Darknet: failing to compile with GPU=1

[zacbayhan]

Started playing with your project after catching the ted talk, very cool project. I've gotten it to compile with GPU=0, however When setting GPU=1 I get the following error.

/usr/include/string.h: In function ‘void* __mempcpy_inline(void*, const void*, size_t)’:
/usr/include/string.h:652:42: error: ‘memcpy’ was not declared in this scope
   return (char *) memcpy (__dest, __src, __n) + __n;
                                          ^
compilation terminated due to -Wfatal-errors.
Makefile:88: recipe for target 'obj/convolutional_kernels.o' failed
make: *** [obj/convolutional_kernels.o] Error 1

I'll keep tinkering and see if I can't figure it out just thought it might be worth reporting.

[25b3nk]

Firstly check if the CUDA path is correct in the Mkefile.

ifeq ($(GPU), 1) 
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif

In the above part, /usr/local/cuda/ is your CUDA path. New CUDA-8.0 will be having path /usr/local/cuda-8.0/. Check that once.

After that too you get the error message,
execute this line export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}} , before make.

[zacbayhan]

Thanks, I'll have a look Monday morning. I kind of assumed it was an error
on my part

On Sep 15, 2017 02:19, "Bhaskar C" [e-mail] wrote:

Firstly check if the CUDA path is correct in the Mkefile.

ifeq ($(GPU), 1)
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif

In the above part, /usr/local/cuda/ is your CUDA path. New CUDA-8.0
will be having path /usr/local/cuda-8.0/. Check that once.

After that too you get the error message,
execute this line export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
, before make.

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[deepakcrk]

I was getting same errors, when I installed proper nvidia drivers I was able to fix this

[loretoparisi]

@zacbayhan so did you solve this? You can close it then :)

[zacbayhan]

Sorry I got distracted, I'll look into it tomorrow and check all the
path's. I'll close it and reopen it if I run into any issues. Thanks for
the reminder.

On Mon, Sep 25, 2017 at 11:35 AM, Loreto Parisi [e-mail]
wrote:

@zacbayhan https://github.com/zacbayhan so did you solve this? You can
close it then :)

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[jazoom]

I'm trying to install YOLO on Google's Colaboratory and I can't get it to compile with CUDA. I'm not sure what path to add because there isn't a /cuda directory anywhere (checked using find).

Pytorch seems to just work.

How do I get YOLO to work?

[zacbayhan]

Can you do a nvidia-smi (linux) and see info on your gpu?

On Wed, Apr 11, 2018, 18:37 jazoom [e-mail] wrote:

I'm trying to install YOLO on Google's Colaboratory and I can't get it to
compile with CUDA. I'm not sure what path to add because there isn't a
/cuda directory anywhere (checked using find).

Pytorch seems to just work.

How do I get YOLO to work?

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[jazoom]

Thanks for the reply. Is this what you were asking for?
https://colab.research.google.com/drive/1V65EmJc7h5cf1A_fJsdZ__-B9pW6o3fh

Edit: I figured seeing as I shared this notebook with you I may as well show you the exact implementation for trying to get YOLO working. Feel free to mess around with the commands and run them yourself. This is just a playground to demonstrate this issue.

[zacbayhan]

Driving at the moment so can't take a good look but it looks like it's not
finding the needed libraries. Or they are in a different location. I'll
take a better look in a few hours, if you know they are installed might try
creating a symbolic link to usr/local/cuda

On Wed, Apr 11, 2018, 18:59 jazoom [e-mail] wrote:

Thanks for the reply. Is this what you were asking for?
https://colab.research.google.com/drive/1V65EmJc7h5cf1A_fJsdZ__-B9pW6o3fh

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[jazoom]

I'm not sure what path I would need to create a symbolic link to.

There's no rush. Though, it'll be good to get this sorted out because I'm sure more and more people are going to want to run YOLO on Colaboratory in future.

[youngxiao]

@csbenk Thanks a lot. It really works.

[danishnazir]

hi @jazoom it would be great if you can share the notebook which contains the installation instructions for installing darknet on colab

[jazoom]

@danishnazir I never got it working and moved on to other things. I just dug up the relevant code from the notebook. I'll paste it below for anyone who wants it.

# install darknet with YOLOv3
!git clone https://github.com/pjreddie/darknet

PATH_TO_CUDA = '/usr/lib/x86_64-linux-gnu/'

STRINGS_TO_REPLACE = [
    ('GPU=0', 'GPU=1'), #change the first line of the Makefile to say "GPU=1" instead of "GPU=0" so it will be compiled to use CUDA
    ('/usr/local/cuda/', PATH_TO_CUDA)
]

# edit the model config file to match our custom requirements
for line in fileinput.input('./darknet/Makefile', inplace=True):
    replaced = False
    for search, replace in STRINGS_TO_REPLACE:
        if search in line:
            print(line.rstrip().replace(search, replace))
            replaced = True
            break
    if not replaced:
        print(line.rstrip())
fileinput.close()




# #change the first line of the Makefile to say "GPU=1" instead of "GPU=0" so it will be compiled to use CUDA
# FIRST_LINE = 'GPU=1'
# with open('./darknet/Makefile') as f:
#     lines = f.readlines()
# lines[0] = FIRST_LINE + '\n'
# with open('./darknet/Makefile', 'w') as f:
#     f.writelines(lines)



!cd darknet && make
!echo DONE

[danishnazir]

@jazoom i got it working thanks for your help though :)

[jazoom]

@danishnazir perhaps you can share how you got it working?

[danishnazir]

sure
1) follow the instruction here to install CUDA
https://colab.research.google.com/drive/14OyDrmxzBmkJ8H51iodPE2aXHzCduKJP#scrollTo=bOHa-Sj8ywxn
2) then simply change the makefile of the darknet and set GPU=1 and compile it :)

[jazoom]

Oh wow. Install CUDA? I thought CUDA was already installed on Colaboratory. I guess this is to install a second one that isn't tired to TF?

Thanks for sharing.

[danishnazir]

no cuda is not installed by default you have to install it in order to compile darknet
welcome :)

[jazoom]

That explains it. But it already is installed somewhere since TF has access to it.

[cadip92]

Firstly check if the CUDA path is correct in the Mkefile.

ifeq ($(GPU), 1) 
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif

In the above part, /usr/local/cuda/ is your CUDA path. New CUDA-8.0 will be having path /usr/local/cuda-8.0/. Check that once.

After that too you get the error message,
execute this line export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}} , before make.

Hello!
I have installed Cuda10.0 and cudnn on Ubuntu 18.04 and also updated my PATH and LD_LIBRARY_PATH as per the path specified in the Installation Guide. However, as i run the Makefile to compile darknet on GPU; it gives me the below error. I am unable to understand where the exact issue is.

/usr/bin/x86_64-linux-gnu-ld: -lcuda kann nicht gefunden werden(cannot be found)
collect2: error: ld returned 1 exit status
Makefile:83: recipe for target 'libdarknet.so' failed
make: * [libdarknet.so] Error 1

Have i done something wrong with the installation?

[Khumayun]

I have exactly the same problem as @cadip92. Please suggest at least something. I really got stuck with it.

[joshinakul]

i have successfully compiled it with cuda-10
things to remember:
1) after cloning darknet, make it with root (sudo)
2) set NVCC = /usr/local/cuda/bin/nvcc

[ThegreatShible]

I had an issue in Google Colab with DarkNet makefile when enabling GPU. I solved it by

1. Executing everything as root : git clone, make .... (I don't think this point is relevant but u nerve know)

2. Using this ARCH variable : ARCH= -gencode arch=compute_37,code=sm_37

3. specifying nvcc path in makefile : NVCC=/usr/local/cuda/bin/nvcc

hope it helps

[forzagreen]

$ ln -s /usr/local/cuda /usr/local/cuda-8.0/

This is because the Makefile is referencing /usr/local/cuda:

ifeq ($(GPU), 1)
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif

[barresoft]

My solution using cuda 10.1:

on MakeFile replace for this:

ifeq ($(GPU), 1)
    COMMON+= -DGPU -I/usr/local/cuda/include/
    CFLAGS+= -DGPU
    #ifeq ($(OS),Darwin) #MAC
    #LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand
    #else
    LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
    #endif
endif

execute this before make:
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}

[andeyeluguo]

cuda9.0 may work, you may change the /usr/local/cuda to /usr/local/cuda-9.0

[dippa-1]

i have successfully compiled it with cuda-10
things to remember:

1. after cloning darknet, make it with root (sudo)

2. set NVCC = /usr/local/cuda/bin/nvcc

this worked for me, thanks

[azzageee]

My solution using cuda 10.1:

on MakeFile replace for this:

ifeq ($(GPU), 1)
  COMMON+= -DGPU -I/usr/local/cuda/include/
  CFLAGS+= -DGPU
  #ifeq ($(OS),Darwin) #MAC
  #LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand
  #else
  LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
  #endif
endif

execute this before make:
export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}

This worked for Cuda 10.2. Thanks heaps @barresoft !

[martianvenusian]

On Ubuntu 20.04, CUDA 10.1, cudnn 7.6.5, Tensorflow 2.3.0
First check where is your cuda

$whereis cuda
>cuda: /usr/lib/cuda /usr/include/cuda.h

In my case cuda was here: /usr/lib/cuda/.

So i have changed CUDA in the Mkefile like following

from

ifeq ($(GPU), 1) 
COMMON+= -DGPU -I/usr/local/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif

to

ifeq ($(GPU), 1) 
COMMON+= -DGPU -I/usr/lib/cuda/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/lib/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
endif