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python – GPU Sync Failed While using tensorflow – Stack Overflow
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Internalerror: GPU sync failed error (How to Solve) | ProgrammerAH
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Questions about GPU Sync failed – Frameworks – NVIDIA Developer Forums
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- Summary of article content: Articles about Questions about GPU Sync failed – Frameworks – NVIDIA Developer Forums If i get the “GPU Sync failed” error message, that means i’m using my GPU for the training process, right? because it was going too slow and i … …
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So i was training my first CNN with tensorflow but at the 7th epoch i get this error message that stated “GPU sync failed”. I was looking for an answer and i found that it may be because my GPU ran out of m… - Table of Contents:
internalerror: gpu sync failed, tensorflow allow growth,
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gpu sync failed
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Internalerror: GPU sync failed error (solution)
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1 Report errors problem ：（ come from 《Python Deep learning 》P178-179）
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gpu sync failed
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GPU Sync Failed While using tensorflow
TLDR: If you find that tensorflow is throwing a GPU sync failed Error , it may be because the model’s inputs are too large (as was my case when first running into this problem) or you don’t have cuDNN installed properly. Verify that cuDNN is installed correctly and reset your nvidia caches (ie. sudo -rf $HOME/.nv/ ) (if you have no yet done so after initially installing CUDA and cuDNN) and restart your machine.
Running an example found in the tensorflow (TF) docs (https://www.tensorflow.org/tutorials/keras/save_and_restore_models#checkpoint_callback_usage), was getting the error
“GPU sync failed Error”
when running a tf.keras model (with a large input (vectorized MNIST feature data (length=28^2))). Looking into this problem, found this post here (https://github.com/tensorflow/tensorflow/issues/5688) (which talks about the problem being caused specifically by large inputs to a model) and (following the chain of supposed effect) here (https://github.com/tensorflow/tensorflow/issues/5688). The last line of the 2nd post question showing error message snippet
F tensorflow/stream_executor/cuda/cuda_dnn.cc:2440] failed to enqueue convolution on stream: CUDNN_STATUS_NOT_SUPPORTED
From this, I decided to try and test if (as required by TF) cuDNN was actually installed correctly (https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-deb). Following the docs to try to verify the cuDNN install (https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#verify),
#Copy the cuDNN sample to a writable path. $cp -r /usr/src/cudnn_samples_v7/ $HOME #Go to the writable path. $ cd $HOME/cudnn_samples_v7/mnistCUDNN #Compile the mnistCUDNN sample. $make clean && make #Run the mnistCUDNN sample. $ ./mnistCUDNN #If cuDNN is properly installed and running on your Linux system, you will see a message similar to the following: Test passed!
found that was throwing error
cudnnGetVersion() : 6021 , CUDNN_VERSION from cudnn.h : 6021 (6.0.21) Host compiler version : GCC 5.4.0 There are 1 CUDA capable devices on your machine : device 0 : sms 20 Capabilities 6.1, SmClock 1797.0 Mhz, MemSize (Mb) 8107, MemClock 5005.0 Mhz, Ecc=0, boardGroupID=0 Using device 0 Testing single precision CUDNN failure Error: CUDNN_STATUS_INTERNAL_ERROR mnistCUDNN.cpp:394 Aborting…
Looking into this more, found nvidiadev threads here (https://devtalk.nvidia.com/default/topic/1025900/cudnn/cudnn-fails-with-cudnn_status_internal_error-on-mnist-sample-execution/post/5259556/#5259556) and here (https://devtalk.nvidia.com/default/topic/1024761/cuda-setup-and-installation/cudnn_status_internal_error-when-using-cudnn7-0-with-cuda-8-0/post/5217666/#5217666), which recommend clearing the nvidia caches via
sudo rm -rf ~/.nv/
and restarting (else both installation verification tests for CUDA and cuDNN will fail) my machine. After doing this, both CUDA (https://docs.nvidia.com/cuda/archive/9.0/cuda-installation-guide-linux/index.html#install-samples) and cuDNN (https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-deb) installation checks passed.
And was finally able to successfully run the TF model without error.
model.fit(train_images, train_labels, epochs = 10, validation_data = (test_images, test_labels), callbacks = [cp_callback]) # pass callback to training
Train on 1000 samples, validate on 1000 samples Epoch 1/10 1000/1000 [==============================] – 1s 604us/step – loss: 1.1795 – acc: 0.6720 – val_loss: 0.7519 – val_acc: 0.7580 Epoch 00001: saving model to training_1/cp.ckpt WARNING:tensorflow:This model was compiled with a Keras optimizer () but is being saved in TensorFlow format with save_weights . The model’s weights will be saved, but unlike with TensorFlow optimizers in the TensorFlow format the optimizer’s state will not be saved. …..
Hope this helps you.
Internalerror: GPU sync failed error (How to Solve)
1. Error reporting: (from Python deep learning p178-179)
When vscode runs the following code in Jupiter notebook, an error is reported: internalerror: GPU sync failed
from tensorflow.keras.models import Sequential from tensorflow.keras import layers from tensorflow.keras.optimizers import RMSprop model = Sequential() model.add(layers.Flatten(input_shape=(lookback // step, float_data.shape[-1]))) model.add(layers.Dense(32, activation=’relu’)) model.add(layers.Dense(1)) model.compile(optimizer=RMSprop(), loss=’mae’) history = model.fit_generator(train_gen, steps_per_epoch=500, epochs=20, validation_data=val_gen, validation_steps=val_steps)
2. Solution:
(1) Don’t open too many ipynb file windows. There is only one running window left. Restart and there should be no problem.
(2) Some friends said that they might have something to do with the wallpaper engine. Just turn it off. I haven’t verified this yet.
However, I found that when the wallpaper engine dynamic desktop is displayed, the GPU utilization will increase sharply:
Questions about GPU Sync failed
Hi everyone!
So i was training my first CNN with tensorflow but at the 7th epoch i get this error message that stated “GPU sync failed”. I was looking for an answer and i found that it may be because my GPU ran out of memory (i’ve got a RTX 2060). And it’s very possible because i’ve left my PC alone, training my CNN, and i found my brother was playing a game, so that may have caused the lack of memory.
Beyond all of that, i have a few questions that surged after this episode:
Did the error message appear because i ran out of memory or could it be caused for other reason? If i get the “GPU Sync failed” error message, that means i’m using my GPU for the training process, right? because it was going too slow and i was thinking that it was running on the CPU. If that error message does not mean i’m using my GPU, how can i check that? What can i do to train my CNN faster?
That would be all, thanks!
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