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failed to run cuBLAS routine cublasSgemm_v2: CUBLAS_STATUS_EXECUTION_FAILED #332
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Hey bro, have you figured it out? I met the same issue. |
i also met the same issue when i run the yolo-v3,did you solve this problem? |
I met the same error! My GPU is RTX2080 8G * 2,tensorflow-gpu:1.12,keras2.2.4, Ubuntu18.04. Can somebody solve it? |
Try the following statement at the beginning of the code.
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Hi, I still got some errors: |
hello i met the same error, my env is cuda9.0 cudnn 7.4 tensorflow-gpu1.12.0,my gpu is RTX 2080, this is my work computer, but my own computer has same env only gpu is 940 can run same project well,how can i do with this error,someone can help me? |
I think that it is a bug of RTX
2080 and I have not figured it out. If you get some progress about this issue, get in touch with me please. Thanks a lot
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hello i met the same error, my env is cuda9.0 cudnn 7.4 tensorflow-gpu1.12.0,my gpu is RTX 2080, this is my work computer, but my own computer has same env only gpu is 940 can run same project well,how can i do with this error,someone can help me?
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i also met same error, my gpu is RTX 2080ti, tensorflow-gpu 1.8.0, cuda 9.0, but in the GTX 1080ti, tensorflow-gpu 1.4.0, cuda 8.0, the program can run normally. Can someone give some advice? thanks |
I have solved this problem: |
Hello! Did you solve it?How? |
I fixed this issue just by installing the CUDA Toolkit patch. |
I have installed the CUDA Toolkit patch but still having this problem |
I have same issue, same code running on K80 but not RTX2080 |
same issue on my Titan RTX. |
It works after I update the tensorflow version from My cuda version is |
It works after I update the tensorflow version from 1.13.1 to 1.14. My cuda version is 10.0, cudnn version is 7.6.3, the gpu is RTX2080 After I made a change follow the above I still got the problem like the following: E tensorflow/stream_executor/cuda/cuda_dnn.cc:329] Could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR |
@xiaohai-AI try this
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I was also getting the same error in tensorflow -gpu 1.6.0 cuda 9.0. Upgrading to cuda 10.0 and tensorflow -gpu 1.14.0 . Solved the issue for me. Thanks @xiaohai-AI. Not sure why you are getting internal errot hough. Probably because you have two cuda versions or maybe because tensorflow is picking up wrong version of cudnn |
But my tensorflow is 1.15, cuda is 10.0, gpu is RTX 3080, still have the same issue. |
hey @mfshiu maybe you can try cuda 10.0 with tensorflow-gpu 1.14 |
hi @mfshiu, NVIDIA maintains its own version of tensorflow 1.15 here: https://github.com/NVIDIA/tensorflow#install , which support latest gpu card. So, you need to remove official tensorflow which installed through pip or conda, and install nvidia's version, as its README.md says: install the NVIDIA wheel index:
install the current NVIDIA Tensorflow release:
after installed, just use it as regular tensorflow:
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Hey @allenyllee I wonder if you might be able to clarify or help: When I follow those install instructions for the NVIDIA-tensorflow, I get a long error that tells me...to re-do what I just did?
Re-running those "This package can be installed as:" commands just results in the same error message again. |
Resolved this issue for myself: Be sure you're running Python 3.8 and Pip 20 or later. |
I had the same problem with an RTX 3090 + TF 1.15. I resolved my problem by using the official nvidia+tf1 ngc docker container, available here: https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow |
It works very well to me, in my case with RTX 3090 +TF 1.15, nvidia+tf1 ngc docker container version '21.05-tf1-py3' works very well! Thanks alot. |
me too!!!!!!. have you solved this problem? |
please find a version that matches your GPU version in nvidia-docker hub |
i found the same question on a10 GPU, that 30-, a10, a100, etc. which compute capacity is more than 8.0 must use CUDA11.x, so you could't use tensorflow1.x which match CUDA10 or lower. |
Problem fixed after installed |
It works for me!!! Thanks a lot~ The tf version of NVIDA is 1.15, but luckily my codes can run successfully on tf==1.15~ |
Thanks! It works for me~ |
Cool!! It fixes perfectly my issue! Thanks! |
Yes! Yes!!!
I used A6000, tf1.15, cuda10.0.130, cudnn7.3.1, and TF website let me use python 3.6 or 3.7, that's what I did before. |
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Thanks! Very Thanks! It has solved my problems. By the way, my device is A6000 and 4090 all have this problem, and now solved it , my tensorflow is 1.12.0. cuda is 9.0 |
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When I train voc data, the error happened. My GPU is RTX2080 8G * 2,tensorflow-gpu:1.12,keras2.2.4
Epoch 1/50 2019-01-28 00:16:00.441512: E tensorflow/stream_executor/cuda/cuda_blas.cc:652] failed to run cuBLAS routine cublasSgemm_v2: CUBLAS_STATUS_EXECUTION_FAILED Traceback (most recent call last): File "train.py", line 192, in <module> _main(annotation_path=anno) File "train.py", line 65, in _main callbacks=[logging, checkpoint]) File "/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper return func(*args, **kwargs) File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1418, in fit_generator initial_epoch=initial_epoch) File "/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py", line 217, in fit_generator class_weight=class_weight) File "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py", line 1217, in train_on_batch outputs = self.train_function(ins) File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2715, in __call__ return self._call(inputs) File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2675, in _call fetched = self._callable_fn(*array_vals) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py", line 1439, in __call__ run_metadata_ptr) File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/errors_impl.py", line 528, in __exit__ c_api.TF_GetCode(self.status.status)) tensorflow.python.framework.errors_impl.InternalError: Blas SGEMM launch failed : m=346112, n=32, k=64 [[{{node conv2d_3/convolution}} = Conv2D[T=DT_FLOAT, _class=["loc:@batch_normalization_3/cond/FusedBatchNorm/Switch"], data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](leaky_re_lu_2/LeakyRelu, conv2d_3/kernel/read)]] [[{{node yolo_loss/while_1/LoopCond/_2963}} = _HostRecv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_6607_yolo_loss/while_1/LoopCond", tensor_type=DT_BOOL, _device="/job:localhost/replica:0/task:0/device:CPU:0"](^_cloopyolo_loss/while_1/strided_slice_1/stack_2/_2805)]]
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