it or you get the following error:

Using gpu device 0: GeForce GTX 1060 (CNMeM is disabled, CuDNN not available)

You can enable it with the following in .theanorc:

	[dnn]
	enabled=True

and now it should say:

Using gpu device 0: GeForce GTX 1060 (CNMeM is disabled, cuDNN 5105)

I enabled CNMeM, with 70% GPU usage by adding this to .theanorc:

	[lib]
	cnmem=0.70

And now it says:

Using gpu device 0: GeForce GTX 1060 (CNMeM is enabled with initial size: 70.0% of memory, cuDNN 5105)

Note: You don't need the dnn section if you enable CNMeM since it will automatically use DNN if both are available. The 3 options for CNMeM are:

0 not enabled
0 < N <= 1: use this fraction of the total GPU memory (clipped to .95 for driver memory). [Note: 			This should be a float value, for instance 0.25 or 1.0]
> 1 use this number in megabytes (MB) of memory

If the percent is set too high you may get the below error. Either close open scripts or reduce the percent:

ERROR (theano.sandbox.cuda): ERROR: Not using GPU. Initialisation of device gpu failed: initCnmem: cnmemInit call failed! Reason=CNMEM_STATUS_OUT_OF_MEMORY. numdev=1

And if that is happening too often still you can just use the dnn option and skip CNMeM.

GPU ONLY 16) I also tried the cuBLAS library by copying the file cublas64_80.dll from C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin into the C:\Anaconda3\Library\bin directory and using that instead of mklBLAS (mkl_rt.dll) and changing the blas line in .theanorc to:

	[blas]
	ldflags = -LC:\Anaconda3\Library\bin -lcublas64_80

Note: Do not use this if only using cpu and not the gpu. Stick with mklBLAS (mkl_rt).

Steps 15-6 are supposed to increase performance but I got no improvement in speed at all. Intel says cuBLAS should be 6-17x faster so I'm just leaving it that way in case it makes a difference for bigger networks.

  1. Install PyCharm

    https://www.jetbrains.com/pycharm/download/?gclid=CIaQwY2q8tACFZ26wAod-ScPwA&gclsrc=aw.ds.ds&dclid=CJC7042q8tACFRO6TwodaKAAAA#section=windows

  2. Open PyCharm, on the bottom right press Configure/Settings and then go to Project Interpreter. Press the down arrow next to Project Interpreter and press Show All. Press the + sign and Add Local. Navigate to and select python.exe in your virtual environment:

C:\Anaconda3\envs\env_name35\python.exe

If you're like me and like undo/redo buttons in a toolbar then you can check View/Toolbar and View/Tool Buttons. You can then customize the toolbar with a right-click on it and select Customize Menus and Toolbars.

  1. Install keras and/or lasagne and program away. If the keras download breaks check_blas.py then update all the libraries with:

conda upgrade --all

Good luck!!

For reference the final GPU .theanorc looks like this:

[global]
floatx = float32
device = gpu
mode = FAST_RUN
cxx = C:\Anaconda3\envs\env_name35\Library\mingw-w64\bin\g++.exe

[blas]
ldflags = -LC:\Anaconda3\Library\bin -lcublas64_80

[gcc]
cxxflags = -LC:\Anaconda3\envs\env_name35\Library\mingw-w64\include -LC:\Anaconda3\envs\env_name35\Library\mingw-w64\lib -lm

[nvcc]
flags=--cl-version=2015 -D_FORCE_INLINES

[dnn]
enabled=True

[lib]
cnmem=0.70

The final CPU .theanorc looks like this:

[global]
floatx = float32
force_device = True
device = cpu
mode = FAST_RUN
cxx = C:\Anaconda3\envs\env_name35\Library\mingw-w64\bin\g++.exe

[blas]
ldflags = -LC:\Anaconda3\Library\bin -lmkl_rt

[gcc]
cxxflags = -LC:\Anaconda3\envs\env_name35\Library\mingw-w64\include -LC:\Anaconda3\envs\env_name35\Library\mingw-w64\lib -lm

[nvcc]
flags=--cl-version=2015 -D_FORCE_INLINES

And the list of installation files I used is:

Anaconda3-4.2.0-Windows-x86_64.exe
vs_community.exe (Programming Languages/Visual C++/Common Tools for Visual C++ 2015)
cuda_8.0.44_win10.exe (CUDA/development, visual studio integration and runtime files sections)
pycharm-community-2016.3.1.exe
cudnn-8.0-windows10-x64-v5.1.zip (Optional and requires login with NVidia)
@nouiz
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nouiz commented Dec 22, 2016 via email

@Xrave
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Xrave commented Dec 23, 2016

I think we should update the website with this post, because it actually described every single problem i hit upon my installation (and then some), and fixes every single one with surgical precision.

Thank you so much 👍

@mustgoplay
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Thank you. I'm glad it's helping. I fine tuned it a little in case it gets included by listing more errors that I've run into or read about and some formatting and directory info.

@mustgoplay mustgoplay changed the title Anaconda Python with PyCharm, CUDA and Theano Install on Windows 10: Anaconda Python, CUDA, cuDNN and Theano with PyCharm Install for CPU and GPU on Windows 10: Dec 24, 2016
@mustgoplay mustgoplay changed the title Anaconda Python, CUDA, cuDNN and Theano with PyCharm Install for CPU and GPU on Windows 10: Anaconda Python, CUDA, cuDNN and Theano with PyCharm Install for CPU and GPU on Windows 10 Dec 24, 2016
@mustgoplay
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mustgoplay commented Dec 24, 2016 •
edited

Please let me know when/if this gets included so I can stop OCDng and updating. I added a new error for CNMeM running out of memory and rearranged that section a little, updated the title, added a reference and clarified the difference between a cpu and gpu install and added old errors I ran into.

Lucked out with a new post that told me the environmental variable for cuda so no need to mess with nvcc profile now. (https://groups.google.com/forum/#!topic/theano-users/_Rl_UeQ7FxU) so a much cleaner install. Only .theanorc and path/environmental variables need to be made now.

I think it's finally done now.

@mustgoplay mustgoplay mentioned this issue Dec 28, 2016
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@roachsinai
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A great help! Thank you very much!

@KhaledElTahan
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I could kiss you right now :D
Thanks for the help !

@nouiz nouiz added this to the 0.9 milestone Jan 19, 2017
This was referenced Jan 23, 2017
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@schlichtanders
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Following the given step by step tutorial I was (almost) able to install theano with C compiler and full GPU support on Windows 10 for python 3.6. Thank you very much for these crucial hints!

In addition I had to do some additional adaptations:

  • Extras For Step 3: when installing theano for python 3.6 with conda install -c conda-forge theano, the following crucial packages are currently left out:

    • m2w64-toolchain

    • mkl-service

      just install them separately with conda install
      [this might get soon fixed by anaconda, but for now I spend a lot of time to track down the missing mingw-w64 folder which happens to correspond to the m2w64-toolchain conda package]

  • Extras Before Step 9: I had to install further libraries (called SDK), I found one, however only in German

    For further reference, here old sdk archives can be found

  • Extras Before Step 11: Add the following lines to your .theanorc (on Windows it should be under %USERPROFILE%, if not create it there)

    [gcc]
cxxflags = -LC:\tools\Anaconda3

    otherwise I get the following error: collect2.exe: error: ld returned 1 exit status. I think this is due to the right python36.dll not being found, which is prevented by adding the above path.

Important to Note:

  • after Step 11, running check_blas.py from powershell.exe breaks. However when run from cmd.exe everything is fine. I don't know why this happens.

@schlichtanders schlichtanders mentioned this issue Feb 17, 2017
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@mustgoplay
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@schlichtanders , thanks for the update for 3.6. I haven't tried it but other installations also recommended the SDK install but it wasn't needed when I used the above method for 3.5 so I went for the minimum install. I think you might actually be able to get them from the install in step 6 when you customize the install. It's weird that it's required now but glad you figured it out.

@sejabs
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sejabs commented Feb 26, 2017 •
edited

For now, the latest cuda 8.0 has supported VS2015 under windows and gcc 5.3 under ubuntu according to their online document. And Ray Donnelly has kindly uploaded his theano 0.9.0rc1 version onto anaconda cloud. To install this package with conda run:conda install -c rdonnelly theano=0.9.0rc1. This package has Integrate libgpuarray and pygpu.
For me, under windows 10 x64 and miniconda3(python 3.6), I only installed numpy (auto mkl), scipy, Vs2015 pro, CUDA 8.0 (the latest), set VS and CUDA path Variable, set .theanorc file.

  • With cpu, it goes fine.

  • But with "device = cuda", the check_blas.py will throw error:
    ERROR (theano.gpuarray): pygpu was configured but could not be imported or is too old (version 0.6 or higher required).
    If add import pygpu, the error changes: ERROR (theano.gpuarray): Could not initialize pygpu, support disabled.
    If change to "device = gpu", it works with warning and result:
    WARNING (theano.sandbox.cuda): The cuda backend is deprecated and will be removed in the next release (v0.10). Please switch to the gpuarray backend. You can get more information about how to switch at this URL: https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29.....
    Using gpu device 0: GeForce GTX 670 (CNMeM is disabled, cuDNN not available)......
    Total execution time: 2.01s on GPU.

It's same on Ubuntu 16.04.

@abergeron
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Can you try to just run python -c 'import pygpu'. This could help figure out what is wrong with the package.

@petecog
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petecog commented Mar 21, 2017

Great info! I think i've been going through the same pain, trying various combinations of python version, compilers, etc.

Any chance of an uplift of the Theano docs to reflect guidance for windows? http://deeplearning.net/software/theano/install_windows.html#install-windows

@nouiz
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nouiz commented Mar 21, 2017 via email

@petecog
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petecog commented Mar 22, 2017 •
edited

@nouiz Theano seemed to work with your suggestion above. Tests mostly passed. But continued to have difficultes with PyMC3 which I was hoping to use theano for. Found this guide also. Currenly following. https://github.com/philferriere/dlwin

@lamblin
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lamblin commented Mar 23, 2017

The guide you link uses Theano 0.8.2 and the old GPU back-end, though.
The Windows documentation should be updated shortly now that 0.9 has been released.

@Ackbach
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Ackbach commented Apr 17, 2017

One of your References links has gone stale: the Tutorial: Theano Install on ...

@nouiz
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nouiz commented Apr 18, 2017

For the insatallation, I would recommand that people use the conda versoin on windows:

conda install theano pygpu

I don't know how that interfere with pycharm. No need to install VS anymore. Also see https://github.com/Theano/Theano/wiki/Converting-to-the-new-gpu-back-end%28gpuarray%29 to use the new gpu back-end

@guddulrk
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Hi All,
I am getting following error using Python 3.5, Theano 0.8.1, Keras 0.3.3 and CUDA 8.0 with Windows 7 64 bit. Thanks for any help

AssertionError: AbstractConv2d Theano optimization failed: there is no implementation available supporting the requested options. Did you exclude both "conv_dnn" and "conv_gemm" from the optimizer? If on GPU, is cuDNN available and does the GPU support it? If on CPU, do you have a BLAS library installed Theano can link against?

@darthdeus
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I'm not really sure what I'm doing wrong, but there's no errors and yet it shows "Total execution time: 0.00"

https://gist.github.com/darthdeus/d96cd056ab7725d42676f2882dc3918b

@ghost
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ghost commented May 18, 2017

Made a similar install on Win 10 relying on similar web tutorials, it was hard actually. All works well now but cuDNN, which is there but not compiling. Getting this error when trying the usual benchmark code:

Using gpu device 0: GeForce GTX 1070 (CNMeM is enabled with initial size: 80.0% of memory, cuDNN Can not compile with cuDNN. We got this error:
b'')
ERROR (theano.gof.opt): SeqOptimizer apply <theano.sandbox.cuda.dnn.NoCuDNNRaise object at 0x000002757D4E4080>
ERROR (theano.gof.opt): Traceback:
ERROR (theano.gof.opt): Traceback (most recent call last):
  File "C:\ProgramData\Anaconda3\lib\site-packages\theano\gof\opt.py", line 235, in apply
    sub_prof = optimizer.optimize(fgraph)
  File "C:\ProgramData\Anaconda3\lib\site-packages\theano\gof\opt.py", line 87, in optimize
    ret = self.apply(fgraph, *args, **kwargs)
  File "C:\ProgramData\Anaconda3\lib\site-packages\theano\sandbox\cuda\dnn.py", line 3479, in apply
    if not dnn_available():
  File "C:\ProgramData\Anaconda3\lib\site-packages\theano\sandbox\cuda\__init__.py", line 360, in dnn_available
    dnn_available.msg)
RuntimeError: You enabled cuDNN, but we aren't able to use it: Can not compile with cuDNN. We got this error:
b''

It should be something messed up with the .theanorc file, many indecisive tweaking attempts already, so asking for help. Any advice? Thanks!

[global]
device = gpu
floatX = float32
cuda.disable_gcc_cudnn_check=True
optimizer_including=cudnn

[cuda]
root = C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0

[nvcc]
flags=-D_FORCE_INLINES
fastmath=True
compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\amd64
optimizer_including=cudnn

[dnn]
enabled = True

[lib]
cnmem=0.8

@nouiz
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nouiz commented May 18, 2017 via email

@ghost
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ghost commented May 19, 2017

Hi,

Many thanks for your reply. I have that already from my installation and it is up to date, but if I use device=cuda from my .theanorc file and run the customary testing script, I get another error:

ERROR (theano.gpuarray): pygpu was configured but could not be imported or is too old (version 0.6 or higher required)
NoneType: None

Is that related to my Anaconda using Python 3.6 ? I can read here and there this backend supports up to 3.5?

@emulvihill
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I had some trouble with 3.6, sadly. I had better luck with 3.5, although I cannot remember anymore which component did not like 3.6.

I also have read conflicting reports about visual studio 2015, as in CUDA 8 was released with support for it, but Microsoft may have changed something that hurt compatibility after the fact? I went with the ancient visual studio 2013 for this reason. (although it was humorous to see the "fabulous new" technologies they were pushing in this version. Silverlight anyone???)

Oh how I wish there was just a more minimal compiler built into Windows than the bloated ecosystem of VS, which i know nothing about nor care to.

@emulvihill
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This is my .theanorc, and I can currently use CUDA 8 on Windows 10 & VS 2013, if it is any help. Mostly got here through trial and error, so this might not be optimal setup:

[global]
device = cuda
floatX = float32

[nvcc]
compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin

[cuda]
root = C:\CUDA\v8.0

[lib]
cnmem = 0.8

[dnn]
enabled = True
include_path=C:\CUDA\v8.0\include
library_path=C:\CUDA\v8.0\lib\x64

@lamblin lamblin removed this from the 0.9 milestone Jun 8, 2017
@nouiz
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nouiz commented Aug 24, 2017

This instruction where for the old back-end and don't work for the new back-end. So closing.

Thanks for the instruction, it helped many people when it was useful.

@nouiz nouiz closed this as completed Aug 24, 2017
@bonejay
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bonejay commented Sep 17, 2017 •
edited

It is already failing with the theano part:

Proceed ([y]/n)? Fetching packages ... libpython-2.0- 100%

|###############################| Time: 0:00:00 1.27 MB/s theano-

0.8.2-p 100% |###############################| Time: 0:00:01 2.91 MB/s

Extracting packages ... [ COMPLETE

]|##################################################| 100%

Linking packages ... [ COMPLETE

]|##################################################| 100%

(C:\Anaconda3\envs\env_name34) C:\Anaconda3>python check_blas.py

Traceback (most recent call last): File

"check_blas.py", line 16, in import theano

ImportError: No module named 'theano'

I tried and I can import theano in spyder on the console but nowhere else

@nouiz
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nouiz commented Sep 18, 2017

You probably have multiple Python installation and Theano was installed in only one of them.

Why not use the spyder console if that work? Also, this isn't related to this issue, so don't continue the discussion here.

@mateuszjurewicz
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Hi,
After following every step I got the error:

Installing the nvidia driver available on the same download page as the cuda package will fix the problem: http://developer.nvidia.com/cuda-downloads

I'm using Windows 10, with Nvidia GeForce 840M with 388 driver, CUDA v 9.0. Updated the driver to the latest one (388), the error persisted. Couldn't find any driver available specifically on the page to which they point me.

@nouiz
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nouiz commented Nov 6, 2017 via email

@Sayantan17
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Hello , guys is this right still i m getting something weird , can somebody sort it out? The .theanorc file is the:-
[global]
floatx = float32
device = cuda
mode = FAST_RUN
cxx = C:\Users\Sayantan\Anaconda3\Library\mingw-w64\bin\g++.exe

[cuda]
enabled=True

[blas]
ldflags = -LC:\Users\Sayantan\Anaconda3\Library\bin -lcublas64_80

[gcc]
cxxflags = -LC:\Users\Sayantan\Anaconda3\Library\mingw-w64\include -LC:Users\Sayantan\Anaconda3\Library\mingw-w64\lib -lm

[nvcc]
flags=--cl-version=2015 -D_FORCE_INLINES

[dnn]
enabled=False

[lib]
cnmem=0.70

But still not appropriate .
untitled
Why is it written Mapped device ........

@nouiz
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nouiz commented May 16, 2018 via email

@Sayantan17
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@nouiz lemme check , will let you know!

@amartya-bhattacharya
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Anyone get this error:
raise Exception("The nvidia driver version installed with this OS " Exception: The nvidia driver version installed with this OS does not give good results for reduction.Installing the nvidia driver available on the same download page as the cuda package will fix the problem: http://developer.nvidia.com/cuda-downloads

I'm using CUDA 9.2 and my Nvidia driver is v398.26

@abergeron
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@Nomat Please don't add comments unrelated to the issue. This is not a forum. Open a new issue or post on the theano-users mailing list if you need help.

Also, this is apparently a return of an old problem. The solution is probably to downgrade the driver version a bit until the current drivers are fine again.

@lucas-figueiredo-m
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lucas-figueiredo-m commented Aug 7, 2018 •
edited

Hi! I was having dll load failed problem and I tryied to use the suggestion on this post. I've just adapted for my environment ( I didnt create a virtual environmnet on conda. I just run it from default) and I got two error popping up while running this code below:

import theano theano.test()

the errors were:

The execution couldn't continue because zlib1.dll wasn't found. Reinstalling the program to fix the problem

and

The execution couldn't continue because libgmp-10.dll wasn't found. Reinstalling the program to fix the problem

How can I fix it?
The .thenorc file for my config is:

`[global]
floatx = float32
device = cpu
mode = FAST_RUN
cxx = C:\Users\Lucas\Anaconda3\Library\mingw-w64\bin\g++.exe

[blas]
ldflags = -LC:\Anaconda3\Library\bin -lcublas64_80

[gcc]
cxxflags = -LC:\Users\Lucas\Anaconda3\Library\mingw-w64\include -LC:\Users\Lucas\Anaconda3\Library\mingw-w64\lib -lm

[dnn]
enabled=True

[lib]
cnmem=0.70`

What can I do?

@abergeron
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@lucas-figueiredo-m Once again, this is not a forum and your problem is unrelated to this issue.

Either your python installation is bad or the compile flags you set interfere with the compilation. Remove your .theanorc and try again. If that doesn't fix it, try reinstalling anaconda from scratch.

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