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Created October 19, 2021 22:05
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Installing dlib using conda with CUDA enabled
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install_dlib_conda.md

Installing dlib using conda with CUDA enabled

Prerequisite: conda and/or miniconda are already installed

  1. Create a conda environment.
$ conda create -n dlib python=3.8 cmake ipython
  1. Activate the environment.
$ conda activate dlib
  1. Install CUDA and cuDNN with conda using nvidia channel
$ conda install cuda cudnn -c nvidia

Then find the path to the nvcc of this environment. We will use this path for the build step below

$which nvcc
/path/to/your/miniconda3/envs/dlib/bin/
  1. Install dlib. Clone and build dlib from source
$ git clone https://github.com/davisking/dlib.git
$ cd dlib
$ mkdir build
$ cd build
$ cmake .. -DDLIB_USE_CUDA=1 -DUSE_AVX_INSTRUCTIONS=1 -DCUDAToolkit_ROOT=/path/to/your/miniconda3/envs/dlib/bin/
$ cmake --build .
$ cd ..
$ python setup.py install --set DLIB_USE_CUDA=1
  1. Test dlib
(dlib) $ ipython
Python 3.8.12 (default, Oct 12 2021, 13:49:34)
Type 'copyright', 'credits' or 'license' for more information
IPython 7.27.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: import dlib

In [2]: dlib.DLIB_USE_CUDA
Out[2]: True

In [3]: print(dlib.cuda.get_num_devices())
1
@ravikiranrao
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Cleanest installation guide of dlib (with CUDA) on the internet. Thanks a ton.

@sashavor
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sashavor commented Sep 14, 2022 •
edited
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Is there specific cuda toolkit version that works with dlib? I can't manage to get it to run:

cnn_face_detector = dlib.cnn_face_detection_model_v1('dlib_models/mmod_human_face_detector.dat')
RuntimeError: Error while calling cudaMallocHost(&data, new_size*sizeof(float)) in ./dlib/dlib/cuda/gpu_data.cpp:211. code: 222, reason: the provided PTX was compiled with an unsupported toolchain.

@Amr-YA
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Amr-YA commented Sep 20, 2022 •
edited
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Surprisingly, this the only way it worked.
Although it froze still while building a sample cuda project and didn't continue till I pressed "space", no idea why or how but it worked.

@sherelynyap
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helped me a lot, thanks!

@brianholland
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brianholland commented Oct 12, 2022 •
edited
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Many thanks! Now I'm off to the next glitch:

Same problem, I think, as @sashavor :

dlib '19.24.99', face_recognition_models '0.1.0'
Ubuntu 20.04, python 3.10

import face_recognition_models
import dlib
cnn_face_detection_model = face_recognition_models.cnn_face_detector_model_location()
cnn_face_detector = dlib.cnn_face_detection_model_v1(cnn_face_detection_model)

I get

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In [1], line 4
     2 import dlib
     3 cnn_face_detection_model = face_recognition_models.cnn_face_detector_model_location()
----> 4 cnn_face_detector = dlib.cnn_face_detection_model_v1(cnn_face_detection_model)

RuntimeError: Error while calling cudaMallocHost(&data, new_size*sizeof(float)) in file /home/brian/dlib/dlib/cuda/gpu_data.cpp:211. code: 222, reason: the provided PTX was compiled with an unsupported toolchain.

@EylonSho
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Many thanks! Now I'm off to the next glitch:

Same problem, I think, as @sashavor :

dlib '19.24.99', face_recognition_models '0.1.0' Ubuntu 20.04, python 3.10

import face_recognition_models
import dlib
cnn_face_detection_model = face_recognition_models.cnn_face_detector_model_location()
cnn_face_detector = dlib.cnn_face_detection_model_v1(cnn_face_detection_model)

I get

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In [1], line 4
     2 import dlib
     3 cnn_face_detection_model = face_recognition_models.cnn_face_detector_model_location()
----> 4 cnn_face_detector = dlib.cnn_face_detection_model_v1(cnn_face_detection_model)

RuntimeError: Error while calling cudaMallocHost(&data, new_size*sizeof(float)) in file /home/brian/dlib/dlib/cuda/gpu_data.cpp:211. code: 222, reason: the provided PTX was compiled with an unsupported toolchain.

Any suggestion for this issue please?

@gipinze
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gipinze commented Dec 28, 2022

Hi,

Thanks for your explanation, I used it, mixing it with some other I found around internet to make a new one that worked for me

I used my device CUDA,=11.5 and Cudnn=8.3 and runs in Anaconda with python=3.8 as well

The biggest differences is that, I guess due to the time you uploaded this, with VS 2022 is not possible to compile DLIB, so we need to specify the version and not use the GIF_SUPPORT library

Thanks again for your clear explanation

@deerwinter
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for windows user who met the
import dlib Traceback (most recent call last): File "<stdin>", line 1, in <module> File "C:\Users\oceanx\AppData\Local\Programs\Python\Python310\lib\site-packages\dlib-19.24.99-py3.10-win-amd64.egg\dlib\__init__.py", line 19, in <module> from _dlib_pybind11 import * ImportError: DLL load failed while importing _dlib_pybind11: The specified module could not be found.
check your __init__.py , make sure its "on"=="on".

@ankit-g
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ankit-g commented May 2, 2023

I'm getting this error:

Could not load library libcudnn_cnn_train.so.8. Error: /home/ankit/anaconda3/envs/dlib/bin/../lib/libcudnn_ops_train.so.8: undefined symbol: _Z22cudnnGenericOpTensorNdILi3EE13cudnnStatus_tP12cudnnContext16cudnnGenericOp_t21cudnnNanPropagation_tPKdPKvPK17cudnnTensorStructS8_S8_SB_S8_S8_SB_Pv, version libcudnn_ops_infer.so.8

@mtalhaubaid
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mtalhaubaid commented Jun 13, 2023 •
edited
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I got this message after installing.

Installed c:\users\asdf\anaconda3\envs\dlib\lib\site-packages\dlib-19.24.99-py3.8-win-amd64.egg
Processing dependencies for dlib==19.24.99
Finished processing dependencies for dlib==19.24.99

But when try to test dlib it gives this error:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\ASDF\anaconda3\envs\dlib\lib\site-packages\dlib-19.24.99-py3.8-win-amd64.egg\dlib\__init__.py", line 19, in <module>
    from _dlib_pybind11 import *
ImportError: DLL load failed while importing _dlib_pybind11: The specified module could not be found.
>>> import dlib
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\ASDF\anaconda3\envs\dlib\lib\site-packages\dlib-19.24.99-py3.8-win-amd64.egg\dlib\__init__.py", line 19, in <module>
    from _dlib_pybind11 import *
ImportError: DLL load failed while importing _dlib_pybind11: The specified module could not be found.
>>> import dlib
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\Users\ASDF\anaconda3\envs\dlib\lib\site-packages\dlib-19.24.99-py3.8-win-amd64.egg\dlib\__init__.py", line 19, in <module>
    from _dlib_pybind11 import *
ImportError: DLL load failed while importing _dlib_pybind11: The specified module could not be found.

@DUDUKorte
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getting this error:

Could not load library libcudnn_cnn_train.so.8. Error: /home/ankit/anaconda3/envs/dlib/bin/../lib/libcudnn_ops_train.so.8: undefined symbol: _Z22cudnnGenericOpTensorNdILi3EE13cudnnStatus_tP12cudnnContext16cudnnGenericOp_t21cudnnNanPropagation_tPKdPKvPK17cudnnTensorStructS8_S8_SB_S8_S8

i got the exactly same error, anyone knows how to fix it???

@bwajster
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Does this work on Ubuntu 18.04? Also, can you share the link to install conda?

@mtalhaubaid
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@bwajster97 I think It should work on ubuntu as well

@lingster
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lingster commented Feb 8, 2024

if you are running on arch linux and you are getting these errors try temporarily downgrading your cc/gcc compilers like this:

export CXX="/usr/bin/g++-12"
export CC="/usr/bin/gcc-12"

or wherever the correct path you have installed the previous versions of the compiler.

@s2485523800
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Thanks a lot.
maybe somebody installed cudnn but dlib said not found, you can use " set CMAKE_PREFIX_PATH=path/to/cudnn"( in conda is /Library) to fix this problem

@s2485523800
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Hi @s2485523800, I am facing this issue, unfortunately, I am not able to set CMAKE_PREFIX_PATH correctly. Could you please help?

maybe you can take a photo about your conda env path,and conda list

@semajbaker
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thanks. Worked for me too

@giampierobartolomei
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giampierobartolomei commented Sep 23, 2024 •
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Hi, i followed this tutorial without doing any other installation and it works. Now I need to install PyQt5 library but when i run conda install pyqt=5 (I tried also with -c nvidia, -c conda-forge) it appears this:

`Channels:

  • defaults
  • conda-forge
  • nvidia
    Platform: linux-64
    Collecting package metadata (repodata.json): done
    Solving environment: failed

InvalidSpec: The package "nvidia/linux-64::cuda-compiler==12.6.0=0" is not available for the specified platform
`
I would avoid using pip, because i prefer install all libraries from conda, so what can i do?

this is the conda list:
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_gnu conda-forge
asttokens 2.4.1 pyhd8ed1ab_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
bzip2 1.0.8 h4bc722e_7 conda-forge
c-ares 1.33.1 heb4867d_0 conda-forge
ca-certificates 2024.8.30 hbcca054_0 conda-forge
cmake 3.30.3 hf9cb763_0 conda-forge
cuda 12.4.1 0 nvidia
cuda-cccl 12.4.127 0 nvidia
cuda-command-line-tools 12.4.1 0 nvidia
cuda-compiler 12.6.0 0 nvidia
cuda-cudart 12.4.127 0 nvidia
cuda-cudart-dev 12.4.127 0 nvidia
cuda-cudart-static 12.4.127 0 nvidia
cuda-cuobjdump 12.4.127 0 nvidia
cuda-cupti 12.4.127 0 nvidia
cuda-cupti-static 12.4.127 0 nvidia
cuda-cuxxfilt 12.4.127 0 nvidia
cuda-demo-suite 12.4.127 0 nvidia
cuda-documentation 12.4.127 0 nvidia
cuda-driver-dev 12.4.127 0 nvidia
cuda-gdb 12.4.127 0 nvidia
cuda-libraries 12.6.0 0 nvidia
cuda-libraries-dev 12.6.0 0 nvidia
cuda-libraries-static 12.4.1 0 nvidia
cuda-nsight 12.4.127 0 nvidia
cuda-nvcc 12.4.131 0 nvidia
cuda-nvdisasm 12.4.127 0 nvidia
cuda-nvml-dev 12.4.127 0 nvidia
cuda-nvprof 12.4.127 0 nvidia
cuda-nvprune 12.4.127 0 nvidia
cuda-nvrtc 12.4.127 0 nvidia
cuda-nvrtc-dev 12.4.127 0 nvidia
cuda-nvrtc-static 12.4.127 0 nvidia
cuda-nvtx 12.4.127 0 nvidia
cuda-nvvp 12.4.127 0 nvidia
cuda-opencl 12.4.127 0 nvidia
cuda-opencl-dev 12.4.127 0 nvidia
cuda-profiler-api 12.4.127 0 nvidia
cuda-sanitizer-api 12.4.127 0 nvidia
cuda-toolkit 12.4.1 0 nvidia
cuda-tools 12.4.1 0 nvidia
cuda-visual-tools 12.6.0 0 nvidia
cudatoolkit 11.1.74 h6bb024c_0 nvidia
cudnn 8.0.4 cuda11.1_0 nvidia
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
dlib 19.24.99 pypi_0 pypi
executing 2.1.0 pyhd8ed1ab_0 conda-forge
gds-tools 1.9.1.3 0 nvidia
ipython 8.12.2 pyh41d4057_0 conda-forge
jedi 0.19.1 pyhd8ed1ab_0 conda-forge
keyutils 1.6.1 h166bdaf_0 conda-forge
krb5 1.21.3 h659f571_0 conda-forge
ld_impl_linux-64 2.42 h02bd663_0 conda-forge
libcublas 12.4.5.8 0 nvidia
libcublas-dev 12.4.5.8 0 nvidia
libcublas-static 12.4.5.8 0 nvidia
libcufft 11.2.1.3 0 nvidia
libcufft-dev 11.2.1.3 0 nvidia
libcufft-static 11.2.1.3 0 nvidia
libcufile 1.9.1.3 0 nvidia
libcufile-dev 1.9.1.3 0 nvidia
libcufile-static 1.9.1.3 0 nvidia
libcurand 10.3.5.147 0 nvidia
libcurand-dev 10.3.5.147 0 nvidia
libcurand-static 10.3.5.147 0 nvidia
libcurl 8.10.1 hbbe4b11_0 conda-forge
libcusolver 11.6.1.9 0 nvidia
libcusolver-dev 11.6.1.9 0 nvidia
libcusolver-static 11.6.1.9 0 nvidia
libcusparse 12.3.1.170 0 nvidia
libcusparse-dev 12.3.1.170 0 nvidia
libcusparse-static 12.3.1.170 0 nvidia
libedit 3.1.20191231 he28a2e2_2 conda-forge
libev 4.33 hd590300_2 conda-forge
libexpat 2.6.3 h5888daf_0 conda-forge
libffi 3.4.2 h7f98852_5 conda-forge
libgcc 14.1.0 h77fa898_1 conda-forge
libgcc-ng 14.1.0 h69a702a_1 conda-forge
libgomp 14.1.0 h77fa898_1 conda-forge
libnghttp2 1.58.0 h47da74e_1 conda-forge
libnpp 12.2.5.30 0 nvidia
libnpp-dev 12.2.5.30 0 nvidia
libnpp-static 12.2.5.30 0 nvidia
libnsl 2.0.1 hd590300_0 conda-forge
libnvfatbin 12.4.127 0 nvidia
libnvfatbin-dev 12.4.127 0 nvidia
libnvjitlink 12.4.127 0 nvidia
libnvjitlink-dev 12.4.127 0 nvidia
libnvjpeg 12.3.1.117 0 nvidia
libnvjpeg-dev 12.3.1.117 0 nvidia
libnvjpeg-static 12.3.1.117 0 nvidia
libsqlite 3.46.1 hadc24fc_0 conda-forge
libssh2 1.11.0 h0841786_0 conda-forge
libstdcxx 14.1.0 hc0a3c3a_1 conda-forge
libstdcxx-ng 14.1.0 h4852527_1 conda-forge
libuuid 2.38.1 h0b41bf4_0 conda-forge
libuv 1.48.0 hd590300_0 conda-forge
libxcrypt 4.4.36 hd590300_1 conda-forge
libzlib 1.3.1 h4ab18f5_1 conda-forge
matplotlib-inline 0.1.7 pyhd8ed1ab_0 conda-forge
ncurses 6.5 he02047a_1 conda-forge
nsight-compute 2024.1.1.4 0 nvidia
openssl 3.3.2 hb9d3cd8_0 conda-forge
parso 0.8.4 pyhd8ed1ab_0 conda-forge
pexpect 4.9.0 pyhd8ed1ab_0 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pip 24.2 pyh8b19718_1 conda-forge
prompt-toolkit 3.0.47 pyha770c72_0 conda-forge
prompt_toolkit 3.0.47 hd8ed1ab_0 conda-forge
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
pure_eval 0.2.3 pyhd8ed1ab_0 conda-forge
pygments 2.18.0 pyhd8ed1ab_0 conda-forge
python 3.8.20 h4a871b0_1_cpython conda-forge
readline 8.2 h8228510_1 conda-forge
rhash 1.4.4 hd590300_0 conda-forge
setuptools 74.1.2 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
stack_data 0.6.2 pyhd8ed1ab_0 conda-forge
tk 8.6.13 noxft_h4845f30_101 conda-forge
traitlets 5.14.3 pyhd8ed1ab_0 conda-forge
typing_extensions 4.12.2 pyha770c72_0 conda-forge
wcwidth 0.2.13 pyhd8ed1ab_0 conda-forge
wheel 0.44.0 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
zstd 1.5.6 ha6fb4c9_0 conda-forge

@semajbaker
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semajbaker commented Sep 23, 2024 via email

@giampierobartolomei
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hi, thank you for the fast response, i will surely try, but i've tried also to install pytorch from conda and encountered the same problem:
$ conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 -c pytorch
Channels:

  • pytorch
  • conda-forge
  • nvidia
  • defaults
    Platform: linux-64
    Collecting package metadata (repodata.json): done
    Solving environment: failed

InvalidSpec: The package "nvidia/linux-64::cuda-compiler==12.6.0=0" is not available for the specified platform

do you think that i can install all from pip? i'm worried of installing from pip because i've a lot of dependencies.

Thank you again

@semajbaker
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semajbaker commented Sep 23, 2024 via email

@giampierobartolomei
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Ok thanks, now I have another question. I successfully installed all my python libraries. now when i try to run a cnn_face_detector (based on dlib) i find this error: dets = self.loader.cnn_face_detector(self.frame, 1)
RuntimeError: Error while calling cudaOccupancyMaxPotentialBlockSize(&num_blocks,&num_threads,K) in file /home/chiara/Desktop/gpu_4070/dlib/dlib/cuda/cuda_utils.h:164. code: 222, reason: the provided PTX was compiled with an unsupported toolchain.
Aborted (core dumped). I found that this error may be due to the cuda compatibilities with my rtx geforce 4070. I checked and it needs cuda >11.8. So I installed dlib with cuda like before and checked and DLIB_USE_CUDA=True.

This on my conda list:
cuda 11.8.0 0 nvidia
cuda-cccl_linux-64 12.6.37 0 nvidia
cuda-command-line-tools 12.6.1 0 nvidia
cuda-compiler 12.6.0 0 nvidia
cuda-cudart 12.6.68 0 nvidia
cuda-cudart-dev 12.6.68 0 nvidia
cuda-cudart-dev_linux-64 12.6.68 0 nvidia
cuda-cudart-static 12.6.68 0 nvidia
cuda-cudart-static_linux-64 12.6.68 0 nvidia
cuda-cudart_linux-64 12.6.68 0 nvidia
cuda-cuobjdump 12.6.68 0 nvidia
cuda-cupti 12.6.68 0 nvidia
cuda-cupti-dev 12.6.68 0 nvidia
cuda-cuxxfilt 12.6.68 0 nvidia
cuda-demo-suite 12.4.127 0 nvidia
cuda-documentation 12.4.127 0 nvidia
cuda-driver-dev 12.6.68 0 nvidia
cuda-driver-dev_linux-64 12.6.68 0 nvidia
cuda-gdb 12.6.68 0 nvidia
cuda-libraries 12.6.1 0 nvidia
cuda-libraries-dev 12.6.1 0 nvidia
cuda-libraries-static 12.6.1 0 nvidia
cuda-nsight 12.6.68 0 nvidia
cuda-nvcc 12.4.131 0 nvidia
cuda-nvdisasm 12.6.68 0 nvidia
cuda-nvml-dev 12.6.68 2 nvidia
cuda-nvprof 12.6.68 0 nvidia
cuda-nvprune 12.6.68 0 nvidia
cuda-nvrtc 12.6.68 0 nvidia
cuda-nvrtc-dev 12.6.68 0 nvidia
cuda-nvrtc-static 12.6.68 0 nvidia
cuda-nvtx 12.6.68 0 nvidia
cuda-nvvp 12.6.68 0 nvidia
cuda-opencl 12.6.68 0 nvidia
cuda-opencl-dev 12.6.68 0 nvidia
cuda-profiler-api 12.6.68 0 nvidia
cuda-runtime 12.6.1 0 nvidia
cuda-sanitizer-api 12.6.68 0 nvidia
cuda-toolkit 12.4.1 0 nvidia
cuda-tools 12.6.1 0 nvidia
cuda-version 12.6 3 nvidia
cuda-visual-tools 12.6.1 0 nvidia
cudnn 8.9.7.29 h092f7fd_3 conda-forge

This my nvidia-smi:
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.01 Driver Version: 535.183.01 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 4070 ... Off | 00000000:01:00.0 On | N/A |
| N/A 38C P8 4W / 55W | 17MiB / 8188MiB | 0% Default |

So even if i installed dlib with the right CUDA VERSION, i cannot use the model with the gpu.
Do you have any suggestion?

@semajbaker
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semajbaker commented Sep 23, 2024 via email

@giampierobartolomei
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yes, i installed it locally following this guide: https://medium.com/@deeplch/the-simple-guide-deep-learning-with-rtx-4090-installation-cuda-cudnn-tensorflow-pytorch-3626266a65e4

@semajbaker
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semajbaker commented Sep 24, 2024 via email

@giampierobartolomei
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Thank you for your help, now it works! I just solved by installing latest NVIDIA driver after instaling cuda and cudnn on my local machine.

@semajbaker
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semajbaker commented Sep 24, 2024 via email

@YBachmann
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Nice guide! In my case I had to set export CUDAHOSTCXX=/usr/bin/g++ because otherwise I got the following error:

-- Building a CUDA test project to see if your compiler is compatible with CUDA...
-- *****************************************************************************************************************
-- *** CUDA was found but your compiler failed to compile a simple CUDA program so dlib isn't going to use CUDA. 
-- *** The output of the failed CUDA test compile is shown below: 
-- *** 
-- ***   Change Dir: /home/yannic/dlib/build/temp.linux-x86_64-3.10/dlib_build/cuda_test_build
   ***   
   ***   Run Build Command(s):/usr/bin/gmake -f Makefile && [ 50%] Building NVCC (Device) object CMakeFiles/cuda_test.dir/cuda_test_generated_cuda_test.cu.o
   ***   nvcc warning : Support for offline compilation for architectures prior to '<compute/sm/lto>_75' will be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
   ***   cc: fatal error: cannot execute ‘cc1plus’: execvp: No such file or directory
   ***   compilation terminated.
   ***   nvcc fatal   : Failed to preprocess host compiler properties.
   ***   CMake Error at cuda_test_generated_cuda_test.cu.o.cmake:216 (message):
   ***     Error generating
   ***     /home/yannic/dlib/build/temp.linux-x86_64-3.10/dlib_build/cuda_test_build/CMakeFiles/cuda_test.dir//./cuda_test_generated_cuda_test.cu.o
   ***   
   ***   
   ***   gmake[2]: *** [CMakeFiles/cuda_test.dir/build.make:77: CMakeFiles/cuda_test.dir/cuda_test_generated_cuda_test.cu.o] Error 1
   ***   gmake[1]: *** [CMakeFiles/Makefile2:82: CMakeFiles/cuda_test.dir/all] Error 2
   ***   gmake: *** [Makefile:91: all] Error 2
   ***   
   ***   
-- *****************************************************************************************************************
-- Disabling CUDA support for dlib.  DLIB WILL NOT USE CUDA

@MichaelTarasov
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The BEST manual!
It's works!
Thank You very much!
Win 11.

@sickybee
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i've added some tweaks in my build
-install cuda 12.8
-install vs_BuildTools ->install Clang
-install intel onemkl
-install OpenBLAS

  • cmake .. -DDLIB_USE_MKL_WITH_TBB=1 -DDLIB_USE_BLAS=1 -DDLIB_USE_CUDA=1 -DUSE_AVX_INSTRUCTIONS=1 -DUSE_SS2_INSTRUCTIONS=1 -DUSE_SS4_INSTRUCTIONS=1 -DCUDAToolkit_ROOT=E:\Source_Codes\WhoIsShe.conda\Library\bin\

  • activate conda environment

  • pip uninstall dlib #uninstall previous dlib

  • python setup.py install --set DLIB_USE_CUDA=1

DLIB CUDA is available
DLIB BLAS is available
DLIB LAPACK is available
DLIB AVX is available

... and now face_recognition webcam face blur works flawlessly in my GTX 970

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