[Solved] Nvrtc: error: invalid value for --gpu-architecture

Troubleshooting
1

Dear all, I am a new to study tvm. I flow the tutorial: “Get Started with Tensor Expression” and got the error message:

(base) ryanyao@umihuang-HP-EliteDesk-800-G1-TWR:~/docker_mount/tvm_ex$ python tensor_expr_get_started.py <class ‘tvm.tensor.Tensor’> terminate called after throwing an instance of ‘dmlc::Error’ what(): [17:27:23] /home/ryanyao/tvm/src/codegen/opt/build_cuda_on.cc:118: Check failed: compile_res == NVRTC_SUCCESS (5 vs. 0) : nvrtc: error: invalid value for --gpu-architecture (-arch)

Aborted (core dumped)

Can someone give me hint how to solve it? Thank you.

2

I have solved it. I think there were something wrong during I build the tvm. So I rebuild it and the problem is gone.

3

Hi,

I’m getting the same error wilr taying to run tensor_expr_get_started.py

Traceback (most recent call last):

  File "tensor_expr_get_started.py", line 139, in <module>
    fadd = tvm.build(s, [A, B, C], tgt, target_host=tgt_host, name="myadd")

  File "/home/drorca/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/build_module.py", line 621, in build
    fhost, mdev = _build_for_device(flist, tar, target_host)

  File "/home/drorca/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/build_module.py", line 488, in _build_for_device
    mdev = codegen.build_module(fdevice, str(target)) if fdevice else None

  File "/home/drorca/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/codegen.py", line 36, in build_module
    return _Build(lowered_func, target)

  File "/home/drorca/.local/lib/python3.5/site-packages/tvm-0.6.dev0-py3.5-linux-x86_64.egg/tvm/_ffi/_ctypes/function.py", line 210, in __call__
    raise get_last_ffi_error()

tvm._ffi.base.TVMError: Traceback (most recent call last):
  File "/home/drorca/dror/tvm/tvm/src/codegen/opt/build_cuda_on.cc", line 119
nvrtc: Check failed: compile_res == NVRTC_SUCCESS (5 vs. 0) : error: invalid value for --gpu-architecture (-arch)
4

I encountered a similar error as the above. Do you rememeber how to solve this issue?

5

Remove tvm and get new tvm source to build it. Because my root cause is that there are some problme (assing target) during building the tvm previously.

6

I just tried downloading new source and rebuilding it, however, it didn’t work for my case:

nvcc fatal   : Value 'sm_75' is not defined for option 'gpu-architecture'

Thanks anyway.

7

This error is coming from nvcc. Do you have multiple versions of nvcc on your machine? Can you make sure that the correct one is first on the path?

9

The nvcc -V reports a version as 9.0.176, but the nvidia-smi reports a version 10.1. My tensorflow version is 1.12.0. The directory of /usr/local/ includes the following files:

bin  cuda  cuda-9.0  etc  games  include  lib  man  sbin  share  src

The path shows

/home/max/miniconda3/bin:/home/max/miniconda3/condabin:/usr/local/cuda-9.0/bin:/usr/local/cuda-9.0/bin:/usr/local/cuda-9.0/bin:/home/max/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/local/cuda-10.1/bin:/snap/bin:/home/max/miniconda3/bin:/home/max/miniconda3/bin:/home/max/miniconda3/bin:/usr/local/cuda-9.0/bin:/home/max/miniconda3/bin:/usr/local/cuda-9.0/bin:/home/max/miniconda3/bin:/usr/local/cuda-9.0/bin:/usr/local/cuda-10.1/bin:/home/max/miniconda3/bin/:/usr/local/cuda-9.1/bin:/home/max/miniconda3/bin/

Is the 10.1 the source of the issue?

10

It looks like it. CUDA 10.1 works fine for me. I would try installing CUDA 10.1 from scratch.

12

Thanks for your reply.

My GPU is GTX 1660 Ti which is not supported by nvidia-384, nvidia-driver-390 that uses cuda 9.x as their runtime. I indeed tried installing the following drivers nvidia-driver-418, nvidia-driver-430, nvidia-driver-435 which all uses cuda 10.x, but the same issue emerged.

13

Finally, finally, I solved this issue. Although I have no idea why this issue happened.

I use nvidia-driver-430 as the driver, and install cuda-10.0 and cudnn-7.6.4 for 10.0, changed the tensorflow version from 1.12.0 to 1.13.1.

Then the error was gone.