creating 2 env seem the right thing to do. I do not think dnn.libary_path
override LD_LIBRARY_PATH.

On Thu, May 10, 2018 at 7:06 PM Michael Klachko <michaelklac...@gmail.com>
wrote:

> What is the best way to do that? Should I use separate conda environments
> for theano and tensorflow, and create LD_LIBRARY_PATH in each? Does
> dnn.libary_path in theanorc override LD_LIBRARY_PATH?
>
>
>
> On Thursday, May 10, 2018 at 3:09:29 PM UTC-7, nouiz wrote:
>
>> You could have multiple cuda version installed to have TF working.
>>
>> Le jeu. 10 mai 2018 16:28, Michael Klachko <michael...@gmail.com> a
>> écrit :
>>
>>> After struggling with this error for a day, I decided to upgrade CUDA to
>>> 9.1 and CuDNN to 7.1. After that I got "your driver might be too old"
>>> error, which was resolved by updating the driver to 396.24. Also, in the
>>> process I found out I had older CuDNN files in /usr/lib/x86_64-linux-gnu/
>>> directory. Not sure how they got there, perhaps because sometimes I
>>> installed CuDNN using .deb package, and sometimes by manually copying the
>>> files. So it's probably not a good idea to mix .deb and .run cuda
>>> installation methods.
>>>
>>> Anyway, now theano works fine now, but unfortunately my Tensorflow is
>>> broken because it does not support cuda 9.1 yet... Will probably have to
>>> compile it from source.
>>>
>>>
>>>
>>> On Thursday, May 10, 2018 at 11:30:38 AM UTC-7, Arnaud Bergeron wrote:
>>>
>>>> This is a new one.  It is also very weird since gemm doesn't involve
>>>> cuLinkAddData.  This may be an error message from something else.
>>>>
>>>> First things first, since you are on cuda 9.0, I would recommend that
>>>> you update your driver to 384.111 or 390.*.  If that doesn't help, then
>>>> I'll need some help reproducing the problem since I don't get that in any
>>>> of my environments.
>>>>
>>> Le 8 mai 2018 à 18:15, Michael Klachko <michael...@gmail.com> a écrit :
>>>>
>>>> I have CUDA 9.0 and CuDNN 7.0.5 on my Ubuntu 16.04, and Tensorflow
>>>> works fine. In order to install theano, I first installed miniconda, then
>>>> ran "conda install theano pygpu" and it seemed to have installed fine.
>>>>
>>>>
>>>>
>>>> However, here's what I get:
>>>>
>>>>
>>>> $ python
>>>> Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
>>>> [GCC 7.2.0] on linux
>>>> Type "help", "copyright", "credits" or "license" for more information.
>>>> >>> import theano
>>>> Using cuDNN version 7005 on context None
>>>> ERROR (theano.gpuarray): Could not initialize pygpu, support disabled
>>>> Traceback (most recent call last):
>>>>   File 
>>>> "/home/michael/miniconda2/envs/las/lib/python3.6/site-packages/theano/gpuarray/__init__.py",
>>>>  line 227, in <module>
>>>>     use(config.device)
>>>>   File 
>>>> "/home/michael/miniconda2/envs/las/lib/python3.6/site-packages/theano/gpuarray/__init__.py",
>>>>  line 214, in use
>>>>     init_dev(device, preallocate=preallocate)
>>>>   File 
>>>> "/home/michael/miniconda2/envs/las/lib/python3.6/site-packages/theano/gpuarray/__init__.py",
>>>>  line 159, in init_dev
>>>>     pygpu.blas.gemm(0, tmp, tmp, 0, tmp, overwrite_c=True)
>>>>   File "pygpu/blas.pyx", line 149, in pygpu.blas.gemm
>>>>   File "pygpu/blas.pyx", line 47, in pygpu.blas.pygpu_blas_rgemm
>>>> pygpu.gpuarray.GpuArrayException: (b'cuLinkAddData: CUDA_ERROR_UNKNOWN: 
>>>> unknown error', 3)
>>>>
>>>>
>>>>
>>>> Here's the packages I have installed in this environment:
>>>>
>>>>
>>>> $ conda list
>>>> # packages in environment at /home/michael/miniconda2/envs/las:
>>>> #
>>>> # Name                    Version                   Build  Channel
>>>> binutils_impl_linux-64    2.28.1               had2808c_3
>>>> binutils_linux-64         7.2.0                        26
>>>> ca-certificates           2018.03.07                    0
>>>> certifi                   2018.4.16                py36_0
>>>> gcc_impl_linux-64         7.2.0                habb00fd_3
>>>> gcc_linux-64              7.2.0                        26
>>>> gxx_impl_linux-64         7.2.0                hdf63c60_3
>>>> gxx_linux-64              7.2.0                        26
>>>> intel-openmp              2018.0.0                      8
>>>> libedit                   3.1                  heed3624_0
>>>> libffi                    3.2.1                hd88cf55_4
>>>> libgcc-ng                 7.2.0                hdf63c60_3
>>>> libgfortran-ng            7.2.0                hdf63c60_3
>>>> libgpuarray               0.7.5                h14c3975_0
>>>> libstdcxx-ng              7.2.0                hdf63c60_3
>>>> mako                      1.0.7            py36h0727276_0
>>>> markupsafe                1.0              py36hd9260cd_1
>>>> mkl                       2018.0.2                      1
>>>> mkl-service               1.1.2            py36h17a0993_4
>>>> mkl_fft                   1.0.1            py36h3010b51_0
>>>> mkl_random                1.0.1            py36h629b387_0
>>>> ncurses                   6.0                  h9df7e31_2
>>>> nose                      1.3.7            py36hcdf7029_2
>>>> numpy                     1.14.2           py36hdbf6ddf_1
>>>> openssl                   1.0.2o               h20670df_0
>>>> pip                       10.0.1                   py36_0
>>>> pygpu                     0.7.5            py36h14c3975_0
>>>> python                    3.6.5                hc3d631a_2
>>>> readline                  7.0                  ha6073c6_4
>>>> scipy                     1.0.1            py36hfc37229_0
>>>> setuptools                39.1.0                   py36_0
>>>> six                       1.11.0           py36h372c433_1
>>>> sqlite                    3.23.1               he433501_0
>>>> theano                    1.0.1            py36h6bb024c_0
>>>> tk                        8.6.7                hc745277_3
>>>> wheel                     0.31.0                   py36_0
>>>> xz                        5.2.3                h5e939de_4
>>>> zlib                      1.2.11               ha838bed_2
>>>>
>>>>
>>>>
>>>> Here's my .theanorc file:
>>>>
>>>>
>>>> [global]
>>>> device = cuda0
>>>> optimizer_including = cudnn
>>>> floatX = float32
>>>>
>>>> [dnn]
>>>> include_path = /usr/local/cuda/include
>>>> library_path = /usr/local/cuda/lib64
>>>>
>>>> [lib]
>>>> cnmem = 0.7
>>>>
>>>> [nvcc]
>>>> fastmath = True
>>>>
>>>> [blas]
>>>> # Only used for device = cpu
>>>> ldflags = -lopenblas
>>>>
>>>> [cuda]
>>>> root = /usr/local/cuda/bin
>>>>
>>>>
>>>>
>>>> Nvidia driver: Driver Version: 384.81
>>>>
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>>>>
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