When I install tensorflow-gpu through Conda; it gives me the following output:
conda install tensorflow-gpu
Collecting package metadata (current_repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /home/psychotechnopath/anaconda3/envs/DeepLearning3.6
added / updated specs:
- tensorflow-gpu
The following packages will be downloaded:
package | build
---------------------------|-----------------
_tflow_select-2.1.0 | gpu 2 KB
cudatoolkit-10.1.243 | h6bb024c_0 347.4 MB
cudnn-7.6.5 | cuda10.1_0 179.9 MB
cupti-10.1.168 | 0 1.4 MB
tensorflow-2.1.0 |gpu_py36h2e5cdaa_0 4 KB
tensorflow-base-2.1.0 |gpu_py36h6c5654b_0 155.9 MB
tensorflow-gpu-2.1.0 | h0d30ee6_0 3 KB
------------------------------------------------------------
Total: 684.7 MB
The following NEW packages will be INSTALLED:
cudatoolkit pkgs/main/linux-64::cudatoolkit-10.1.243-h6bb024c_0
cudnn pkgs/main/linux-64::cudnn-7.6.5-cuda10.1_0
cupti pkgs/main/linux-64::cupti-10.1.168-0
tensorflow-gpu pkgs/main/linux-64::tensorflow-gpu-2.1.0-h0d30ee6_0
I see that installing tensorflow-gpu automatically triggers the installation of the cudatoolkit and cudnn. Does this mean that I no longer need to install CUDA and CUDNN manually anymore to be able to use tensorflow-gpu? Where does this conda installation of CUDA reside?
I first installed CUDA and CuDNN the old way (e.g. by following these installation instructions: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html )
And then I noticed that tensorflow-gpu was also installing cuda and cudnn
Do i now have two versions of CUDA/CuDNN installed and how do I check this?
Answers:
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Method 1
Do i now have two versions of CUDA installed and how do I check this?
No.
conda installs the bare minimum redistributable library components required to support the CUDA accelerated packages they offer. The package name cudatoolkit is a complete misnomer. It is nothing of the sort. Even though it is now greatly expanded in scope from what it used to be (literally 5 files — I think at some point they must have gotten a licensing deal from NVIDIA because some of this wasn’t/isn’t on the official “freely redistributable” list AFAIK), it still is basically just a handful of libraries.
You can check this for yourself:
cat /opt/miniconda3/conda-meta/cudatoolkit-10.1.168-0.json
{
"build": "0",
"build_number": 0,
"channel": "https://repo.anaconda.com/pkgs/main/linux-64",
"constrains": [],
"depends": [],
"extracted_package_dir": "/opt/miniconda3/pkgs/cudatoolkit-10.1.168-0",
"features": "",
"files": [
"lib/cudatoolkit_config.yaml",
"lib/libcublas.so",
"lib/libcublas.so.10",
"lib/libcublas.so.10.2.0.168",
"lib/libcublasLt.so",
"lib/libcublasLt.so.10",
"lib/libcublasLt.so.10.2.0.168",
"lib/libcudart.so",
"lib/libcudart.so.10.1",
"lib/libcudart.so.10.1.168",
"lib/libcufft.so",
"lib/libcufft.so.10",
"lib/libcufft.so.10.1.168",
"lib/libcufftw.so",
"lib/libcufftw.so.10",
"lib/libcufftw.so.10.1.168",
"lib/libcurand.so",
"lib/libcurand.so.10",
"lib/libcurand.so.10.1.168",
"lib/libcusolver.so",
"lib/libcusolver.so.10",
"lib/libcusolver.so.10.1.168",
"lib/libcusparse.so",
"lib/libcusparse.so.10",
"lib/libcusparse.so.10.1.168",
"lib/libdevice.10.bc",
"lib/libnppc.so",
"lib/libnppc.so.10",
"lib/libnppc.so.10.1.168",
"lib/libnppial.so",
"lib/libnppial.so.10",
"lib/libnppial.so.10.1.168",
"lib/libnppicc.so",
"lib/libnppicc.so.10",
"lib/libnppicc.so.10.1.168",
"lib/libnppicom.so",
"lib/libnppicom.so.10",
"lib/libnppicom.so.10.1.168",
"lib/libnppidei.so",
"lib/libnppidei.so.10",
"lib/libnppidei.so.10.1.168",
"lib/libnppif.so",
"lib/libnppif.so.10",
"lib/libnppif.so.10.1.168",
"lib/libnppig.so",
"lib/libnppig.so.10",
"lib/libnppig.so.10.1.168",
"lib/libnppim.so",
"lib/libnppim.so.10",
"lib/libnppim.so.10.1.168",
"lib/libnppist.so",
"lib/libnppist.so.10",
"lib/libnppist.so.10.1.168",
"lib/libnppisu.so",
"lib/libnppisu.so.10",
"lib/libnppisu.so.10.1.168",
"lib/libnppitc.so",
"lib/libnppitc.so.10",
"lib/libnppitc.so.10.1.168",
"lib/libnpps.so",
"lib/libnpps.so.10",
"lib/libnpps.so.10.1.168",
"lib/libnvToolsExt.so",
"lib/libnvToolsExt.so.1",
"lib/libnvToolsExt.so.1.0.0",
"lib/libnvblas.so",
"lib/libnvblas.so.10",
"lib/libnvblas.so.10.2.0.168",
"lib/libnvgraph.so",
"lib/libnvgraph.so.10",
"lib/libnvgraph.so.10.1.168",
"lib/libnvjpeg.so",
"lib/libnvjpeg.so.10",
"lib/libnvjpeg.so.10.1.168",
"lib/libnvrtc-builtins.so",
"lib/libnvrtc-builtins.so.10.1",
"lib/libnvrtc-builtins.so.10.1.168",
"lib/libnvrtc.so",
"lib/libnvrtc.so.10.1",
"lib/libnvrtc.so.10.1.168",
"lib/libnvvm.so",
"lib/libnvvm.so.3",
"lib/libnvvm.so.3.3.0"
]
.....
i.e. what you get is (keeping in mind most of those “files” above are just symlinks)
- CUBLAS runtime
- The CUDA runtime library
- CUFFT runtime
- CUrand runtime
- CUsparse rutime
- CUsolver runtime
- NPP runtime
- nvblas runtime
- NVTX runtime
- NVgraph runtime
- NVjpeg runtime
- NVRTC/NVVM runtime
The CUDNN package that conda installs is the redistributable binary distribution which is identical to what NVIDIA distribute — which is exactly two files, a header file and a library.
You would still require a supported NVIDIA driver installation to make the tensorflow which conda installs work.
If you want to actually compile and build CUDA code, you need to install a separate CUDA toolkit which contains all the the development components which conda deliberately omits from their distribution.
All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0