Tensorflow crash with CUDNN_STATUS_ALLOC_FAILED

Question

Been searching the web for hours with no results, so figured I'd ask here.

I'm trying to make a self driving car following Sentdex's tutorial, but when running the model, I get a bunch of fatal errors. I've searched all over the internet for the solution, and many seem to have the same problem. However, none of the solutions I've found (Including this Stack-post), work for me.

Here is my software:

• Tensorflow: 1.5, GPU version
• CUDA: 9.0, with the patch
• CUDnn: 7
• Windows 10 Pro
• Python 3.6

Hardware:

• Nvidia 1070ti, with latest drivers
• Intel i5 7600K

Here is the crash log:

2018-02-04 16:29:33.606903: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED 2018-02-04 16:29:33.608872: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED 2018-02-04 16:29:33.609308: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_blas.cc:444] failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED 2018-02-04 16:29:35.145249: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:385] could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED 2018-02-04 16:29:35.145563: E C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\stream_executor\cuda\cuda_dnn.cc:352] could not destroy cudnn handle: CUDNN_STATUS_BAD_PARAM 2018-02-04 16:29:35.149896: F C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\kernels\conv_ops.cc:717] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo<T>(), &algorithms)

Here's my code:

 import tensorflow as tf
    import numpy as np
    import cv2
    import time
    from PIL import ImageGrab
    from getkeys import key_check
    from alexnet import alexnet
    import os
    from sendKeys import PressKey, ReleaseKey, W,A,S,D,Sp

    import random

    WIDTH = 80
    HEIGHT = 60
    LR = 1e-3
    EPOCHS = 10
    MODEL_NAME = 'DiRT-AI-Driver-{}-{}-{}-epochs.model'.format(LR, 'alexnetv2', EPOCHS)

    def straight():
        PressKey(W)
        ReleaseKey(A)
        ReleaseKey(S)
        ReleaseKey(D)
        ReleaseKey(Sp)
    def left():
        PressKey(A)
        ReleaseKey(W)
        ReleaseKey(S)
        ReleaseKey(D)
        ReleaseKey(Sp)
    def right():
        PressKey(D)
        ReleaseKey(A)
        ReleaseKey(S)
        ReleaseKey(W)
        ReleaseKey(Sp)
    def brake():
        PressKey(S)
        ReleaseKey(A)
        ReleaseKey(W)
        ReleaseKey(D)
        ReleaseKey(Sp)
    def handbrake():
        PressKey(Sp)
        ReleaseKey(A)
        ReleaseKey(S)
        ReleaseKey(D)
        ReleaseKey(W)

    model = alexnet(WIDTH, HEIGHT, LR)
    model.load(MODEL_NAME)


    def main():
        last_time = time.time()
        for i in list(range(4))[::-1]:
            print(i+1)
            time.sleep(1)


    paused = False
    while(True):
            if not paused:
                screen = np.array(ImageGrab.grab(bbox=(0,40,1024,768)))
                screen = cv2.cvtColor(screen,cv2.COLOR_BGR2GRAY)
                screen = cv2.resize(screen,(80,60))
                print('Loop took {} seconds'.format(time.time()-last_time))
                last_time = time.time()
                print('took time')
                prediction = model.predict([screen.reshape(WIDTH,HEIGHT,1)])[0]
                print('predicted')
                moves = list(np.around(prediction))
                print('got moves')
                print(moves,prediction)

                if moves == [1,0,0,0,0]:
                    straight()
                elif moves == [0,1,0,0,0]:
                    left()
                elif moves == [0,0,1,0,0]:
                    brake()
                elif moves == [0,0,0,1,0]:
                    right()
                elif moves == [0,0,0,0,1]:
                    handbrake()

            keys = key_check()

            if 'T' in keys:
                if paused:
                    pased = False
                    time.sleep(1)
                else:
                    paused = True
                    ReleaseKey(W)
                    ReleaseKey(A)
                    ReleaseKey(S)
                    ReleaseKey(D)
                    ReleaseKey(Sp)
                    time.sleep(1)


main()

I've found that the line that crashes python and spawns the first three bugs is this line:

• prediction = model.predict([screen.reshape(WIDTH,HEIGHT,1)])[0]

When running the code, the CPU goes up to a whopping 100%, suggesting that something is seriously off. GPU goes to about 40-50%

I've tried Tensorflow 1.2 and 1.3, as well as CUDA 8, to no good. When installing CUDA I do not install the specific drivers, since they are too old for my GPU. Tried different CUDnn's too, did no good.

Answer 1

In my case, the issue happened because another python console with tensorflow imported was running. Closing it solved the problem.

I have Windows 10, the main errors were :

failed to create cublas handle: CUBLAS_STATUS_ALLOC_FAILED

Could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED

Answer 2

Probably you're running out of GPU memory.

---

If you're using TensorFlow 1.x:

1st option) set allow_growth to true.

import tensorflow as tf    
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)

2nd option) set memory fraction.

# change the memory fraction as you want

import tensorflow as tf
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

---

If you're using TensorFlow 2.x:

1st option) set set_memory_growth to true.

# Currently the ‘memory growth’ option should be the same for all GPUs.
# You should set the ‘memory growth’ option before initializing GPUs.

import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
  except RuntimeError as e:
    print(e)

2nd option) set memory_limit as you want. Just change the index of gpus and memory_limit in this code below.

import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])
  except RuntimeError as e:
    print(e)